2022–2023
STUDENT HANDBOOK
THE DEPARTMENT OF
BIOSTATISTICS
Message from the Chair
W
Welcome to the Department of Biostatistics at Columbia
University’s Mailman School of Public Health! There has
never been a more exciting time to study biostatistics. The
advent of data science, the increasing availability of big
data, and stark reminders of public health's importance,
such as the COVID-19 pandemic that has engulfed our lives,
and the heightened awareness to advance equity and social
justice have made the need for rigorous analytic tools, and
their proper use to address scientific hypotheses extremely
important. By studying biostatistics, you will be able to
draw sound inferences by separating the signal from the
noise and affect proper interpretations that would advance
public health for all.
By joining our highly reputable graduate programs, you
will be trained in the rigorous theory that underlies
statistical principles and the latest computational tools and
machine learning techniques to prepare you for the age of
data science. You will get ample opportunity to immerse in
biomedical problems from complex networks that would
inspire methodologic advances and allow for
comprehensive data analysis with proper attention to
validity of assumptions. Members of our faculty are at the
forefront of development of cutting-edge techniques to
address important public health challenges, making
significant impact in numerous areas including multi-omics
analysis, personalized medicine, mobile health, analysis of
electronic medical records, and clinical trials.
Your experience will be enriched by interactions
with fellow students from diverse backgrounds,
providing excellent training in theory and methods, as
well as skills necessary for professional practice. As
our alumni would attest, employers recognize the
high quality of our training programs, and our
graduates receive premier jobs in academia, private
sector, and government.
We fully realize that you are joining us at a time of
great uncertainty due to the COVID-19 pandemic. I
would like to assure you that the Department of
Biostatistics is fully committed to being available and
accessible to ensure the full Columbia experience by
providing all necessary help and guidance. In this
handbook, you will find readily accessible information
on faculty research interests, descriptions of our
state-of-the-art course offerings and coursework, and
milestones for our programs.
Welcome again as you join our talented faculty,
staff, students, and alumni. I look forward to following
your academic and professional growth with great
interest. I am always happy and ready to hear from
you and to help make your stay with us as fulfilling as
p
ossible.
Kiros Berhane, PhD
Cynthia and Robert Citron-Roslyn and Leslie
Goldstein Professor and Chair
Department of Biostatistics
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
The Department of Biostatistics
Biostatistics is the science of developing and applying statistical methods
for quantitative studies in biomedicine, health, and population sciences.
Biostatisticians play a crucial role in research design, collection and organization of data, analysis, presentation, and
interpretation of results. Career opportunities are usually found in governmental agencies, private industry, and medical
research institutions.
The Department of Biostatistics maintains collaborative relationships with other units of the University and with outside
agencies and institutions. Among the many affiliated institutions and centers are: Columbia University Irving Medical
Center, New York State Psychiatric Institute, the Department of Statistics at Columbia’s Morningside Campus, the
Gertrude H. Sergievsky Center (research in the field of neuroepidemiology), the Herbert Irving Comprehensive Cancer
Center and Institute of Cancer Research, the HIV Center for Clinical and Behavioral Studies, and the Irving Center for
Clinical Research.
Faculty in the Department of Biostatistics work at the frontier of public health, leading research teams that investigate
some of today’s most pressing health issues. Recruited from the top universities from around the world, the faculty bring
to the School a wealth of experience that serves to inform their research and teaching.
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
SRIKESH ARUNAJADAI(PhD, University of California, Berkeley)
Adjunct Assistant Professor of Biostatistics
Research interests: Time Series Analysis, longitudinal data analysis, statistical modeling, data fusion, machine and deep
learning applications and healthcare
KIROS BERHANE(PhD, University of Toronto)
Cynthia and Robert Citron-Roslyn and Leslie Goldstein Professor and Chair
Research interests: Longitudinal data modeling, multi-level growth curve models, nonparametric regression, multiple
outcomes, quantile regression, mediation, applications to environmental data
MELISSA D. BEGG (ScD, Harvard University)
Professor of Clinical Biostatistics and Dean of Social Work
Research interests: Analysis of clustered data, oral health research, mental health statistics, clinical research training
XIAOYU CHE (PhD, Claremont Graduate University)
Assistant Professor of Biostatistics (in the Center for Infection & Immunity)
Research interests: Statistical modeling, predictive modeling/machine learning, survival analysis, counting processes,
statistical applications in system biology for chronic and emerging infectious diseases, neurodevelopmental disorders
QIXUAN CHEN (PhD, University of Michigan)
Associate Professor of Biostatistics
Research interests: Bayesian inference for complex survey data, analysis of incomplete data, non-parametric regression, and
random effects models
BIN CHENG(PhD, University of Wisconsin-Madison)
Professor of Biostatistics
Research interests: Linear and generalized linear mixed models, statistical analysis of clinical trials, longitudinal non-normal
data modeling, statistical computing, statistical inference on manifolds
KENNETH CHEUNG (PhD, University of Wisconsin-Madison)
Professor of Biostatistics
Research interests: Design and analysis of clinical trials, methods in toxicology studies and bioassay, applications of Monte
Carlo methods, nonparametric methods, bioethics
PROFILES
faculty
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
DEBRA D’ANGELO (MS, Columbia University)
Associate in Biostatistics
Research interests: Applied biostatistical consulting in various medical specialties, database development and data
management for research studies, SQL programming
HANGA GALFALVY (PhD, University of Illinois at Urbana-Champaign)
Associate Professor of Biostatistics (in Psychiatry)
Research interests: Statistical methodology in psychiatric research, with a special focus on the prediction models for
suicidal behavior from high-dimensional data, censored regression models, statistical genetics, and longitudinal data
analysis in observational studies
JEFF GOLDSMITH (PhD, Johns Hopkins University)
Associate Professor of Biostatistics
Research interests: Functional data analysis, high-dimensional regression, longitudinal data analysis, smoothing, Bayesian
variable selection, neuroimaging, and accelerometry
PRAKASH GORROOCHURN (PhD, Monash University)
Associate Professor of Clinical Biostatistics
Research interests: Mathematical population genetics, genetic mapping of complex diseases
WENPIN HOU (PhD, The University of Hong Kong)
Assistant Professor of Biostatistics
Research interests: Statistical genomics, Bayesian methods, functional data analysis, single-cell genomics and epigenomics
data modeling, gene regulation inference, spatio-temporal analysis, deep neural networks, Boolean networks controllability,
cancer and infectious disease research, maternal and child health
JIANHUA HU (PhD, University of North Carolina-Chapel Hill)
Professor of Biostatistics (in Medicine)
Research interests: high-dimensional genomics/proteomics, imaging, and longitudinal data, modeling disease heterogeneity,
and adaptive designs to achieve personalized treatments
IULIANA IONITA-LAZA (PhD, New York University)
Professor of Biostatistics
Research interests: Statistical genetics and bioinformatics
HAOMIAO JIA (PhD, Case Western University)
Professor of Biostatistics (in Nursing)
Research interests: Small area estimation, data smoothing, temporal-spatial analysis, survey sampling
ZHEZHEN JIN (PhD, Columbia University)
Professor of Biostatistics
Research interests: Survival analysis, resampling methods, ROC curves, smoothing methods, nonparametric regression,
clinical trials
SEONJOO LEE (PhD, University of North Carolina-Chapel Hill)
Associate Professor of Clinical Biostatistics (in Psychiatry)
Research interests: Neuroimaging, cognitive neuroscience, machine learning, and functional data analysis
SHING M. LEE (PhD, Columbia University)
Associate Professor of Clinical Biostatistics
Research interests: Rapid dose finding techniques in Phase I trials, and the development of more sensitive endpoints in
Phase I Trials
PROFILES
faculty
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the department of
biostatistics
CHENG-SHIUN LEU(PhD, Columbia University)
Professor of Clinical Biostatistics (in Psychiatry)
Research interests: Sequential selection procedures for multi-armed clinical trials, statistical application in behavioral
studies
MOLEI LIU (PhD, Harvard University)
Assistant Professor of Biostatistics
Research interests: High dimensional statistics, semiparametric theory, federated learning, semi-supervised learning,
transfer learning, model-X inference, biomedical informatics, EHR studies
ZHONGHUA LIU (PhD, Harvard University)
Assistant Professor of Biostatistics
Research interests: mixed models, multiple testing, semiparametric efficiency theory, causal inference, causal
mediation analysis, Mendelian randomization, deep learning, statistical genetics/genomics with applications to
medicine and public health
CHRISTINE MAURO (PhD, Columbia University)
Assistant Professor of Biostatistics
Research interests: Analysis of clinical trials, longitudinal data analysis, statistical learning techniques, and the
application of statistics to problems in mental health research
IAN MCKEAGUE (PhD, University of North Carolina at Chapel Hill)
Professor of Biostatistics
Research interests: Survival analysis, competing risks in HIV/AIDS studies, inference for stochastic processes,
empirical likelihood, Markov chain Monte Carlo, functional data analysis, semiparametric efficiency, Bayesian
statistics, and martingale and counting process methods
DANIEL MALINSKY (PhD, Carnegie Mellon University)
Assistant Professor of Biostatistics
Research interests: Causal inference, graphical models, missing data, stochastic processes, machine learning,
algorithmic fairness, social & environmental determinants of health, health disparities
CALEB MILES (PhD, Harvard University)
Assistant Professor of Biostatistics
Research interests: causal inference, HIV, interference, measurement error, mediation analysis, semiparametric
inference
TODD OGDEN (PhD, Texas A&M University)
Professor of Biostatistics (in Psychiatry)
Research interests: Analysis of brain imaging data, functional data analysis, nonparametric regression, wavelet
applications, statistical modeling
MARTINA PAVLICOVA (PhD, Ohio State University)
Associate Professor of Clinical Biostatistics
Research interests: Functional magnetic resonance imaging, multiple comparisons methods, spatial statistics
MIN QIAN (PhD, University of Michigan)
Associate Professor of Biostatistics
Research interests: Medical decision making, dynamic treatment regimes, variable selection/model selection for
decision making, statistical machine learning, reinforcement learning, statistical inference, bootstrap, empirical
processes, concentration inequalities, stochastic processes
PROFILES
faculty
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the department of
biostatistics
YIFEI SUN (PhD, Johns Hopkins University)
Assistant Professor of Biostatistics
Research interests: General biostatistical methodology for survival, longitudinal and multivariate data, machine
learning,electronic health record data, wearable device data
JOHN (SEAMUS) L.THOMPSON (PhD, University of California-Los Angeles)
Clinical Professor of Biostatistics and Neurology
Research interests: Randomized clinical trials, trial design, neurology, data management systems
NAITEE TING (PhD, Colorado State University)
Adjunct Professor of Biostatistics
Research interests: Clinical development of new drugs, dose selection, Phase II
LINDA VALERI (PhD, Harvard University)
Assistant Professor of Biostatistics
Research interests: Causal inference, measurement error, missing data, mental health, environmental health, and health
disparities
MELANIE WALL (PhD, Iowa State University)
Professor of Biostatistics (in Psychiatry)
Research interests: Latent variable modeling, spatial, and longitudinal data analysis
SHUANG WANG (PhD, Yale University)
Professor of Biostatistics
Research interests: Statistical genetics, genetic epidemiology, quantitative trait loci analysis
YUANJIA WANG (PhD, Columbia University)
Professor of Biostatistics
Research interests: Statistical learning, analytics for personalized medicine, and network analysis; applications to psychiatric
disorders and neurological disorders
YING WEI (PhD, University of Illinois at Urbana-Champaign)
Professor of Biostatistics
Research interests: Quantile regression methods, growth charts estimation, longitudinal data analysis, semiparametric
modeling, and robust statistics
PRIYA WICKRAMARATNE (PhD, Yale University)
Associate Professor of Clinical Biostatistics
Research interests: Epidemiologic methods, observational studies, survival analysis, generalized linear models, psychiatric
epidemiology
XIAO WU (PhD, Harvard University)
Assistant Professor of Biostatistics
Research interests: Causal Inference, nonparametric statistics, Bayesian biostatistics, data science, environmental
biostatistics, climate and health
Degree Programs
• Describe the roles biostatistics serves in the discipline of public health.
• Describe the basic concepts of probability, random variation and commonly used
statistical probability distributions.
• Describe preferred methodological alternatives to commonly used statistical methods
when assumptions are not met.
• Distinguish among the different measurement scales and the implications for selection
of statistical methods to be used based on these distinctions.
• Apply descriptive techniques commonly used to summarize public health data.
• Apply common statistical methods for inference.
• Apply descriptive and inferential methodologies according to the type of study design
for answering a particular research question.
• Apply basic informatics techniques with vital statistics and public health records in the
description of public health characteristics and in public health research and evaluation.
• Interpret results of statistical analyses found in public health studies.
• Develop written and oral presentations based on statistical analyses for both public
health professionals and educated lay audiences.
MPH
p
rogram
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2022-2023 STUDENT HANDBOOK
the department
of biostatistics
Master of Public Health degree program
The Department of Biostatistics offers the two-year Master of Public (MPH) degree. The
MPH prepares specialists in public health who use and adapt statistical procedures for
health and medical care programs, or serve in a technical capacity as resource person and
collaborators in field and programmatic studies.
Upon satisfactory completion of the MPH Biostatistics, graduates will be able to:
Columbia MPH
Director: Martina Pavlicova, PhD
The Columbia Masters in Public Health in Biostatistics (MPH) is a two-year
program designed to enhance the quantitative skills of public health practitioners who will use
statistics frequently in their work.
Course Requirements
The structure of the degree program includes five components, which are all carefully timed and integrated
so that learning in one part of the program informs activities and assignments in another:
1. Discipline - courses required by your home department
2. Core - curriculum that provides the broad, interlocking foundation of knowledge needed for a career in
public health
3. Integration of Science and Practice - two semester long course that bridges the gap between what you
traditionally learn in a classroom and the real-world experience of working as a public health professional
4. Leadership & Development - course aims to develop and improve MPH students’ abilities in three key
areas: leading teams in a variety of settings, working effectively as a team member, and implementing
fresh, innovative ideas within an organization or larger community.
5. Practicum - supervised practical experience in the field
6. Culminating Experience - combination of two capstone courses that are designed to connect the skills
and knowledge acquired throughout the degree program
Certificate
Every student in the two-year MPH program enrolls in a certificate program which provides training in a
focused area of expertise—in addition to the student’s departmental discipline—and leads to a Columbia
University approved credential. The certificate programs have been developed in consultation with public
health employers and other key stakeholders and reflect today’s most sought-after skills and knowledge.
Students taking the Columbia MPH within the Department of Biostatistics are able to select a certificate
from various school-wide certificate programs listed below. You can also find requirements and sample
coursework for each certificate using the Certificate Requirements Database.
Advanced Epidemiology
Child Youth and Family Health
Climate and Health
CEOR
Environmental Health Policy
Epi of Chronic Disease
Global Health (3 month)
Health and Human Rights
Health Communication
Health of an Aging Society
Health Policy and Practice
Health Promotion Research and
Practice
History, Ethics, Law
Infectious Disease Epidemiology
Injury Prevention and Control
Molecular Epi
Public Health Research Methods
Sexuality, Sexual, and Repro Health
Social Determinants of Health
Toxicology
Degree Programs
continued
MPH
program
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
COLUMBIA MPH
degree programs
Practicum
One term of practical experience is required of all students, intended to provide educational opportunities
that are different than and supplementary to the more academic aspects of the program. The practicum
may be completed over the summer after the first year. MPH students are required to do a minimum of
280 hours in a public health setting.
All MPH students must complete the practicum scope of work (SOW form prior to starting a practicum
experience. The SOW, which is managed by the Mailman’s Office of Careers and Practice, is an important
tool for planning the practicum and meeting the School’s requirements for engaging in a structured
practicum process. Students must develop a practicum SOW in collaboration with the practicum
organization .
Student along with their practicum advisor must identify at least at least 3 foundational MPH
competencies and 2 departmental competencies (listed in the student handbook) that the practicum
will fulfill. These competencies and how they will be fulfilled must be described in the proposed SOW. In
addition, the student along with their practicum supervisor must identify at least two deliverables expected
at the close of the practicum and describe them in detail in the proposed SOW.
The SOW must be approved by the student’s faculty advisor and the Practicum Academic Coordinator
(Corey Adams) before the start of the practicum. After the completion of the practicum, a copy of the two
deliverables described in SOW must be submitted to the Director of Academic Programs.
Students will present their experience at a Practicum Symposium which will be held towards the end of
second spring semester.
Culminating Experience
A formal culminating experience is required for graduation. The MPH culminating experience consists of a
combination of the Capstone Consulting Seminar and the Integrative Capstone Experience, which are
both taken during the student’s last spring semester.
The Capstone Consulting Seminar is a one-credit course that requires students to attend at least one
session of the Biostatistics Consulting Service and present the consult to the class for discussion. The
Biostatistics Consulting Service, which is run by faculty in the Department of Biostatistics, offers advice on
data analysis and appropriate methods of data presentation for publications, and provides design
recommendations for public health and clinical research, including preparation of grant proposals and
manuscripts.
The Integrative Capstone Experience is a two-credit course in which students produce a written report
that describes, interprets, and compares multiple analyses of relevant data using statistical techniques
learned during the course of the MPH program.
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
COLUMBIA MPH
degree programs
Curriculum
Timeline
Fall I Spring I Fall 2 Spring 2
MPH Core
Integration of Science and
Practice
P8107 Introduction to
Mathematical Statistics
Practicum
Leadership and
Development
P8110 Applied Regression II
Certificate Requirements
P6110 Statistical
Computing Using SAS
Certificate Requirements
P8100 Applied Regression I
P8120 Analysis of
Categorical Data
Required Discipline Courses
Points
Statistical Computing Using SAS
3
Applied Regression I
3
Introduction to Mathematical Statistics 3
Applied Regression II 3
Analysis of Categorical Data 3
Capstone Consulting Seminar
TOTAL POINTS FROM REQUIRED COURSES 18
Elective Courses
Choose courses from this list or from alternatives approved by your academic advisor
Points
89260
Building Interdisciplinary Research models
2
P8101
Introduction to Health Data Science
3
P8105
Data Science I
3
P8106
Data Science II
3
P8140
Randomized Clinical Trials
3
P8142
Clinical Trial Methodology
3
P8144
Pharmaceutical Statistics
3
P8158
Latent Variable and Structural Equation Modeling for Health Sciences
3
P8160
Topics in Advanced Statistical Computing
3
P8180
Relational Databases and SQL Programming for Research and Data Science
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
1
2
3
P6110
P8100
P8107
P8110
P8120
P8185
P8170
Integrative Capstone Experience
2
P8185 Capstone Consulting
Seminar
P8170 Integrative Capstone
Experience
Degree Programs
continued
Master of Science degree programs
The Department of Biostatistics offers two Master of Science degree
programs: the MS in Biostatistics and the MS in Patient Oriented Research.
Students pursuing the MS in Biostatistics degree select one of four tracks of specialization: Clinical
Research Methods, Pharmaceutical Statistics, Public Health Data Science, Statistical Genetics, and Theory
& Methods. The MS in Patient Oriented Research degree program is also housed in the department.
Whether the focus of the degree is to prepare for doctoral research training, to advance the skills critical
for clinical scientists, or as a biostatistician in public health or the pharmaceutical industry, both
programs require a facility for quantitative reasoning and a true enjoyment of working with data.
Upon satisfactory completion of the MS in Biostatistics or the MS in Patient Oriented Research, graduates
will be able to:
Data Analysis and Computing
• Formulate and produce graphical displays of quantitative information that effectively communicate
analytic findings
• Explain general principles of study design in attempting to identify risk factors for disease, isolate
targets for prevention, and assess the effectiveness of one or more interventions
• Select and perform appropriate hypothesis tests for comparing two or more independent exposure
groups, or two or more groups of matched/clustered subjects, with respect to a discrete or continuous
response measurement of interest
• Interpret associations estimated via linear regression, logistic regression, and Cox models for survival
data
• Apply the basic tenets of research design and analysis for the purpose of critically reviewing research
and programs in disciplines outside of biostatistics
• Interpret quantitative findings in accurate, accessible language for colleagues outside of biostatistics, as
well as for broader dissemination to the public and other public health professionals
Public Health and Collaborative Research
• Translate research objectives into testable hypotheses
• Compare and contrast different study designs and their implications for inference in medical/public
health research
• Describe basic principles and the practical importance of key concepts from probability and inference to
colleagues without extensive statistical training
• Develop and execute power and sample size calculations for research studies utilizing simple random
sampling
• Evaluate research reports and proposals for research funding on the basis of their scientific integrity,
validity, and the strength of the quantitative analysis
MS
programs
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the department of
biostatistics
degree programs
MS PROGRAMS
A brief comparison of the MS Degree Programs
Degree Program Track
Minimum
Credits
Typical
Duration
Practicum
Capstone
Master of Science
in Biostatistics
Clinical Research
Methods Track
(MS/CRM)
30
4 semesters
No Yes
Master of Science
in Biostatistics
Pharmaceutical
Statistics Track
(MS/PS)
35 4 semesters Yes Yes
Master of Science
in Biostatistics
Statistical
Genetics Track
(MS/SG)
36 4 semesters Yes Yes
Master of Science
in Biostatistics
Theory and
Methods Track
(MS/TM)
36 4 semesters Yes Yes
Master of Science in
Patient Oriented
Research
Patient Oriented
Research Program
(MS-POR)
30
5 semesters
(including
summer)
No
Yes
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the department of
biostatistics
Master of Science
in Biostatistics
Public Health Data
Science Track
(MS/PHDS)
36
4 semesters
Yes
Yes
Clinical Research Methods Track
Director: Todd Ogden, PhD
The Master of Science in Biostatistics - Clinical Research Methods (MS/
CRM) provides formal, rigorous training in skills critical to the design and analysis of clinically oriented research
studies. It is intended for physicians, nurses, dentists, psychologists, pharmacists, and other health care
professionals who plan careers or are actively engaged in clinical research. MS/CRM students will hone their
quantitative talents to better pursue research objectives in their chosen fields. As the level of competitiveness
for limited research support increases, it is now more important than ever to develop a well-designed study with
a strong analytic plan. Mastery of applied biostatistical methods improves the likelihood of assembling
compelling and effective clinical research projects and promoting good research practices.
Course Requirements
The required courses are intended to enable degree candidates to gain proficiency in study design, application of
commonly-used statistical procedures, facility with statistical software packages, and ability to successfully interpret and
communicate the results of an analysis. Students must complete a minimum of 30 points to earn the MS/CRM degree, of
which 24 points must be taken at the Mailman School of Public Health. Up to two electives may be taken pass/fail, intended
to encourage students to take courses outside their field of experience.
Note that some courses in the required curriculum may be waived based on prior coursework with approval of the course
instructor. In this event, the student may substitute another, more advanced course in place of the waived course. Students
interested in completing the program in 1.5 years, would be well served to begin coursework by enrolling in the Columbia
Summer Research Institute which allows students to complete 10 credits over 5 weeks.
In advance of beginning the MS program, any student who has not previously completed an MPH will be required to take
the online course offered by the Mailman School: PUBH P6025-Introduction to Public Health.
Students’ progress will be reviewed after each semester. Students whose academic performance falls below a B average
(3.0 GPA) in required courses may not be allowed to graduate without remedial course work.
Capstone Experience
As part of the MS/CRM training, each student is required to enroll in P9160 Master’s Essay—Clinical Research Methods.
This research component of the MS/CRM program should be completed during the final year of study. Conducted in
workshop style, students in this course will participate in a weekly seminar geared towards enhancing research skills. At
the end of the term, each student will be required to submit a research paper of publishable quality, summarizing their
research project. Before beginning P9160, each student must have a data set of interest available to them, as well as
permission (and IRB approval) to analyze and publish results from an analysis of these data. Most P9160 sessions will be
devoted to discussion of the individual research projects. Students will present their topics, plans for analysis, and
interpretation of their findings to the class for evaluation and feedback. The completion and submission of this research
paper satisfies the student’s capstone requirement.
MS
programs
Degree Programs
continued
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2022-2023 STUDENT HANDBOOK
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degree programs
MS / CRM TRACK
Required Courses
Points
P6104 Introduction to Biostatistical Methods 3
P6400 Principles of Epidemiology 3
P8100 Applied Regression I 3
P8110 Applied Regression II 3
P8120 Analysis of Categorical Data 3
P8140 Introduction to Randomized Clinical Trials 3
P8438 Epidemiology II: Design and Conduct of Observational Epidemiology 3
P9160
Master’s Essay - Clinical Research Methods
TOTAL POINTS FROM REQUIRED COURSES 24
Elective Courses
Choose courses from this list or from alternatives approved by your academic advisor.
Points
P6110 3
P6530 3
P8104 3
P8105 3
P8109 3
P8112 1.5
P8142 3
P8144 3
P8149 3
P8157 3
P8158 3
P8180
P8307 3
P8308 3
P8404 3
P8405 3
P8406 3
P8414 3
P8417 3
P8432 3
P8440 3
P8482 3
P8508
Statistical Computing with SAS
Issues and Approaches in Health Policy and Management
Probability
Data Science I
Statistical Inference
Systematic Review and Meta-analysis
Clinical Trial Methodology
Pharmaceutical Statistics
Human Population Genetics
Analysis of Longitudinal Data
Latent Variable and Structural Equation Modeling for Health Sciences
Relational Databases and SQL Programming for Research and Data Science
Molecular Epidemiology
Molecular Toxicology
Epidemiology of Genetics and Aging
Genetics in Epidemiology
Epidemiology of Infections Diseases I
Cancer Epidemiology
Selected Problems of Measurement in Epidemiology
Environmental Epidemiology
Epidemiology of Cardiovascular Diseases
Outcomes Research: Methods and Public Health Implications
Analysis of Large Scale Data Sets
1.5
Curriculum (TOTAL POINTS: 30 OR MORE)
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the department of
biostatistics
3
3
Fall I Spring I Fall II Spring II
P6104 Introduction to
Biostatistical Methods I
P8100 Applied Regression I P8110 Applied Regression II P8438 Epidemiology II
P6400 Principles of
Epidemiology
P8120 Analysis of
Categorical Data
P8140 Introduction to RCTs P9160 Master’s Essay
Elective Elective
Sample Timeline
MS / CRM TRACK
degree programs
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the department of
biostatistics
MS
programs
Degree Programs
continued
Pharmaceutical Statistics
Director: Ken Cheung, PhD
The Master of Science in Biostatistics - Pharmaceutical Statistics (MS/PS)
provides study design, research, and biostatistics skills to individuals who are currently working in the
pharmaceutical research industry and those seeking to begin a career in the industry. MS/PS students
will understand the challenges and modern methods relevant to translational research and clinical
trials.
Course Requirements
Students must complete a minimum of 35 credits of coursework to earn the MS/PS degree, of which 30 points must be
taken at the Mailman School of Public Health. Up to two electives may be taken pass/fail, especially to encourage students
to take courses outside their field of experience.
Note that some courses in the required curriculum may be waived based on prior graduate level coursework with
approval from the course instructor. In this event, the student may substitute another, more advanced course in place of
the waived course. Credits from waived courses do not count towards the degree.
In advance of beginning the MS program, any student who has not previously completed an MPH will be required to take
the online course offered by the Mailman School: PUBH P6025-Introduction to Public Health.
Students’ progress will be reviewed after each semester. Those students whose academic performance falls below a
cumulative B average (3.0 GPA) in required courses may not be allowed to graduate without remedial course work.
NOTES: 1) International students are required to be registered for at least 12 credits during their second and third
semesters. 2) Request for a track change must be made before the start of a student's second semester. Tracks cannot
be changed once the second semester has begun.
Practicum Requirement
One term of practical experience is required of all students, providing educational opportunities that are different from
and supplementary to the more academic aspects of the program. The practicum may be fulfilled during the school year
or over the summer. Arrangements are made on an individual basis in consultation with faculty advisors who must
approve both the proposed practicum project prior to its initiation, and the report submitted at the conclusion of the
practicum experience. Students will be required to make a presentation at the department’s Annual Practicum
Symposium which is held in late April/early May. See the practicum information section at the back of the handbooks
for more details.
Capstone Experience
A formal culminating experience is required for graduation. The capstone consulting experience is designed to enable
students to demonstrate their ability to integrate their academic studies with the role of biostatistical consultant/
collaborator, which will comprise the major portion of their future professional practice. Students register for P8185
Capstone Consulting Seminar, a one-semester, one-credit course during their final spring semester. The course requires
students to attend at least one session of the Biostatistics Consulting Service and present the consult to the class for
discussion. The Consulting Service, which is run by faculty in the Department of Biostatistics, offers advice on data
analysis and appropriate methods of data presentation for publications, and provides design recommendations for public
health and clinical research, including preparation of grant proposals and manuscripts.
16
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
Required Courses
Points
P6110 3
3
3
3
3
3
P6170
P6400
P8104
P8130
P8120
P8140 3
P8142* 3
P8144 3
P8180
P8185
Statistical Computing with SAS
New Drug Development: A Regulatory Overview
Principles of Epidemiology
Probability
Biostatistical Methods I
Analysis of Categorical Data
Introduction to Randomized Clinical Trials
Clinical Trial Methodology
Pharmaceutical Statistics
Relational Databases and SQL Programming for Research and Data Science
Capstone Consulting Seminar 1
TOTAL POINTS FROM REQUIRED COURSES 31
degree programs
MS / PS TRACK
Curriculum (TOTAL POINTS: 35 OR MORE)
Elective Courses
Choose courses from this list or from alternatives approved by your faculty advisor.
Points
P6503 Introduction to Health Economics 3
P8105 Data Science I 3
P8108 ** Survival Analysis 3
P8109 Statistical Inference 3
P8116 Design of Medical Experiments 3
P8133 Bayesian Analysis and Adaptive Designs in Clinical Trials 3
P8157 ** Analysis of Longitudinal Data 3
P8401 Pharmacoepidemiology 3
G4010 Responsible Conduct of Research and Related Policy Issues 1
W4200 Biopharmaceutical Development and Regulation 3
W4201 Seminar in Biopharmaceutical Development and Regulation 3
*P8142 may be replaced by P8133, which requires P8104 and P8109
** requires P8104 and P8109
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
3
degree programs
MS / PS TRACK
Sample Timeline
Fall I Spring I Fall II Spring II
P6400 Principles of
Epidemiology I
P6170 New Drug
Development
P6110 Statistical Computing
Using SAS
P8144 Pharmaceutical
Statistics
P8104 Probability
P8120 Analysis of
Categorical Data
P8142 Clinical Trial Methodology
(or P8133 Adaptive Designs)
P8185 Capstone
Consulting Seminar
P8130 Biostatistical
Methods I
P8140 Introduction to RCTs
P8180 Relational Databases
and SQL
Complete practicum
requirements
Elective Elective
18
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
MS
programs
Degree Programs
continued
Statistical Genetics
Director: Prakash Gorroochurn, PhD
The Master of Science in Biostatistics - Statistical Genetics (MS/SG) prepares
well-qualified students to use advanced modern statistical genetic methods to dissect complicated
human genetic archeology with cutting-edge technologies. Students begin with a rigorous grounding
in statistical theory and practice, and then incorporate modern analytic methods into their tool box
via new coursework.
Course Requirements
MS/SG students are expected to gain proficiency in genetic study design and analysis as represented by the courses
listed below. Students must complete a minimum of 36 academic credits to earn the MS/SG degree, of which 30 points
must be taken at the Mailman School of Public Health. Up to two electives may be taken pass/fail, especially to
encourage students to take courses outside their field of experience.
Note that some courses in the required curriculum may be waived based on prior graduate level coursework with
approval from the course instructor. In this event, the student may substitute another, more advanced course in place of
the waived course. Credits from waived courses do not count towards the degree.
In advance of beginning the MS program, any student who has not previously completed an MPH will be required to take
the online course offered by the Mailman School: PUBH P6025-Introduction to Public Health.
Students’ progress will be reviewed after each semester. Those students whose academic performance falls below a
cumulative B average (3.0 GPA) in required courses may not be allowed to graduate without remedial course work.
A student is considered full-time in the MS/SG program if he or she takes a minimum of 12 credits per semester.
NOTES: 1) International students are required to be registered for at least 12 credits during their second and third
semesters. 2) Request for a track change must be made before the start of a student's second semester. Tracks cannot
be changed once the second semester has begun.
Practicum Requirement
One term of practical experience is required of all students, providing educational opportunities that are different from
and supplementary to the more academic aspects of the program. The practicum may be fulfilled during the school year
or over the summer. Arrangements are made on an individual basis in consultation with faculty advisors who must
approve both the proposed practicum project prior to its initiation, and the report submitted at the conclusion of the
practicum experience. Students will be required to make a presentation at the department’s Annual Practicum
Symposium which is held in late April/early May. See the practicum information section at the back of the handbook for
more details.
Capstone Experience
A formal culminating experience is required for graduation. The capstone consulting experience is designed to enable
students to demonstrate their ability to integrate their academic studies with the role of biostatistical consultant/
collaborator, which will comprise the major portion of their future professional practice. Students register for P8185
Capstone Consulting Seminar, a one-semester, one-credit course during their final spring semester. The course requires
students to attend at least one session of the Biostatistics Consulting Service and present the consult to the class for
discussion. The Consulting Service, which is run by faculty in the Department of Biostatistics, offers advice on data
analysis and appropriate methods of data presentation for publications, and provides design recommendations for
public health and clinical research, including preparation of grant proposals and manuscripts.
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
degree programs
MS / SG TRACK
Sample Timeline
Elective Courses
Choose courses from this list or from alternatives approved by your academic advisor.
Points
3
W4761
3
W4771
3
P8160
3
P8405
3
P8438
Fall I Spring I Fall II Spring II
P8104 Probability
P8109 Statistical Inference
P6400 Principles of
Epidemiology
P8185 Capstone Consulting
Seminar
P8105 Data Science I
P8131 Biostatistical
Methods II
P8119 Advanced Statistical
&
Computational Methods
Complete practicum
requirements
P8130 Biostatistical
Methods I
P8139 Statistical Genetics
Modeling
Elective
P8149 Human Population
Genetics
Elective
Elective
Core Biostatistics Courses
Points
P6400 Principles of Epidemiology 3
P8104 Probability 3
P8105 Data Science I 3
P8109 Statistical Inference 3
P8130 Biostatistical Methods I 3
P8131 Biostatistical Methods II 3
Core Genetics Courses
P8119 Adv. Statistical and Computational Methods in Genetics & Genomics 3
P8139 Statistical Genetics Modeling 3
P8149 Human Population Genetics 3
P8185 Capstone Consulting Seminar 1
TOTAL POINTS FROM REQUIRED COURSES 28
Curriculum (TOTAL POINTS: 36 OR MORE)
Required Courses
20
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
Topics in Advanced Statistical Computing
Genetics in Epidemiology
Epidemiology II: Design and Conduct of Observational Epidemiology
Computational Genomics
Machine Learning
MS
programs
Degree Programs
continued
Theory & Methods
Director: Qixuan Chen, PhD
The Master of Science in Biostatistics - Theory and Methods (MS/TM)prepares
individuals for a career applying statistical methods in the biomedical sciences. The MS/TM program
is the appropriate program for a student whose goal is to work effectively as a biostatistician in a
biomedical, clinical, or laboratory research setting; or for a student who plans to pursue a PhD in
biostatistics.
Course Requirements
MS/TM students are expected to master certain mathematical and biostatistical concepts and techniques as
represented by the courses listed below. Students must complete a minimum of 36 points to earn the MS/TM degree,
of which 30 points must be taken at the Mailman School of Public Health. Up to two s/electives may be taken pass/fail
(i.e., one selective and one elective or two electives).
Note that some courses in the required curriculum may be waived based on prior graduate level coursework with
approval from the course instructor. In this event, the student may substitute another, more advanced course in place
of the waived course. Credits from waived courses do not count towards the degree.
In advance of beginning the MS program, any student who has not previously completed an MPH will be required to
take the online course offered by the Mailman School: PUBH P6025-Introduction to Public Health. Students’ progress
will be reviewed after each semester. Those students whose academic performance falls below a cumulative B average
(3.0 GPA) in required courses may not be allowed to graduate without remedial course work.
A student is considered full-time in the MS/TM program if he or she takes a minimum of 12 credits per semester.
NOTES: 1) International students are required to be registered for at least 12 credits during their second and third
semesters. 2) Request for a track change must be made before the start of a student's second semester. Tracks cannot
be changed once the second semester has begun.
Practicum Requirement
One term of practical experience is required of all students, providing educational opportunities that are different from
and supplementary to the more academic aspects of the program. The practicum may be fulfilled during the school
year or over the summer. Arrangements are made on an individual basis in consultation with faculty advisors who must
approve both the proposed practicum project prior to its initiation, and the report submitted at the conclusion of the
practicum experience. Students will be required to make a presentation at the department’s Annual Practicum
Symposium which is held in late April/early May. See the practicum information section at the back of the
handbook for details.
Capstone Experience
A formal culminating experience is required for graduation. The capstone consulting experience is designed to enable
students to demonstrate their ability to integrate their academic studies with the role of biostatistical consultant/
collaborator, which will comprise the major portion of their future professional practice. Students register for P8185
Capstone Consulting Seminar, a one-semester, one-credit course during their final spring semester. The course requires
students to attend at least one session of the Biostatistics Consulting Service and present the consult to the class for
discussion. The Consulting Service, which is run by faculty in the Department of Biostatistics, offers advice on data
analysis and appropriate methods of data presentation for publications, and provides design recommendations for
public health and clinical research, including preparation of grant proposals and manuscripts.
21
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
MS / TM TRACK
degree programs
Curriculum (TOTAL POINTS: 36 OR MORE)
Required Courses
Points
P6400 Principles of Epidemiology
3
P8104 Probability
P8105 Data Science I
P8109 Statistical Inference
P8130 Biostatistical Methods I
P8131 Biostatistical Methods II
P8185 Capstone Consulting Seminar
TOTAL POINTS FROM REQUIRED COURSES 19
Points
P6110
3
P8106 3
P8108
3
P8119 3
P8122 3
P8123 3
P8149
3
P8157
3
P8158
3
P8160
3
P8180
P9120
Topics in Statistical Learning and Data Mining I
3
Selective Courses
Choose 1 course from each group.
Points
GROUP 1: Principles of Statistical Design
P8116 Design of Medical Experiments
3
P8140 Randomized Clinical Trials
P8142 Clinical Trial Methodology
P8144 Pharmaceutical Statistics
P8133 Bayesian Analysis and Adaptive Designs in Clinical Trials
GROUP 2: Advanced Statistical Methods
P8157
Elective Courses
Choose courses from this list or from alternatives approved by your academic advisor.
Analysis of Longitudinal Data
22
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
3
P8123
Analysis of Health Surveys
P8124
Statistical Computing with SAS
Data Science II
Survival Analysis (if not chosen as a selective)
Adv Statistical and Computational Methods in Genetics & Genomics
Statistical Methods for Causal Inference (if not chosen as a selective)
Analysis of Health Surveys (if not chosen as a selective)
Graphical Models for Complex Health Data (if not chosen as a selective)
Human Population Genetics
Analysis of Longitudinal Data (if not chosen as a selective)
Latent Variable and Structural Equation Modeling for Health Sciences
Topics in Advanced Statistical Computing for Health Sciences
Relational Databases and SQL Programming for Research and Data Science
3
3
3
3
3
3
1
TOTAL POINTS FROM SELECTIVE COURSES 6
P8108
Survival Analysis
3
3
3
3
3
3
3
MS / TM TRACK
degree programs
Sample Timeline
Fall I Spring I Fall II Spring II
P6400 Principles of
Epidemiology
P8109 Statistical Inference
Selective/Elective
P8185 Capstone Consulting
Seminar
P8104 Probability
P8131 Biostatistical
Methods II
Selective/Elective
P8105 Data Science
Selective/Elective Selective/Elective
P8130 Biostatistical
Methods I
Selective/Elective Selective/Elective
23
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
Complete practicum
requirements
MS
programs
Degree Programs
continued
Public Health Data Science
Director: Min Qian, PhD
The Master of Science in Biostatistics - Public Health Data Science(MS/PHDS)prepares
students interested in careers as biostatisticians applying statistical methods in health-related
research settings. The track provides core training in biostatistical theory, methods, and applications,
but adds a distinct emphasis on modern approaches to statistical learning, reproducible and
transparent code, and data management.
Course Requirements
MS/PHDS students are expected to gain proficiency in genetic study design and analysis as represented by the
courses listed below. Students must complete a minimum of 36 academic credits to earn the MS/PHDS degree, of
which 30 points must be taken at the Mailman School of Public Health. Up to two electives may be taken pass/fail,
especially to encourage students to take courses outside their field of experience.
Note that some courses in the required curriculum may be waived based on prior graduate level coursework with
approval from the course instructor. In this event, the student may substitute another, more advanced course in place
of the waived course. Credits from waived courses do not count towards the degree.
In advance of beginning the MS program, any student who has not previously completed an MPH will be required to
take the online course offered by the Mailman School: PUBH P6025-Introduction to Public Health.
Students’ progress will be reviewed after each semester. Those students whose academic performance falls below a
cumulative B average (3.0 GPA) in required courses may not be allowed to graduate without remedial course work.
A student is considered full-time in the MS/PHDS program if he or she takes a minimum of 12 credits per semester.
NOTES: 1) International students are required to be registered for at least 12 credits during their second and third
semesters. 2) Request for a track change must be made before the start of a student's second semester. Tracks cannot
be changed once the second semester has begun.
Practicum Requirement
One term of practical experience is required of all students, providing educational opportunities that are different from
and supplementary to the more academic aspects of the program. The practicum may be fulfilled during the school year
or over the summer. Arrangements are made on an individual basis in consultation with faculty advisors who must
approve both the proposed practicum project prior to its initiation, and the report submitted at the conclusion of the
practicum experience. Students will be required to make a presentation at the department’s Annual Practicum
Symposium which is held in late April/early May. See the practicum information section at the back of the handbook
for more details.
Capstone Experience
A formal culminating experience is required for graduation. The capstone consulting experience is designed to enable
students to demonstrate their ability to integrate their academic studies with the role of biostatistical consultant/
collaborator, which will comprise the major portion of their future professional practice. Students register for P8185
Capstone Consulting Seminar, a one-semester, one-credit course during their final spring semester. The course requires
students to attend at least one session of the Biostatistics Consulting Service and present the consult to the class for
discussion. The Consulting Service, which is run by faculty in the Department of Biostatistics, offers advice on data
analysis and appropriate methods of data presentation for publications, and provides design recommendations for
public health and clinical research, including preparation of grant proposals and manuscripts.
24
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
degree programs
MS / PHDS TRACK
Sample Timeline
Elective Courses
Choose courses from this list or from alternatives approved by your academic advisor.
Points
P6110
3
P8108
3
P8119
3
P8157
Analysis of Longitudinal Data
3
P8158
Latent Variable and Structural Equation Modeling for Health Sciences
3
3
Fall I Spring I Fall II Spring II
P8104 Probability
P8109 Statistical Inference
P8180 Relational Databases
and SQL Programming
P8185 Capstone Consulting
Seminar
P8105 Data Science I
P8106 Data Science II
Complete practicum
requirements
P8130 Biostatistical
Methods I
P8131 Biostatistical
Methods II
Elective
P6400 Principles of
Epidemiology
Elective
Elective
Points
P6400 Principles of Epidemiology 3
P8104 Probability 3
P8105 Data Science I 3
P8109
Statistical Inference
3
P8130 Biostatistical Methods I
3
P8131
Biostatistical Methods II
3
P8180 Relational Databases and SQL Programming for Research and Data Science 3
P8185 Capstone Consulting Seminar 1
TOTAL POINTS FROM REQUIRED COURSES 25
Curriculum (TOTAL POINTS: 36 OR MORE)
Required Courses
25
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
P8106
Data Science II
3
Statistical Computing Using SAS
Survival Analysis
Adv Statistical and Computational Methods in Genetics & Genomics
P9120
Topics in Statistical Learning and Data Mining I
3
Elective
Topics in Advanced Statistical Computing for Health Sciences
P8160
P8158
P8124 Graphical Models for Complex Health Data
3
Patient Oriented Research
Director: Todd Ogden, PhD
The Master of Science in Patient Oriented Research (MSPOR)provides
training in the fundamentals of clinical and translational investigation, with a view to enabling young
researchers to compete more effectively for research funding. MS/POR students are trained in the
design, conduct, and evaluation of clinical research studies, with close supervision and support from
the Program Director. The program is comprised of an interdisciplinary series of courses and
colloquia that reflects both the public health faculty’s expertise in design and conduct of research
studies and the clinical faculty’s intimate knowledge of human health and patient care.
Course Requirements
The required courses are intended to enable degree candidates to gain proficiency in study design, application of
commonly-used statistical procedures, facility with statistical software, and ability to successfully interpret and
communicate the results of an analysis. The overall goal is to make graduates more competitive in pursuit of research
funding. The two-year MS-POR curriculum consists of 30 credits in total and a culminating Master’s Essay. Up to two
s/electives may be taken pass/fail (i.e., one selective and one elective or two electives).
MS/POR candidates must begin study during their first summer by enrolling in the Columbia Summer Research Institute
(CSRI). In the CSRI, students will earn 10 credits, completing courses in biostatistics, epidemiology, NIH grant writing,
health disparities research and decision analysis. With this, students earn one-third of the required credits in the first
summer, leaving greater flexibility and fewer scheduling commitments over the remaining months.
Note that some courses in the required curriculum may be waived based on prior graduate level coursework with
approval from the course instructor. In this event, the student may substitute another, more advanced course in place
of the waived course. Credits from waived courses do not count towards the degree.
In advance of beginning the MS program, any student who has not previously completed an MPH will be required to
take the online course offered by the Mailman School: PUBH P6025-Introduction to Public Health.
Students’ progress will be reviewed after each semester. Those students whose academic performance falls
below a B average (3.0 GPA) in required courses may not be allowed to graduate without remedial course work.
Capstone Experience
As part of the MS/POR training, each student is required to register for Public Health P9165 Master’s Essay-Patient
Oriented Research, and complete a master’s essay consisting of the construction of an NIH-style grant application. The
student is supervised by a ProJect Sponsor from biostatistics and by a clinical mentor from the student’s own field of
expertise. At the end of the term, each student will submit a research grant proposal, following NIH guidelines for
applications. Each proposal will be reviewed by the program leaders, followed by a formal presentation to the
TRANSFORM (Training And Nurturing Scholars FOr Research that is Multidisciplinary) Advisory Board. The completion,
submission, and presentation of the research proposal fulfill the capstone requirement.
MS-POR
program
Degree Programs
continued
26
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
Required Courses
Points
P6104 Introduction to Biostatistical Methods (CSRI) 3
P6400 Principles of Epidemiology (CSRI)
3
P8103 Colloquium on Patient Oriented Research (taken over four semesters)
2
P8120 Analysis of Categorical Data
3
P8182 Writing a Successful Grant Application (CSRI)
1
P8568 Decision Analysis for Clinical and Public Health Practices (CSRI)
2
P8750 Race and Health (CSRI)
1
P9165 Master’s Essay - Patient Oriented Research 0
G4010 Responsible Conduct of Research and Related Policy Issues
1
M9780 Funding for Research Activities: Basic Issues in Obtaining Support
1
89260 Building Interdisciplinary Research Models (also N9260)
2
MS - POR
degree programs
Curriculum (TOTAL POINTS: 30 OR MORE)
Restricted electives (selectives)
Points
Precision Medicine and Genetics
M7208 Precision Medicine 3
P6385 Principles of Genetics and the Environment 3
P8119 Adv Statistical & Computational Methods in Genetics & Genomics 3
P8405 Genetics in Epidemiology 3
Mechanisms/Molecular Electives
G4500 Cancer Biology 1 3
G6003 Mechanisms in Human Disease I 4.5
P8307 Molecular Epidemiology 3
P8308 Molecular Toxicology 3
P8312 Fundamentals of Toxicology 3
P8319 Biological Markers of Chemical Exposure 3
Data and Computing
G4001 Introduction to Computer Application in Health Care & Biomedicine 3
P6110 Statistical Computing Using SAS 3
MSPOR elective courses continued on
next page
27
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
TOTAL POINTS FROM REQUIRED COURSES 19
In addition to the 11 courses listed above, students are required to take at least two of the following courses of which at least one
course has to be from the Biostatistics and Epidemiology list. Other courses related to precision medicine, statistical genetics, molecular
biology mechanisms, data and computing, dissemination/implementation, biostatistics, or epidemiology may count towards fulfilling
the selective requirement as long as they are approved in advance by the MS/POR Advisory Committee.
MS - POR
degree programs
P8101 Introduction to Health Data Science 3
P8105 Data Science I 3
P8180
Relational Databases and SQL Programming for Research and Data Science
P8451 Introduction to Machine Learning for Epidemiology 3
Dissemination & Implementation Science and Community-based
Participatory Research
P8792 Dissemination and Implementation Science 3
P8771 Community Based Participatory Research 3
Biostatistics and Epidemiology
P8100 Applied Regression I 3
P8110 Applied Regression II 3
P8112 Systematic Review and Meta-analysis 1.5
P8122 Statistical Methods for Causal Inference 3
P8140 Randomized Clinical Trials 3
P8142 Clinical Trials Methodology 3
P8400 Epi III: Applied Epidemiological Analysis 3
P8401 Pharmacoepidemiology 3
P8438 Epi II: Design and Conduct of Observational Epidemiology 3
P8450 Clinical Epidemiology 3
P8777 Survey Research Methods 3
P8902 Introduction to Mixed Methods 3
Elective Courses. Students will choose elective courses from one or more of the following:
• Departments of Epidemiology or Biostatistics, or other departments at the School of Public Health
• From the list of restricted electives (selectives) see above
• Elective courses from other Columbia schools in the list below
Points
B8128 Healthcare Investment and Entrepreneurship 1.5
B8342 Healthcare Investment and Deal-making 1.5
B8692 Pharmaceutical Drug Commercialization: Strategy & Practice 1.5
B8745 Forecasting for Drug Development Strategy 1.5
E6893 Topics in Information Processing: Big Data Analytics 3
G4006 Translational Bioinformatics 3
G4062 Public Health Informatics 1
Electives in Clinical and Translational Research
outside of Mailman School of Public Health
curriculum CONTINUED
28
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
3
MS - POR
degree programs
Sample Timeline
Summer I Fall I Spring I Fall II Spring II
P6104 Intro to
Biostatistical Methods
P8103 Colloquium (0.5)
P81033Colloquium (0.5)
P81033Colloquium (0.5)
P8103 Colloquium (0.5)
P6400 Principles of
Epidemiology
Elective
P8120 Analysis of
Categorical Analysis
Elective
M9780 Funding for
Research Activities
P8182 Writing a
Successful Grant
Selective
G4010 Responsible
Conduct of Research
P9165 Master’s Essay (POR Capstone)
P8568 Decision
Analysis
Selectives/Elective
89260 Building
Interdisciplinary
Research Models
P8750 Race and
Health
29
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
Degree Programs
CONTINUED
Doctoral degree programs
The Department of Biostatistics offers two doctoral degree programs: the Doctor
of Public Health (DrPH) and the Doctor of Philosophy (PhD). Both the DrPH and PhD
programs train candidates to apply state-of-the art statistical methods to the analysis of important public
health issues and potential solutions, but differ in their relative emphasis on application versus statistical
theory.
Doctor of Public Health (DrPH)
DrPH training places relatively greater emphasis on the application of statistical methods to public health problems,
although many DrPH students propose new methods and contribute to the advancement of statistical theory as part of
their dissertation research.
Upon satisfactory completion of the DrPH degree in Biostatistics, graduates will be able to:
Data Analysis and Computing
• Identify and implement advanced statistical models for the purposes of estimation, comparison, prediction, and adjustment in
non-standard settings
Public Health and Collaborative Research
• Describe the foundations of public health, including the biological, environmental, behavioral, and policy factors that affect the
health of populations
• Develop and execute calculations for power and sample size when planning research studies with complex sampling schemes;
• Formulate and prepare a written statistical plan for analysis of biomedical or public health research data that clearly reflects the
research hypotheses of the proposal in a manner appropriate for scientists with varied backgrounds
Consulting
• Function as an effective consultant in biomedical and public health research projects
• Communicate and write effectively in order to describe complex topics in a consulting environment
Data Management
• Identify the uses to which data management can be put in practical statistical analysis, including the establishment of standards
for documentation, archiving, auditing, and confidentiality; guidelines for accessibility; security; structural issues; and data cleaning
• Differentiate between analytical and data management functions through knowledge of the role and functions
of databases, different types of data storage, and the advantages and limitations of rigorous database systems in conjunction with
statistical tool
• Assess database tools and the database functions of statistical software, with a view to explaining the impact of data management
processes and procedures on their own research
Teaching
• Explain and illustrate principles of study design and data analytic techniques to public health students enrolled in first and second
level graduate public health courses
Biostatistical Research
• Identify and integrate new developments in the statistical literature for challenging research problems in public health
• Generate efficient computer code to implement sophisticated statistical techniques
doctoral
programs
30
2022-2023 STUDENT HANDBOOK
the department of
biostatistics
degree programs
DOCTORAL PROGRAMS
Doctor of Philosophy (PhD)
PhD training places relatively greater emphasis on the development of novel statistical theory and methods. A PhD
dissertation must represent an original contribution to statistical theory or methods that has relevance to a real
biomedical or public health application.
Upon satisfactory completion of the PhD degree in Biostatistics, graduates will be able to:
Data Analysis and Computing
• Identify and implement advanced statistical models for the purposes of estimation, comparison, prediction, and adjustment in
non-standard settings.
Public Health and Collaborative Research
• Develop and execute calculations for power and sample size when planning research studies with complex sampling schemes;
• Formulate and prep
are a written statistical plan for analysis of biomedical or public health research data that clearly reflects the
research hypotheses of the proposal in a manner appropriate for scientists with varied backgrounds; and
• Evaluate research reports and proposals for research funding on the basis of their scientific integrity, validity, and the strength of
the quantitative analysis.
Consulting
• Function as an effective consultant in biomedical and public health research projects
• Develop communication and writing skills in a consulting environment
Data Management
• Identify the uses to which data management can be put in practical statistical analysis, including the establishment of standards
for documentation, archiving, auditing, and confidentiality; guidelines for accessibility; security; structural issues; and data cleaning
• Differentiate between analytical and data management functions through knowledge of the role and functions
of databases, different types of data storage, and the advantages and limitations of rigorous database systems in conjunction with
statistical tools
• Describe the different types of database management systems, the ways these systems can provide data for analysis and interact
with statistical software, and methods for evaluating technologies pertinent to both
Teaching
• Explain and illustrate selected principles of study design, probability theory, inference, and data analytic techniques to public
health students enrolled in first and second level graduate public health courses
• Explain advanced concepts in the theory of statistical inference to graduate students in biostatistics and mathematical statistics
Biostatistical Research
• Identify and integrate new developments in the statistical literature for challenging research problems in biomedicine and public
health
• Generate efficient computer code to implement sophisticated statistical techniques
• Recognize gaps in current inferential methods that limit further public health research and propose and develop solutions based
on rigorous theoretical considerations
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biostatistics
Doctor of Public Health
Director: Shing Lee, PhD
The Doctor of Public Health degree in Biostatistics (DrPH) prepares candidates
to apply modern statistical methods to the solution of important public health problems as leaders
of multidisciplinary research teams. The degree program is administered by the Standing Doctoral
Committee of the Mailman School of Public Health, which carries out faculty policy on admission to
the doctoral program and upholds the criteria for granting the degree.
Course Requirements
The Doctor of Public Health degree calls for completion of an approved program of study totaling no less than 36
doctoral credits. Upon completion of 36 credits of coursework, a student is permitted to take the written qualifying
examination. In some instances it may be determined by the Department that a student needs more than 36 post-MPH
course credits before the qualifying examination.
DrPH students must maintain continuous registration every semester from the start of the program until deposit of the
doctoral dissertation. After completion of all coursework students register for Doctoral Registration (RSRHP0001) each
term until they are ready to graduate.
No more than 10 credits may be tutorials, and no more than six may be earned at 6000-level courses at the Mailman
School of Public Health or 4000-level courses at the Graduate School of Arts and Sciences; the Department may apply to
the Doctoral Committee for a variance on the six-credit rule on a case-by-case basis.
A list of required courses is given below. A DrPH student who has not previously earned an MPH degree must complete
courses in each of the core areas of public health: biostatistics, environmental health sciences, epidemiology, health
policy, and social and behavioral sciences. Core courses will have to be completed through the Columbia MPH Core or
completed by taking a comparable graduate level course at an accredited institution. These comparable courses must
be approved by the School’s Doctoral Committee. A DrPH student who has not previously earned an MPH will also be
required to take the online course, PUBH P6025-Introduction to Public Health. The credits accrued for completing the
core requirements DO NOT count towards the 36 doctoral credits.
A grade of B or better is necessary in all required courses. Up to 2 elective courses may be taken pass/fail.
Training in Interdisciplinary Research
The curriculum is designed to enable students to integrate their training in statistical methods and theory with the role
of biostatistical consultant/collaborator on interdisciplinary teams, which will comprise a major portion of their future
professional practice. Statistical Practices and Research for Interdisciplinary Sciences I & II are courses in which students
gain experience with design, data analysis, and both oral and written presentation communication through exposure to
several consulting projects. PhD students are required to enroll in P9185 prior to taking the Qualifying Exam, and to
enroll in P9186 after taking the Qualifying Exam. Students with extensive work experience in the field may request to
waive the P9186 requirement from the Director of the DrPH Program.
Degree Programs
CONTINUED
DrPH
program
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biostatistics
degree programs
DRPH
Required Courses
Points
3
3
3
3
3
3
3
3
P8104*
P8105*
P8106**
P8108**
P8109**
P8120**
P8130*
P8131*
P8157*
3
3
1.5
3
1.5
1.5
P9185
P9186
P9070
P9050
P9040
P9060
Probability
Data Science I
Data Science II
Survival Analysis
Statistical Inference
Analysis of Categorical Data
Biostatistical Methods I
Biostatistical Methods II
Analysis of Longitudinal Data
Statistical Practices and Research for Interdisciplinary Sciences I
Statistical Practices and Research for Interdisciplinary Sciences II
DrPH Case Studies in Public Health Leadership I & II
DrPH Seminar in Strategic Management
DrPH Seminar in Management and Organizational Behavior
Essentials of Teaching and Communication for Doctoral
1.5
Curriculum (TOTAL POINTS: 36 OR MORE)
Elective Courses
Points
3
3
3
3
3
3
Clinical Trial Methodology
Design of Medical Experiments
Statistical Methods for Causal Inference
Analysis of Health Surveys
Introduction to Randomized Clinical Trials
Latent Variable and Structural Equation Modeling for Health Sciences Topics
Topics in Advanced Statistical Computing 3
Relational Databases and SQL Programming for Research and Data Science
3
P8142
P8116
P8122
P8123
P8140
P8158
P8160
P8180
P9120
89260
Topics in Statistical Learning and Data Mining I
Building Interdisciplinary Research Models
2
*requirements can be waived, consult with your faculty advisor
Departmental Colloquium
All doctoral students are required to attend the Departmental Colloquium and Research Talks held weekly each
semester. Dates, times, and locations will be posted on the Department electronic board, as well as on the
Department’s website and Facebook page.
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biostatistics
3
The Applied Practice Experience (Practicum) for DrPH Students
Regardless of the amount or level of prior experience, all DrPH students are required to engage in an applied
practice experience in which students are responsible for completion of at least one project that is meaningful
for an organization and to advance public health practice.
The work product may be a single project or a set of related projects that demonstrate a depth of
competence. The deliverable must contain a reflective component that includes the student’s expression of
personal and/or professional reactions to the applied practice experience. This may take the form of
a manuscript, journal article or other written product, a professional portfolio, or another deliverable that
serves to assess the ability of the student to meet department and School competencies.
The applied practice experience should take place within an organization external to the student’s school or
program so that it is not merely an academic exercise, but application of learning to a “real world” setting.
Relevant organizations may include governmental, non-governmental, non-profit, industry and for-profit
settings. The Office of Field Practice and individual departments identify sites in a manner that is sensitive to
the needs of the agencies or organizations involved, and sites should benefit from Mailman students’
experiences. The applied practice experience may be completed within a student’s own work setting, as long
as the applied practice experience differs substantially from a student’s current job description and meets the
required competencies described below.
The applied practice experience must meet a minimum of five (5) foundational and/or concentration-specific
competencies that are reinforced and/or assessed through application. One of these competencies must be a
school-wide or a departmental-specific competency in leadership, management, and governance.
Competencies for the applied practical experience must be agreed upon by the student, advisor, and applied
learning experience preceptor, as specified in the statement of work form.
While there is not a minimum number of hours for the applied practice experience, it does require
substantive, quality opportunities that address the identified competencies.
Students must complete the practicum scope of work (SOW) form prior to starting a practicum experience.
The SOW, which is managed by the Mailman’s Office of Careers and Practice, is an important tool for planning
the practicum and meeting the School’s requirements for engaging in a structured practicum process.
degree programs
DRPH
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Qualifying Examination
There is a two-part qualifying examination for all DrPH candidates in Biostatistics that must be completed before going
on to the oral comprehensive examination.
Basic Inference. The first part assesses basic familiarity with statistical inference as presented in the course P8109
Statistical Inference. Students who have taken this course (or a comparable graduate course) and have received a
grade of B+ or above automatically satisfy the basic inference requirement. All others will be required to take a written
examination testing their knowledge of the material in this course. In all cases, students must fulfill the requirement
within two years of starting the doctoral program. Students must pass the basic inference requirement before they
may sit for the Applications exam.
Applications. The Applications portion covers the practical analysis of data. This part focuses on addressing applied
problems requiring statistical inference based on data analysis. The purpose of the Applications exam is to ensure that
the student is able to determine the appropriate statistical and analytic approaches needed to solve real world public
health / medical problems, correctly interpret the statistical results from these approaches, and translate and summarize
those findings into language that public health and medical professionals would find useful.
Course Work and Progressing toward the Applications portion. Preparation should include additional coursework in skills
classes, review and thorough understanding of the material in the suggested readings, group and individual study
sessions, completion of timed practice tests, and enrollment in P9185 Statistical Practices and Research for
Interdisciplinary Sciences I, a course in which students gain exposure to real world design, analysis, and report writing.
With approval and consent of his or her academic advisor
, the student should inform the Director of Academic Programs
two months in advance of sitting for the Applications portion of the exam.
Grading on the Applications exam. Grading is holistic, and can also take into account performance in coursework, and
other factors deemed relevant. A score below 66% on the exam would generally be considered unsatisfactory. The
student will be allowed no more than two attempts at passing either the Basic Inference or Applications parts of the
examination. The Applications portion must be taken and passed by the end of the third year in the DrPH program.
Questions from prior years are available to the student to assist in preparing for the examination.
degree programs
DRPH
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Reading List
The following list consists of textbooks that are used in the courses required for the DrPH degree, plus additional
references which are generally at the appropriate level for the DrPH Qualifying Examinations. Those marked with an
asterisk are highly recommended to students preparing for their examinations.
Breslow NE and Day NE, Statistical Methods in Cancer Research
Conover WJ, Practical Nonparametric Statistics
Cox DR and Oakes D, Analysis of Survival Data
Fleiss JL, The Design and Analysis of Clinical Experiments
* Fleiss JL, Levin B, and Paik MC, Statistical Methods for Rates and Proportions
Hogg RV and Craig AT, Introduction to Mathematical Statistics
* Hosmer D and Lemeshow S, Applied Logistic Regression
* Johnson RA and Wichern DW, Applied Multivariate Statistical Analysis
Kalbfleisch JD and Prentice RL, Statistical Analysis of Failure Time Data
Kleinbaum DG and Kupper LL, Applied Regression Analysis and other Multivariable Methods
* Lawless JF, Statistical Models and Methods for Lifetime Data
* Lee ET, Statistical Methods for Survival Data Analysis
Lehmann ER, Nonparametrics: Statistical Methods Based on Ranks
Mardia KV, Kent JT, and Bibby JM, Multivariate Analysis
* Mood AM, Graybill FA, and Boes D, Introduction to Statistical Inference
Morrison DF, Multivariate Statistical Methods
* Mosteller F and Tukey JW, Data Analysis and Regression
* Neter J, Wasserman W, and Kutner MH, Applied Linear Statistical Models
Rao CR, Linear Statistical Inference and Its Applications
Scheffe H, The Analysis of Variance
Searle SR, Linear Models
Snedecor GW and Cochran WG, Statistical Methods
Tukey JW, Exploratory Data Analysis
degree programs
DRPH
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
Oral Comprehensive
Examination
After completing all course work and passing the qualifying examination described above, the DrPH candidate begins
planning for the Integrative Learning Experience (ILE). The oral comprehensive examination for the DrPH in Biostatistics
is intended to examine the student’s mastery of the current state of knowledge about his or her project area, and thus
to indicate whether the student is prepared to undertake such a project. The Oral Comprehensive Examination should be
taken no later than six months after passing the qualifying exam.
Composition of the Examining Committee. The examining committee will consist of five members approved by the
chair of the Doctoral Program Subcommittee on Biostatistics, and will include:
i) three members who are inside examiners (i.e. holding a formal appointment or approved as a
dissertation sponsor);
ii) preferably two (but at least one) members who are outside examiners.
The latter faculty should represent disciplines closely related to the area of application of the student’s proposed
research. After the sponsor obtains consent from each member, the faculty sponsor submits the list of names to the
Chair of the Department and to the Chair of the Departmental Subcommittee on Biostatistics (DPSOB) for approval, who
then recommends the student’s committee to the DrPH Committee of the Mailman School of Public Health.
Scheduling the Exam. The oral comprehensive examination should be taken within one year of passing the qualifying
examination.
Nature of the Examination. After the committee selection and approval process has been completed, the student
submits in writing a description of the current state of knowledge about the proposed area of research. This submission
should be from 15 to 25 pages in length and contain between 15 and 20 references. This paper serves as the basis for
the oral comprehensive examination. The student must give each member of the Examining Committee this written
submission and discuss with each any additions or deletions that the committee member feels should be incorporated in
the write-up. Since the final written submission and the references therein will constitute the basic material upon which
the student will be examined, each member of the committee and the student must come to an agreement on the
scope of the submission. It should be neither too narrow nor too broad in scope. After all members of the ad hoc
committee approve the submission, the examination is scheduled within the next 60 days. The written submission
may contain original results by the student, but this is not required.
degree programs
DRPH
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ORAL COMPREHENSIVE EXAMINATIONN CONTINUED
Format of the Exam. The actual examination shall be an Oral Comprehensive Examination conducted by the
Examining Committee as follows:
1. The chair of the Examining Committee will not be the ILE advisor but another member of the ad hoc committee.
2. The examination will run approximately two hours and will consist of an oral presentation of the content of the
written submission by the student (a planned presentation of about 30 minutes is appropriate), which may be
interrupted by members of the Examining Committee with appropriate questions on the material presented or
relevant related material. The chair of the Examining Committee may challenge any question felt to be unrelated
to the written submission and its background material.
3. After the presentation and questions, each member may ask additional questions of the examinee. Any such
questions should be within the broad content of the written submission and its references. Again, the Examining
Committee chair may challenge any question felt to be too far removed from the basic material upon which the
examination is based, namely on the written submission and the references therein.
4. After all questions are completed, the examinee leaves the room and the committee then votes
on whether or not the examinee passed the examination. All members must agree in order for the student to
pass the examination. Instead of pass or fail, the committee may unanimously decide upon the option of
retesting the student within a six-month period on the same written submission.
The committee’s decision will be put into writing by the chair of the Examining Committee, as well as brief
comments on the strengths and weaknesses of the student’s performance as deemed necessary. Copies of this
statement will be sent to the student and placed in the student’s file.
Second Attempt at Passing. The student is entitled to no more than two attempts at passing the Oral
Comprehensive Examination. The second attempt need not be based on the same written submission nor be
examined by the same committee, but the same rules will govern the second attempt, including approval by the
committee of the written submission. The second attempt must be made no more than 6 months after the first
attempt.
Upon passing the Oral Comprehensive =Examination, a student will typically ask his sponsor or another member of
the faculty to agree to serve as the student’s sponsor. No formal approval of an ILE topic is required; however, a
suitable and mutually agreeable topic must be established by the student and advisor. As stated earlier, it is often
the case that the Oral Comprehensive Examination is on a topic related to the student’s ILE, although this is not a
formal requirement.
degree programs
DRPH
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Progressing toward the Integrated Learning Experience Defense
Between the Oral Exam and the ILE Defense, the DrPH student is required to present his/her proJect in two public
settings. The first is the Doctoral Research Seminar, usually held in the spring, where doctoral students present their
work to the faculty and their peers. The second setting is the preparation and presentation of a paper (or poster) at a
conference of professional societies or at a statistics or biostatistics departmental talk for job interviews. A select, but
not exhaustive, list of such societies is presented below. More information is available on the Doctoral Bulletin Board.
Travel funds are often available.
Example of Professional Societies / Associations:
• American Statistical Association (ASA)
• American Public Health Association (APHA)
• International Biometric Society (ENAR/IBS)
• Joint Statistical Meetings (JSM)
• Society for Clinical Trials (SCT)
TheI ntegrated Learning Experience (ILE)
Once a DrPH student has advanced to doctoral candidacy, s/he begins to develop a proposal for the ILE proJect. The
topic must deal with an important problem or issue in public health which can be addressed by the sound and original
application of existing statistical methods. It must demonstrate that the candidate has engaged in independent and
original research that has advanced our understanding of or knowledge about the public health problem, though the
methods themselves need not be original. After the project is successfully defended, the doctoral degree is awarded by
the Mailman School of Public Health in the Faculty of Medicine.
In most cases, completion of DrPH course work and written qualifiers should take no more than two full-time academic
years. On average, the ILE may take an additional two or three full-time academic years. An overall time limit of seven
years is set from the date of first registration as a doctoral student.
In unusual instances a student may wish to change ILE sponsors, for instance, if the student’s project requires different
areas of expertise than originally anticipated. In such cases the student may seek approval from a new faculty sponsor.
The candidate must inform the Department Chair and the previous sponsor that the new sponsor will assume the
previous sponsor’s duties. At this point the student may also decide to pursue a new project topic, with approval of the
new sponsor, but in all cases the rules governing time limits and extensions remain in force.
DrPH candidates are required to submit an electronic copy of their final report to the department. Copies of past reports
are available from the Director of Academic Programs.
degree programs
DRPH
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2022-2023 STUDENT HANDBOOK
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biostatistics
Some Past DrPH ILE Titles
The titles below are provided to give students some idea of ILE topics which in past years have proved
appropriate for the DrPH degree:
Analysis Approaches for Wearable Device Data, Patrick Hilden (2021)
Statistical Methods for Healthcare Cost Data: An Application to Administrative Claims Data for Pediatric Patients
with Acute Lymphoblastic Leukemia, Elisabetta Malangone Monaco (2021)
Clustering Algorithm for Zero-Inflated Data, Anusorn Thanataveerat (2020)
Statistical Issues in Platform Trials with a Shared Control Group, Jessica Overbey (2019)
Bayesian Modeling of Latent Heterogeneity in Complex Survey Data and Electronic Health Records, Rebecca
Anthopolos (2019)
Statistical Methods for the Study of Etiologic Heterogeneity, EEmily Zabor (2019)
Statistical Methods for Integrated Cancer Genomic Data Using a Joint Latent Variable Model, Esther Drill (2018)
Bayesian Modeling for Mental Health Surveys, Sharifa Williams (2018)
Data-Driven Methods for Identifying and Validating Shorter Symptom Criteria Sets: The Case for DMS-5 Substance
Use Disorders, Cheryl Raffo (2018)
Design and Analysis of Sequential Multiple Assignment Randomized Trial for Comparing Multiple Adaptive
Interventions, Xiaobo Zhong (2018)
Prognostic Modeling in the Presence of Competing Risks: An Application to Cardiovascular and Cancer Mortality in
Breast Cancer Survivors, Nicole Leoce (2016)
New Estimating Equation Approach for the Secondary Trait Analyses in Genetic Case-Control Studies, Xiaoyu Song
(2015)
Identifying Patterns in Behavioral Public Health Data Using Mixture Modeling with an Informative Number of
Repeated Measures, Gary Yu (2012)
A Life Expectancy-based Comprehensive Quantification of Structural-level Health Disparities, Emma Benn (2012)
degree programs
DRPH
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2022-2023 STUDENT HANDBOOK
the department of
biostatistics
Doctor of Philosophy
Director: Jeff Goldsmith, PhD
The Doctor of Philosophy in Biostatistics (PhD) prepares candidates for leadership roles in
the development and application of statistical methods to biomedical research for the advancement
of public health. The PhD is awarded by the Graduate School of Arts and Sciences (GSAS) as
governed by the Doctoral Program Subcommittee on Biostatistics. The program is administered by
the faculty and staff of the Mailman School of Public Health.
Course Requirements
Students take courses in the department of biostatistics, and other academic units representing various fields of
application and/or related background material. A student should plan his or her course work in consultation with his
or her academic advisor and/or the PhD subcommittee chair. Students wishing to waive one or more required courses
must request approval in writing from their faculty advisor and the Director of Academic Programs.
A grade of B or better is necessary in all required courses, except for P9111 which requires a B+ or better. Electives
may be taken pass/fail, in order to encourage candidates to take courses outside his or her field of experience.
In advance of beginning the PhD program, any student who has not completed an MPH will be required to take the
online course required by the Mailman School: PUBH P6025-Introduction to Public Health.
Training in Interdesciplinary Research
The curriculum is designed to enable students to integrate training in statistical methods and theory with the role of
biostatistical collaborator on interdisciplinary teams, which will comprise a major portion of their future professional
practice. Statistical Practices and Research for Interdisciplinary Sciences I & II are courses in which students gain
experience with design, data analysis, and both oral and written communication through exposure to several consulting
projects. PhD students are required to enroll in P9185 during the spring semester prior to taking the Qualifying Exam, and
to enroll in P9186 during the fall semester after taking the Qualifying Exam.
Statistical Inference Problem Seminar
To prepare for the written component of the Qualifying Exam, students are required to take the problem seminar in
which students work on problems and discuss problem solving strategy useful for theoretical questions. The problem
seminar is held in the months prior to the written portion of the Qualifying Exam.
GSAS Requirements
In addition to registering for individual courses, PhD students are required to register for the Residence Unit (RU) which
provides the basis for tuition charges and provides full-time status. Six RUs are required for the PhD degree. RUs may only
be earned during fall and spring semesters, not during the summer. PhD students must register for 1 RU each semester
up to the total required 6 RUs. After one year of study, students who enter with a Master’s degree may apply for
advanced standing of two residence units representing work completed in their Master’s program. After the student has
satisfied the residency requirement they must register for full-time Matriculation & Facilities (M&F) status until a
successful dissertation defense.
Degree Programs
CONTINUED
PhD
program
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2022-2023 STUDENT HANDBOOK
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biostatistics
degree programs
PHD
Required Courses
Points
3
3
3
3
3
3
3
P6400*
P8104*
P8105*
P8106*
P8109*
P8130*
P8131*
P8160**
3
3
3
3
3
3
3
3
P9104
P9109
P9110
P9111
P9120
P9130
P9185
P9186
Principles of Epidemiology
Probability
Data Science I
Data Science II
Statistical Inference
Biostatistical Methods I
Biostatistical Methods II
Topics in Advanced Statistical Computing
Probability for Biostatisticians
Theory of Statistical Inference I
Theory of Statistical Inference II
Asymptotic Statistics
Topics in Statistical Learning and Data Mining I
Advanced Biostatistical Methods I
Statistical Practices and Research for Interdisciplinary Sciences I
Statistical Practices and Research for Interdisciplinary Sciences II 1.5
Curriculum
Elective Courses
Points
3
3
3
3
3
3
3
3
P8108
P8116
P8122
P81233
P8133
P8124
P8140
P8142
P8144
P81577
Survival Analysis
Design of Medical Experiments
Statistical Methods for Causal Inference
Analysis of Health Surveys
Bayesian Analysis and Adaptive Designs in Clinical Trials
Graphical Models for Complex Health Data
Introduction to Randomized Clinical Trials
Clinical Trial Methodology
Pharmaceutical Statistics
Analysis of Longitudinal Data
3
*requirements can be waived if taken a comparable course at the Master’s level; consult with your faculty advisor
Departmental Colloquium
All doctoral students are required to attend the Departmental Colloquium and Research Talks held weekly each
semester. Dates, times, and locations will be posted on the Department electronic board, as well as on the
Department’s website and Facebook page.
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3
Qualifying Examination
There is a two-part qualifying examination for all PhD candidates in Biostatistics that must be completed prior to the oral
comprehensive examination. The written and take home portions of the exam are to be taken during the same summer
semester.
Written Portion - Theory and Methods. The written or theory and method exam draws from material presented in the
following MS and doctoral level courses: P8104, P8109,P8130, P8131, P9104, P9109, P9110, and P9130. The purpose of
the written exam is to ensure that the PhD student is able to fully understand and use the mathematical and theoretical
tools that form the basis of doctoral level biostatistical research. The exam requires solutions to five questions. Students
entering with a Bachelor’s are expected to take the exam after their second year; students entering with a relevant
Master’s are expected to take the exam after their first year.
Course Work and Progressing toward the written portion of the Qualifying Exam. Preparation should include coursework
or mastery of content of the material in the required courses, review and thorough understanding of the material in
the suggested readings, group and individual study sessions, and completion of timed practice tests. With approval and
consent of the student’s academic advisor, the student should inform the Director of Academic Programs two months in
advance of sitting for the written portion of the Qualifying Exam.
Take Home Portion - Applications. The take-home exam covers the practical analysis of data. The examination
focuses on applied problems requiring statistical inference based on data analysis, with particular emphasis on material
from P8105, P8106, P8130, P8131, P9130 and P9185.
The purpose of the take-home exam is to ensure that the student is able to determine the appropriate statistical and
analytic approaches needed to solve real world public health / medical problems, correctly interpret the statistical
results from these approaches, and translate and summarize those findings into language that public health and medical
professionals would find useful. The take-home exam is administered over a two-day period. Students are encouraged
to use personal laptops and any familiar software. Students entering with a bachelors are expected to take the exam
after their second year; students entering with a relevant Master’s are expected to take the exam the summer after
their first year.
Course Work and Progressing toward the take home portion of the Qualifying Exam. Preparation should include
additional coursework in skills classes, review and thorough understanding of the material in the suggested readings,
group and individual study sessions, completion of timed practice tests, as well as enrollment in P9185. With approval
and consent of his or her academic advisor, the student should inform the Director of Academic Programs two months
in advance of sitting for the take-home exam.
Grading on the Qualifying Exam. Grading is holistic, taking into account performance in coursework, on both portions,
and other factors deemed relevant. A score below 65% on either the written or take home portion will generally be
considered unsatisfactory. The student will be allowed no more than two attempts at passing either part of the exam. It
is strongly recommended that the second attempt be made at the time of the next exam offering.
Exam questions from prior years are available to the student to assist in preparing for the examination.
degree programs
PHD
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degree programs
PHD
Reading List
The following list consists of textbooks that are generally appropriate to use for preparing for the PhD qualifying
examination.
Agresti A, Categorical Data Analysis
Bickel PJ and Doksum KA, Mathematical Statistics
Casella G and Berger RL, Statistical Inference
Cox D and Hinkley DV, Theoretical Statistics
Efron B and Tibshirani R, An Introduction to the Bootstrap
Hastie T, Tibshirani R, and Friedman J, The Elements of Statistical Learning Hettmansperger TP and
McKean JW, Robust Nonparametric Methods
Hollander M, Nonparametric Statistical Methods
Johnson RA and Wichern DW, Applied Multivariate Statistical Analysis
Klein JP and Moeschberger ML, Survival Analysis
Lehmann EL, Point Estimation
Lehmann EL, Testing Statistical Hypotheses Lehmann EL, Elements of Large-Sample Theory
McCullagh P and Nelder JA, Generalized Linear Models
Rao CR, Linear Statistical Inference and Its Applications
Robert CP and Casella, G, Monte Carlo Statistical Methods
Ruppert D, Wand MP, and Carroll R, Semiparametric Regression
Shao J, Mathematical Statistics
Wickham, H and Grolemund, G, R for Data Science
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Oral Comprehensive Examination
After completing all course work and passing the two-part qualifying examination described in the previous sections, the
PhD candidate begins planning for dissertation research and preparing for the Oral Exam. The Oral Comprehensive
Examination is intended to demonstrate the student’s mastery of the material in a defined statistical content area by
verbally presenting a thorough description of the state of the art in that area, identifying limitations or areas of
incomplete knowledge in that area, and proposing the development of new methods that would advance that area. This
topic area may or may not end up being the student’s dissertation topic. The Oral Comprehensive Examination should be
taken no later than six months after passing the two-part qualifying exams. Fellows in the program, please note all tuition
expenses incurred as a result of any delay in scheduling this exam shall be the responsibility of the student and not the
Department of Biostatistics.
Composition of the Examining Committee. The examining committee will consist of five members approved by the
chair of the Doctoral Program Subcommittee on Biostatistics, and will include:
i) three members who are inside examiners (i.e. holding a formal appointment or approved as a
dissertation sponsor);
ii) preferably two (but at least one) members who are outside examiners.
The chair of the Examining Committee will be a member of the Doctoral Program Subcommittee On Biostatistics. One
member of this committee should be the faculty member who acts as the student’s sponsor and anticipated thesis
advisor. With the consent of the members of the proposed committee, the faculty sponsor then submits their names for
approval by the Chair of the Doctoral Program Subcommittee on Biostatistics.
Nature of the Examination. After the committee selection and approval process has been completed, the student
submits in writing a description of the current state of knowledge about the proposed area of research. This submission
should be from 15 to 25 pages in length and contain between 15 and 20 references. This paper serves as the basis for
the oral comprehensive examination. The student must give each member of the committee this written submission and
discuss with each any additions or deletions that the committee member feels should be incorporated in the write-up.
Since the final written submission and the references therein will constitute the basic material upon which the student
will be examined, each member of the committee and the student must come to an agreement on the scope of the
submission and references. After such modifications to the written submission have been approved by all four members
of the Examining Committee, the Comprehensive Exam is scheduled within the next 30 days. The written submission
may contain original research by the student, but need not be original in content. It should not be too narrow in scope
and should reflect the necessary basic material relevant to the student’s chosen area of research. Before and during the
examination, the three faculty examiners other than the student’s sponsor make suggestions for and may insist on
changes in the student’s perception of the topic. Part of the student’s written submission is an enumeration of as yet
unanswered questions. The examiners make their opinions plain as to how important and challenging they perceive
these questions to be.
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PHD
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PHD
ORAL COMPREHENSIVE EXAMINATION CONTINUED
Format of the Exam. The actual examination shall be an Oral Comprehensive Examination conducted by the
Examining Committee as follows:
1. The chair of the Examining Committee will not be the dissertation advisor but some other member of the ad
hoc committee.
2. The examination will run approximately two hours and will consist of an oral presentation of the content of
the written submission by the student (a planned presentation of about 30 minutes is appropriate), which
may be interrupted by members of the Examining Committee with appropriate questions on the material
presented or related material. The chair of the Examining Committee may overrule any question felt to be
unfair or unrelated to the written submission and its background material.
3. After the presentation and questions, each member may ask additional questions of the examinee. Such
questions should be within the broad scope of the written submission and references. Again, the Examining
Committee chair may rule against any questions felt to be too far removed from the basic material upon
which the examination is based, that is, the written submission and the references therein.
4. After all questions are completed, the examinee leaves the room and the committee then votes on whether
or not the examinee passed the exam. Three of the four members must vote to pass the student in order for
the student to pass the exam.
The committee’s decision will be put into writing by the chair of the Examining Committee, as well as brief comments on
the strengths and weaknesses of the student’s performance as deemed necessary. Copies of this statement will be sent
to the student and placed in the student’s file.
Second Attempt at Passing. The student is entitled to no more than two attempts at passing the Oral Comprehensive
Examination. The second attempt need not be based on the same written submission nor be examined by the same
committee, but the same rules will govern the second attempt, including approval by the committee of the written
submission. The second attempt must be made no more than 6 months after the first attempt.
The examination and written submission are designed to focus the examination on basic material which is important to
the student’s area of research, and allow the Examining Committee to judge that the student fully comprehends this
material. Upon passing the Comprehensive Examination, a student will typically ask his sponsor or another member of
the PhD subcommittee to serve as the student’s dissertation advisor and sponsor. No formal approval of a dissertation
topic is required; however, a suitable and mutually agreeable topic must be established by the student and advisor.
While it is usually the case that the Oral Comprehensive Examination is on a topic that will become the student’s
dissertation topic, this is not a formal requirement.
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Advancement of PhD students to Master of Philosophy Degree
Upon the student’s passing the qualifying and oral comprehensive examinations and the successful completion of six
residence units beyond the Master’s degree (two residence units awarded for a completed Master’s degree), he or she is
awarded the Master of Philosophy degree. Failure on the Oral Comprehensive Examination implies that it is the
Subcommittee’s Judgment the student is not yet prepared to carry out original research. The awarding of the Master of
Philosophy to a student, on the other hand, certifies that the student has mastered the fundamental material necessary
for him or her to conduct research in biostatistics. Students who apply for and receive two residence units of advanced
standing are still required to complete four additional residence units before the Master of Philosophy may be awarded.
Progressing toward Dissertation Defense
Between the Oral Exam and the Dissertation Defense, the PhD student is required to present his or her research in a
two public settings. Typically, one of these settings is at a Graduate Research Seminar, where doctoral students at
various stages of their research present their work to their peers. A second setting is the preparation and presentation of a
paper (or poster) at a conference of professional societies.
A select, but not exhaustive, list of such societies is presented below. Students who are selected to present at a conference
can apply for travel funds at the School and department levels. Information requests about available travel funds should be
directed to the Director of Academic Programs.
Example of Professional Societies / Associations:
• American Statistical Association (ASA)
• American Public Health Association (APHA)
• International Biometric Society (ENAR/IBS)
• Joint Statistical Meetings (JSM)
• Society for Clinical Trials (SCT)
Dissertation
The PhD dissertation is expected to contain original results in statistical theory and methods in the solution of a problem
which has relevance to a biomedical application. As a rule, the content of the dissertation should be adequate for
publication in peer-refereed journals in the topic area of the dissertation. Students begin work on their dissertation
research with the approval of their thesis sponsor and comprehensive examination committee. The only time limitation
is the Graduate School of Arts and Sciences maximum of seven years from the time of enrollment in the doctoral
program (the maximum is six years for those receiving advanced standing). Candidates who are making satisfactory
progress toward finishing the dissertation have, upon application, been granted extensions by the Dean of GSAS, with
the approval of their sponsor. With proper advising, PhD students should be able to finish the degree within five years of
entry into the PhD program.
PhD candidates are required to submit an electronic copy of their final dissertation to the department. Electronic copies
of past dissertations are available from the Director of Academic Programs.
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PHD
DISSERTATION CONTINUED
In some cases a student may wish to change dissertation sponsors – for instance, if the student’s research leads to
different areas of expertise than originally anticipated. In such cases, the student may seek approval from a new faculty
sponsor. The candidate must inform the Doctoral Program Subcommittee Chair and the previous sponsor that the new
sponsor will assume the previous sponsor’s duties. At this point, the student may also decide to pursue a new
dissertation topic, with approval of the new sponsor, but in all cases the rules governing time limits and extensions still
apply. Upon completion of the dissertation, and with approval of the candidate’s dissertation committee, the
dissertation defense is scheduled.
PhD candidates are required to submit an electronic copy of their final dissertation to the department.
For more details regarding the PhD dissertation, the student is referred to the Dissertation Office website:
www.gsas.columbia.edu/dissertations. The GSAS Dissertation Office is located on the Columbia Morningside Heights
campus at 107 Low Memorial Library, 535 W. 116th Street, New York, NY 100277. Information is also available in the
Department of Biostatistics and the Dean’s Office of GSAS on Morningside Campus.
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Some Past PhD Dissertation Titles
The titles below are provided to give students some idea of topics that in past years have proved
appropriate for the PhD degree:
Statistical Methods for Learning Patients Heterogeneity and Treatment Effects to Achieve Precision Medicine, Tianchen Xu (2022)
The Joint Modeling of Longitudinal Covariates and Censored Quantile Regression for Health Applications, Bo Hu (2022)
Statistical Analysis of Large Scale Data with Perturbation Subsampling, Yujing Yao (2022)
Statistical methods for modeling progression and learning mechanisms of neuropsychiatric disorders, Qinxia Wang (2021)
Bayesian modeling in personalized medicine with applications to N-of-1 trials, Ziwei Liao (2021)
Dynamic graphical models and curve registration for high-dimensional time course data, Erin McDonnell (2021)
Statistical and machine learning methods for precision medicine, Yuan Chen (2021)
Topics in Bayesian design and analysis for sampling, Yutao Liu (2021)
GGQ-learning for indefinite horizon problem with L1 penalty, Xiaoqi Lu (2021)
Optimal Treatment Regimes for Personalized Medicine and Mobile Health, Eun Jeong Oh (2020)
Statistical Learning Methods for Depression Screening and Intervention, and Structured Missing Imputation, Huichen Zhu (2019)
Quantile regression for zero-inflated outcomes, Wodan Ling (2019))
Functional Data Analytics for Wearable Device and Neuroscience Data, JJulia Wrobel (2019)
Statistical Methods for Constructing Heterogenous Biomarker Networks, Shanghong Xie (2019)
Machine Learning Methods in Personalized Medicine Using Electronic Health Records, Peng Wu (2019)
Statistical Methods for Genetic Studies with Family History of Diseases, Annie Lee (2019)
Varying-Coefficient Models and Functional Data Analysis for Dynamic Network and Wearable Device Data, Jihui Lee (2018)
Statistical Methods for Modeling Biomarkers of Neuropsychiatric Disease, Ming Sun (2018)
Flexible Regression Models for Estimating Interaction between a Treatment and Scalar/Functional Predictors, Hyung Park (2018)
Statistical Learning Methods for Personalized Medicine, Xin Qiu (2018)
Methods in functional data analysis and functional genomics, Daniel Backenroth (2018)
Marginal screening on survival data, Tzu-Jung Huang (2017)
Methods for functional regression and nonlinear mixed-effect models with applications to PET data, Yakuan Chen (2017)
Statistical Learning Methods for Personalized Medical Decision Making, Ying Liu (2016)
Survival Analysis using Bivariate Archimedean Copulas, Krishnendu Chandra (2015)
Learning Logic Rules for Disease Classification: With an Application to Developing Criteria Sets for the Diagnostic and Statistical
Manual of Mental Disorders, Christine Mauro (2015)
Empirical likelihood tests for stochastic ordering based on censored and biased data, Hsin-wen Chang (2015)
Sequential Designs for Individualized Dosing in Phase I Cancer Clinical Trials, Xuezhou Mao (2015)
Methods for Handling Measurement Error and Sources of Variation in Functional Data Models, Xiaochen Cai (2015)
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PHD
Fall I Spring I Summer I Fall II Spring II
Submit paper
for poster
presentation
at
professional
society
DISSERTATION
DEFENSE
P9104
Probability for
Biostatisticians
QUALIFYING
EXAMS
Written/
Take-home
P9111
Asymptotics
Formalize
Research
Topic
P8160
Topics in Advanced
Statistical
Computing
P9120
Topics in
Statistical
Learning & Data
Mining I
Present
research
topic at
Doctoral
Seminar
P9109
Theory of
Statistical
Inference I
P9110
Theory of Statistical
Inference II
P9130
Advanced
Methods I
Electives
P9185
SPRIS I
Electives
P9186
SPRIS II
Electives
Prepare for
Oral Exam
Statistical Inference Problem Seminar
Attend all Departmental Colloquium and Research Talks
Typical PhD Timeline (PhD student entering with a relevant Masters)
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PHD
Typical PhD Timeline (PhD student entering without a relevant Masters)
Fall I Spring I Summer I Fall II Spring II Summer II Fall III Spring III
P6400
Principles of
Epidemiology
P8109
Statistical
Inference
Review
material;
begin
research
projects
P9104
Probability for
Biostatisticians
QUALIFYING
EXAMS
(Written/
Take-home)
P9111
Asymptotics
Formalize
Research
Topic
P8160
Topics in
Advanced
Statistical
Computing
P9120
Topics in
Statistical
Learning &
Data Mining I
P8104
Probability
P8131
Biostatistical
Methods II
P9109
Theory of
Statistical
Inference I
P9110
Theory of
Statistical
Inference II
P8105
Data
Science I
P8106
Data
Science II
Elective
P9130
Advanced
Methods I
P9185
SPRIS I
P9186
SPRIS II
Electives
Prepare for
Oral Exam
P8130
Biostatistical
Methods I
Statistical Inference Problem
Seminar
Attend all Departmental Colloquium and Research Talks
Submit paper
for
presentation
poster at a
professional
society
DISSERTATION
DEFENSE
Present research
topic at Doctoral
Seminar
**Attend all department colloquia and
research talks**
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Electives
1) Successful completion of the CSRI, a 10-credit summer program consisting of five required courses:
• P6104 Introduction to Biostatistical Methods
• P6400 Principles of Epidemiology
• P8182 Writing a Successful NIH Grant
• P8568 Decision Analysis for Public Health and Clinical Practices
• P8750 Race and Health
2) Successful completion of a 2-credit Master’s Essay course - select one:
• P9160 Master’s Essay (original research article) - Spring
• P9165 Master’s Essay (full grant proposal) - Fall
Key Deliverables
• NIH-style grant outline for either an R01 or K award (all)
• Full grant proposal for an R01 or K award (P9165)
• Original research article submitted to a peer-reviewed journal (P9160)
• Critical thinking skills
• Foundational analytic toolbox
• The application process for the CPA-CTR is the same as the process for CSRI Bootcamp. CSRI alums who wish
to earn the CPA-CTR should contact [email protected] for next steps.
Certificate Program
Certification of Professional Achievement in Clinical and Translational
Research (CPA-CTR) provides effective training in core methods used in clinical and translational research in a
condensed format suited to working clinicians and post-doctoral researchers. The 12-credit program may be
completed in as little as six months; ten credits are earned over 5-weeks in the summer through the Columbia
Summer Research Institute (CSRI), and the remaining two credits are earned in a subsequent Fall or Spring semester.
In this format, working professionals are able to receive graduate level training in research methods while
simultaneously working with a multi-disciplinary mentoring team to develop a research project.
The Certification of Professional Achievement in Clinical and Translational Research curriculum is comprised of intensive
instruction in biostatistics and epidemiology, followed by more specialized coursework, and culminates in a full NIH-
style grant proposal or an original paper to be submitted to a peer-reviewed journal. The selected coursework and key
deliverables emphasize quantitative training, critical thinking skills, and practical strategies in order for jjunior
investigators to be competitive in the quest for independent grant funding. CPA-CTR requirements must be completed
within two years of enrollment in CSRI.
Program Requirements
Other program
Certificate
Program
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certification program
CPA-CTR
Application to:
Columbia Summer
Research Institute (CSRI)
Degree Program:
MS/CRM or
MS-POR
10 credits
earned through successful completion of CSRI Bootcamp
Application to CPA-CTR Application to MS program
Certification of Professional
Achievement
in Clinical and Translational Research
(CPA-CTR)
2 credit
Master’s Essay
20 credits
additional coursework
†
Conferral of
CPA-CTR
Application to MS program
Conferral of
MS/CRM or MS-POR
Research Training Options
up to 6 credits
additional coursework through CSRI
Summer 2
† A portion of the additional coursework for the MS/
CRM and the MS-POR degree programs may be
taken through CSRI Summer 2 which offers a
selection of 1.5 to 3-credit courses that run from 1
week to 6 weeks in length.
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Department of Biostatistics Courses
These are the courses offered by the Department of Biostatistics. Due to faculty
commitments, the frequency of the courses changes from time to time. Students are advised
to check the current schedule of courses listed on the MSPH web page:
www.mailman.columbia.edu/academics/courses. Students may also review the course
offerings at the University: columbia.edu/cu/bulletin/uwb. Students are encouraged to meet
with their faculty advisors at least twice a year (in the fall and in the spring). Permission is
not required for approved courses in a student’s approved program of study. Students must
first obtain permission from their faculty advisors to take courses outside the approved
program. Failure to comply with these guidelines may jeopardize plans for graduation.
P6103 Introduction to Biostatistics 3 points
Prerequisites: Permission of the instructor required for all non-Public Health students.
Biostatistics is essential to ensuring that findings and practices in public health and biomedicine are supported
by reliable evidence. This course covers the basic tools for the collection, analysis, and presentation of data in
all areas of public health. Central to these skills is assessing the impact of chance and variability on the
interpretation of research findings and subsequent recommendations for public health practice and policy.
Topics covered include: general principles of study design; hypothesis testing; review of methods for
comparison of discrete and continuous data including ANOVA, t-test, correlation, and regression.
P6104 Introduction to Biostatistical Methods 3 points
Prerequisites: Instructor’s permission for non-Biostatistics students
An enriched core course for students concentrating in biostatistics and others who expect to take additional
courses in biostatistics beyond the two main second-level courses (P8100 and P8120). It covers in greater
depth all of the topics in P6103 and is the best preparation for students anticipating a quantitative orientation
in their degree programs. Topics covered include standard distributions, measures of central tendency and
dispersion, hypothesis testing, point estimation, confidence intervals, and an introduction to correlation and
regression.
P6110 Statistical Computing with SAS 3 points
Prerequisites: P6104, P8130 or MPH Quantitative Foundations core course
A logical follow-up course to an introductory biostatistics course. Covers uses of the computer in cleaning,
summarizing, and cross-classifying data. Enhancement of the material covered in P6104— including regression,
correlation, and contingency table analysis, and the analysis of variance–with data analysis carried out using
SAS software.
P6170 New Drug Development: A Regulatory Overview 3 points
Prerequisite: P6104, P8130 or MPH Quantitative Foundations core course and P6400
Provides our CTSA fellows and scholars with insights into and understanding of the process of patient oriented/
translational research and gives them an opportunity to meet active investigators from academia and industry,
and learn about some career enhancing resources available at CUMC. Active researchers from various clinical
disciplines and public health are invited to speak on research techniques, design, and laboratory methodology
as applied to current studies. They present their experiences in conducting patient orientated research on the
Health Sciences campus and elsewhere. Also features speakers from both the pharmaceutical and biotech
industries who discuss drug development, and preclinical and clinical trials. Other lectures deal with FDA
regulations, patent law, and the Institutional Review Board and ways to effectively build and succeed in a
clinical/translational academic career.
DESCRIPTIONS
course
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P8100 Applied Regression I 3 points
Prerequisites: P6104 or MPH Quantitative Foundations core course. (Not open to MS/TM, PHDS, PS, or SG tracks) This
course will provide an introduction to the basics of regression analysis. The class will proceed systematically from the
examination of the distributional qualities of the measures of interest, to assessing the appropriateness of the
assumption of linearity, to issues related to variable inclusion, model fit, interpretation, and regression diagnostics.
P8101 Introduction to Health Data Science 3 points
Prerequisites: P6104, P8130 or MPH Quantitative Foundations
This course will introduce students to core data science skills and concepts through the exploration of applied
biostatistics. The course will begin with an introduction to the R programming language and the RStudio IDE, focusing
on contemporary tidyverse functions and reproducible programming methods. Then, the course will instruct students
in contemporary data manipulation and visualization tools while systematically covering core applied biostatistics
topics, including confidence intervals, hypothesis testing, permutation tests, and logistic and linear regression. Finally,
the semester will end with an introduction to machine learning concepts, including terminology, best practices in test/
training sets, cross-validation, and a survey of contemporary classification and regression algorithms.
P8103 Colloquium on Patient Oriented Research 2 points (0.5 points x 4 semesters)
Prerequisite: MS-POR students only
Provides our CTSA fellows and scholars with insights into and understanding of the process of patient oriented/
translational research and gives them an opportunity to meet active investigators from academia and industry, and
learn about some career enhancing resources available at CUMC. Active researchers from various clinical disciplines
and public health are invited to speak on research techniques, design, and laboratory methodology as applied to
current studies. They present their experiences in conducting patient orientated research on the Health Sciences
campus and elsewhere. Also features speakers from both the pharmaceutical and biotech industries who discuss drug
development, and preclinical and clinical trials. Other lectures deal with FDA regulations, patent law, and the
Institutional Review Board and ways to effectively build and succeed in a clinical/translational academic career.
P8104 Probability 3 points
Prerequisites: P6104 or P8130 (may be corequisite), working knowledge of calculus
Topics include: Fundamentals, random variables, and distribution functions in one or more dimensions; moments,
conditional probabilities, and densities; Laplace transforms and characteristic functions. Infinite sequences of random
variables, weak and strong large numbers; central limit theorem.
P8105 Data Science I 3 points
Prerequisites: Experience in R programming (or programming in another language) and data analysis is recommended
Contemporary biostatistics and data analysis depends on the mastery of tools for computation, visualization,
dissemination, and reproducibility in addition to proficiency in traditional statistical techniques. The goal of this course
is to provide training in the elements of a complete pipeline for data analysis.
P8106 Data Science II 3 points
Prerequisites: P8105
With the explosion of “Big Data” problems, statistical learning has become a very hot field in many scientific areas.
The goal of this course is to provide the training in practical statistical learning. It is targeted to MS students with some
data analysis experience.
DESCRIPTIONS
course
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P8107 Introduction to Mathematical Statistics 3 points
Prerequisities: MPH Quantitative foundations or P6104 (Not open to MS/TM, PHDS, or SG tracks)
The first portion of this course provides an introductory-level mathematical treatment of the fundamental principles
of probability theory, providing the foundations for statistical inference. Students will learn how to apply these
principles to solve a range of applications. The second portion of this course provides a mathematical treatment of
(a) point estimation, including evaluation of estimators and methods of estimation; (b) interval estimation; and (c)
hypothesis testing, including power calculations and likelihood ratio testing.
P8108 Survival Analysis 3 points
Prerequisites: P8104, P8109, and P8130
This course focuses on methods for the analysis of survival data, or time-to-event data. Survival analysis is a method
for survival data or failure (death) time data, that is time-to-event data, which arises in a number of applied fields,
such as medicine, biology, public health, epidemiology, engineering, economics, and demography. A special course
of difficulty in the analysis of survival data is the possibility that some individual may not be observed for the full time
to failure. Instead of knowing the failure time t, all we know about these individuals is that their time-to-failure
exceeds some value y where y is the follow-up time of these individuals in the study. Students in this class will learn
how to make inference for the event times with censored.
P8109 Statistical Inference 3 points
Prerequisites: P8104, working knowledge of calculus and linear algebra
This course covers a review of mathematical statistics and probability theory at the Masters level. Students will be
exposed to theory of estimation and hypothesis testing, confidence intervals and Bayesian inference. Topics include
population parameters, sufficient statistics, basic distribution theory, point and interval estimation, introduction to
the theory of hypothesis testing, and nonparametric procedures.
P8110 Applied Regression II 3 points
Prerequisites: P6104 or MPH Quantitative Foundations core course, and P8100 (Not open to MS/TM, PHDS, or SG
tracks)
An introduction to the application of statistical methods in survival analysis, generalized linear models, and design of
experiments. Topics to be covered include estimation and comparison of survival curves, regression models for
survival data, log-linear models, logit models, analysis of repeated measurements, and the analysis of data from
blocked and split-plot experiments. Examples are drawn from the health sciences.
P8112 Systematic Review and Meta-Analysis 1.5 points
Prerequisites: P6104, P8130 or MPH Quantitative Foundations core course and P6400
Research synthesis using systematic review and meta-analysis is one of the most valuable of research endeavors,
and can be a particularly rewarding experience for junior investigators who want to develop expertise in a specific
area of public health or medicine by producing a product with significant scientific impact. This course will combine
lecture and workshop elements to introduce students to the principles and practices of systematic review and meta-
analysis. It will be targeted to students who have previously been introduced to the concepts of basic biostatistics,
epidemiology, and clinical trials.
P8116 Design of Medical Experiments 3 points
Prerequisites: P8104, P8109, and P8130
This course covers the fundamental principles and techniques of experimental designs in clinical studies. Topics
include reliability of measurement, linear regression analysis, parallel groups design, analysis of variance
(ANOVA), multiple comparison, blocking, stratification, analysis of covariance (ANCOVA), repeated measures studies;
Latin squares design, crossover study, randomized incomplete block design, and factorial design.
DESCRIPTIONS
course
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P8119 Advanced Statistical and Computational Methods in Genetics and Genomics 3 points
Prerequisites: P6104 or P8130
This course introduces students to advanced computational and statistical methods used in the design and analysis
of high-dimensional genetic data, an area of critical importance in the current era of Big Data. The course starts with
a brief background in genetics, followed by in depth discussion of topics in genome-wide linkage and association
studies, and next-generation sequencing studies. Additional topics such as network genetics will also be covered.
Examples from recent and ongoing applications to complex traits will be used to illustrate methods and concepts.
P8120 Analysis of Categorical Data 3 points
Prerequisites: P6104 or P8130 or MPH Quantitative Foundations core course, and P6400 (Not open to MS/TM, PHDS,
or SG tracks)
A comprehensive overview of methods of analysis for binary and other discrete response data, with applications to
epidemiological and clinical studies. Topics discussed include the fourfold table, significance versus magnitude of
association; estimation of relative risk; matching in design and analysis; interrater agreement; logistic regression
analysis.
P8122 Statistical Methods for Causal Inference 3 points
Prerequisites: P8100 and P8110 or P8130 and P8131
This class will introduce students to both statistical theory and practice of causal inference. As theoretical
frameworks, we will discuss potential outcomes, causal graphs, randomization and model-based inference, causal
mediation, and suf ficient component causes. We will cover various methodological tools including randomized
experiments, matching, inverse probability weighting, instrumental variable approaches, dynamic causal models,
sensitivity analysis, statistical methods for mediation and interaction.
P8123 Analysis of Health Surveys 3 points
Pre-requisites: P8131 (or P8110) and P8104 (or P8107)
This is an applied statistical methods course. The course will introduce main techniques used in sampling
practice,including simple random sampling, stratification, systematic sampling, cluster sampling, probability
proportional to size sampling, and multistage sampling. Using national health surveys as examples, the course will
introduce and demonstrate the application of statistical methods in analysing across-sectional surveys and repeated
and longitudinal surveys, and conducting multiple imputation for missing data in large surveys. Other topics will
include methods for variance estimation, weighting, post-stratification, and non-sampling errors. If time allows, new
developments in small area estimation and in the era of data science will also be discussed.
P8124 Graphical Models for Complex Health Data 3 points
Pre-requisites: P8105 and P8109 or instructor's permission
This is a course at the intersection of statistics and machine learning, focusing on graphical models. In complex
systems with many (perhaps hundreds or thousands) of variables, the formalism of graphical models can make
representation more compact, inference more tractable, and intelligent data-driven decision-making more feasible.
We will focus on representational schemes based on directed and undirected graphical models and discuss
statistical inference, prediction, and structure learning. We will emphasize applications of graph-based methods in
areas relevant to health: genetics, neuroscience, epidemiology, image analysis, clinical support systems, and more.
DESCRIPTIONS
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P8130 Biostatistical Methods I 3 points
Prerequisites: Students are required to have working knowledge of calculus and linear algebra
This course introduces basic applied descriptive and inferential statistics. The first part of the course includes
elementary probability theory, an introduction to statistical distributions, principles of estimation and
hypothesis testing, methods for comparison of discrete and continuous data including chi-squared test of
independence, t-test, analysis of variance (ANOVA), and their non-parametric equivalents. The second part of
the course focuses on linear models (regression) theory and their practical implementation.
P8131 Biostatistical Methods II 3 points
Prerequisites: P8130
Regression analysis is widely used in biomedical research. Non-continuous (e.g., binary or count-valued)
responses, correlated observations, and censored data are frequently encountered in regression analysis. This
course will introduce advanced statistical methods to address these practical problems. Topics include
generalized linear models (GLM) for non-Gaussian response, mixed-effects models and generalized estimating
equations (GEE) for correlated observations, and Cox proportional hazards models for survival data analysis.
Examples are drawn from biomedical sciences.
P8133 Bayesian Analysis and Adaptive Designs for Clinical Trials 3 points
Prerequisites: P8104,P8109, and P8140
An introduction to sequential analysis as it applies to statistical problems in clinical trials, hypothesis testing,
selection, and estimation. Emphasis is placed on a study of procedures, operating characteristics, and problems
of implementation, rather than mathematical theory. Students obtain an overview of currently available
sequential designs and the advantages and disadvantages they offer in comparison with classical designs.
P8134 Stochastic Approximation and Modern Dose-Finding 3 points
Prerequisites: P8104 and P8109 or their equivalents
Provides an in-depth study of statistical designs for dose-finding clinical trials of new drugs. This course is designed
for advanced Master’s, DrPH, and PhD students in biostatistics. The overall learning objective is to equip students
with the techniques to construct, evaluate, and critique dose-finding designs. The course consists of two parts. The
first is a review of modern dose-finding techniques with a focus on the continual reassessment method (CRM) and its
clinical applications. The second part presents advanced topics on stochastic approximation and its related theory.
Connections between the dose-finding methods (part 1) and the stochastic approximation (part 2) will be drawn. The
practical implication of these connections is two-fold. First, the stochastic approximation will provide a versatile and
mathematically rigorous framework for tailoring dose-finding designs to specific clinical situations. Second, the well-
studied theory of stochastic approximation will be an effective analytical tool to approximate the theoretical
properties of the CRM.
P8134 Stochastic Approximation and Modern Dose-Finding 3 points
Prerequisites: P8104 and P8109 or their equivalents
Provides an in-depth study of statistical designs for dose-finding clinical trials of new drugs. This course is designed
for advanced Master’s, DrPH, and PhD students in biostatistics. The overall learning objective is to equip students
with the techniques to construct, evaluate, and critique dose-finding designs. The course consists of two parts. The
first is a review of modern dose-finding techniques with a focus on the continual reassessment method (CRM) and its
clinical applications. The second part presents advanced topics on stochastic approximation and its related theory.
Connections between the dose-finding methods (part 1) and the stochastic approximation (part 2) will be drawn. The
practical implication of these connections is two-fold. First, the stochastic approximation will provide a versatile and
mathematically rigorous framework for tailoring dose-finding designs to specific clinical situations. Second, the well-
studied theory of stochastic approximation will be an effective analytical tool to approximate the theoretical
properties of the CRM.
DESCRIPTIONS
course
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P8139 Statistical Genetics Modeling 3 points
Prerequisites: P6104 or P8130, and a working knowledge of calculus
Present to students statistical tools so that they can grasp the fundamentals of the design, conduct and analysis of
genetic association studies. The course will thoroughly discuss current methods that are being used to map genes for
common complex diseases. Great emphasis will be placed on candidate-gene and genome-wide association studies,
but linkage methods will also be treated. Another key feature of this course will be a detailed treatment of the major
findings of the Human Genome Project and HapMap Project.
P8140 Introduction to Randomized Clinical Trials 3 points
Prerequisites: P6104, P8130 or MPH Quantitative Foundations core course
Fundamental methods and concepts of the randomized clinical trial: protocol development, randomization,
blindedness, patient recruitment, informed consent, compliance, sample size determination, crossovers,
collaborative trials. Each student prepares and submits the protocol for a real or hypothetical clinical trial.
P8142 Clinical Trial Methodology 3 points
Prerequisites: P6104 or P8130
The main objective of this course is to provide students and investigators with a working knowledge of certain
methodological issues that arise in designing a clinical trial in order to conduct complex study designs that yield valid
and reliable results. With emphasis on several methodological and practical issues related to the design and analysis
of clinical experiments, topics include: the design of small studies (Phase I and II studies), interim analyses and group
sequential methods, survival studies, multiple outcome measures, surrogate outcomes, multicenter studies, issues in
data analysis, and reporting and interpreting study results.
P8144 Pharmaceutical Statistics 3 points
Prerequisites: P6104, P8130 or MPH Quantitative Foundations core course. SAS knowledge recommended.
Drug development from compound discovery to marketing and commercialization registration is a lengthy and
complex process in which statisticians play an important role from beginning to end. The main objective of this
course is to provide students with working knowledge of the methodological and operational issues that arise in
different stages of drug development that involve statistical contributions.
P8149 Human Population Genetics 3 points
Prerequisites: P8104
This course will cover all statistical aspects of population genetics. Upon completion of this course, the students will
be able to model and do inference of underlying population genetic mechanisms and apply acquired knowledge
about population genetics to the analyses of phenotypes.
P8157 Analysis of Longitudinal Data 3 points
Prerequisites: P8104, P8109, and P8130
The course will introduce students to statistical models and mthods for longitudinal data, i.e., repeatedly measured
data over time or under different conditions. The topics will include design and sample size calculation, Hotelling’s
T2, multivariate analysis of variance, multivariate linear regression (generalized linear models), models for
correlation, unbalanced repeated measurements, mixed effects models, EM algorithm, methods for non-normally
distributed data, generalized estimating equations, generalized linear mixed models, and missing data.
DESCRIPTIONS
course
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P8158 Latent Variable and Structural Equation Modeling for Health Sciences 3 points
Prerequisites: P6104, P8130 or MPH Quantitative core course
This course is designed for those students (or any researchers) who want to gain a significant familiarity with a
collection of statistical techniques that target the measurement of latent variables (i.e. variables that cannot be
measured directly) as well as methods for estimating relationships among variables within causal systems. This
course covers: both continuous and categorical latent variable measurement models (i.e. exploratory and
confirmatory factor analysis, item response theory models, latent class and finite mixture models), as well as
estimation of relationships in hypothesized causal systems using structural equation modeling. Data analysis
examples will come from health science applications and practical implementation of all methods will be
demonstrated using predominately the Mplus software, but also the R software.
P8160 Topics in Advanced Statistical Computing 3 points
Prerequisites: P8109, a basic understanding of Bayesian inference and working knowledge of a programming
language
As statistical models become increasingly complex, it is often the case that exact or even asymptotic distributions of
estimators and test statistics are intractable. With the continuing improvement of processor speed, computationally
intensive methods have become invaluable tools for statisticians to use in practice. This course covers the basic
modern statistical computing techniques and how they are applied in a variety of practical situations. Topics include
numerical optimization, random number generation, simulation, Monte Carlo integration, permutation tests,
jackknife and bootstrap procedures, Markov Chain Monte Carlo methods in Bayesian settings, and the EM algorithm.
P8170 Integrative Capstone Experience 2 points
Prerequisites: Biostatistics MPH students only
Required capstone course forall MPH students in Biostatistics. In this course, students will produce a written report
that describes an analysis of relevant data using statistical techniques learned during the course of the MPH
program.
P8180 Relational Databases and SQL Programming for Research and Data Science 3 points
Prerequisites: P6104, P8130 or MPH Quantitative Foundations core course, and the instructor’s permission.
This class provides an overview of the specific techniques available to collect, store, retrieve, and control the quality
of data in research projects. Students will be introduced to these concepts through a combination of lecture videos
and a substantial hands-on component consisting of structured computer-based exercises. Spreadsheet and
database technologies will be reviewed in detail to establish guidelines as to the appropriateness of their use to
manage data in research.
P8182 Writing a Successful Grant Application 1 point
Prerequisites: Concurrent enrollment in the Columbia Summer Research Institute. Required for MS-POR students. This
seminar-style course will lead students through the process of writing an NIH-style grant application. By the end of
the course, each student submits a research proposal outline following NIH guidelines for either an R01 or K
(career development) award. The emphasis in this course is on the quality of the proposed research, taking into
account feasibility, relevance, innovation, ethical foundation, and public health impact. As a culminating experience,
students make oral presentations summarizing their research proposals to an invited panel of senior, experienced
CUMC faculty, and receive feedback on their proposed research aims and approaches.
DESCRIPTIONS
course
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P8185 Capstone Consulting Seminar 1 point
Prerequisites: At least 15 points of required coursework in biostatistics. Biostatistics MS/MPH students only.
Required capstone course for most MS students and all MPH students in Biostatistics. Provides experience in the art
of consulting and in the proper application of statistical techniques to public health and medical research problems.
Enables students to translate research objectives into statistical hypotheses, devise appropriate study designs,
perform sample size calculations for studies employing simple random sampling, formulate and prepare written
plans for statistical analysis for a research proposal, compose summaries of quantitative analyses, and communicate
results clearly to public health colleagues. Based on seminars requiring active student participation.
P9104 Probability for Biostatisticians 3 points
Pre-requisites: P8109 and P8110, advanced calculus. Instructor’s permission needed for MS students
The biostatistical field is changing with new directions emerging constantly. Doing research in these new directions,
which often involve large data and complex designs, requires advanced probability and statistics tools. The purpose
of this new course is to collect these important probability methods and present them in a way that is friendly to a
biostatistics audience. This course is designed for PhD students in Biostatistics. Its primary objective is to help the
students achieve a solid understanding of these probability methods and develop strong analytical skills that are
necessary for conducting methodological research in modern biostatistics. At the completion of this course, the
students will a) have a working knowledge in Law of Large Numbers, Central Limit Theorems, martingale theory,
Brownian motions, weak convergence, empirical process, and Markov chain theory; b) be able to understand the
biostatistical literature that involves such methods; c) be able to do proofs that call for such knowledge.
P9109 Theory of Statistical Inference I 3 points
Prerequisites: P8104, P8109. Instructor’s permission needed for MS students
This course offers a general introduction to essential materials in advanced statistical theory for doctoral students in
biostatistics. The course is designed to prepare doctoral students in biostatistics for their written theory qualifying
exam. Students in this course will learn theory of estimation, confidence sets and hypothesis testing. Specific topics
include a quick review of measure-theoretic probability theory, concepts of suf ficiency and completeness, unbiased
estimation (UMVUE), least squares principle, likelihood estimation, a variety of estimators and their asymptotic
properties, confidence sets, the Neyman-Pearson lemma and uniformly most powerful tests. If time permits, the
likelihood ratio test, score test and Wald test, and sequential analysis will be covered.
P9110 Theory of Statistical Inference II 3 points
Prerequisites: P8104, P8109, and P9109. Instructor’s permission needed for MS students.
This course continues the introduction to mathematical statistics for doctoral students in biostatistics. Topics to be
covered include: principles of decision theory, Bayesian estimation, Hypothesis testing, asymptotics, M-estimation,
Wald tests, and score tests.
P9111 Asymptotic Statistics 3 points
Prerequisites: P8104, P8109, P9109, and P9110. Instructor’s permission needed for MS students.
The choice of topics will vary from year to year, but will typically include: empirical processes and
M-estimation, bootstrap methods, empirical likelihood, contiguity, local asymptotic normality, counting process
methods in survival analysis, semiparametric inference and efficiency.
DESCRIPTIONS
course
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P9120 Topics in Statistical Learning and Data Mining I 3 points
Prerequisites: Intended for Biostatistics PhD students and theoretically inclined MS students.
Provide students a systematic training in key topics in modern supervised statistical learning and data mining. For the
most part, the focus will remain on a theoretically sound understanding of the methods (learning algorithms) and
their applications in complex data analysis, rather than proving technical theorems. Applications of the statistical
learning and data mining tools in biomedical and health sciences will be highlighted.
P9130 Advanced Biostatistical Methods I 3 points
Prerequisites: Advanced calculus, linear algebra, basic probability, statistical inference. Instructor’s permission needed
for MS students
The course will provide a solid foundation of the theory behind linear models and generalized linear models. More
emphasis will be placed on concepts and theory with mathematical rigor. Topics covered including linear regression
models, logistic regression models, generalized linear regression models and methods for the analysis contingency
tables.
P9160 Master’s Essay in Biostatistics: Clinical Research Methods 3 points
Prerequisites: At least 15 points of required coursework. MS/CRM students only.
Students produce a Master’s essay in the form of aresearch article of publishable quality, supervised by faculty
members from Biostatistics and from the student’s own clinical field.
P9165 Master’s Essay in Biostatistics: Patient Oriented Research 0 points
Prerequisites: At least 15 points of required coursework. MS-POR students only.
Students produce a Master’s essay in the form of an NIH-style grant application, supervised by a project sponsor
from Biostatistics and a mentor from the student’s own clinical field. A formal presentation to the POR advisory
board is required for successful completion of the course.
P9185 Statistical Practices and Research for Interdisciplinary Sciences (SPRIS) I 3 points
Prerequisites: DrPH and PhD Biostatistics only
Required course for the DrPH and PhD students in biostatistics. Provides experience in the art of consulting and in
the proper application of statistical techniques to public health and medical research problems. Enables students to
translate research objectives into statistical hypotheses, devise appropriate study designs, perform sample size
calculations for studies employing simple random sampling, formulate and prepare written plans for statistical
analysis for a research proposal, compose summaries of quantitative analyses, and communicate results clearly to
public health colleagues. Based on seminars requiring active student participation.
P9186 Statistical Practices and Research for Interdisciplinary Science (SPRIS) II 1.5 points
Prerequisites: P9185
Students will apply the concepts and methods introduced in Statistical Practices and Research for Interdisciplinary
Science (SPRIS) I to a real research setting. Each student will be paired with a Biostatistics faculty member. The
student will participate in one of the mentor’s collaborative projects to learn how to be an effective member of an
interdisciplinary team. Student experience will vary depending on the assigned faculty member, but all students will
gain exposure to preparing collaborative grant applications, designing research studies, analyzing real data,
interpreting and presenting results, and writing manuscripts. Mentors will help to develop the student’s data
intuition skills, ability to ask good research questions, and leadership qualities. Where necessary, students may
replicate projects already completed by the faculty mentor to gain experience.
DESCRIPTIONS
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P8190/P9190 Tutorials in Biostatistics 1 to 6 points
For appropriately qualified students wishing to enrich their programs by undertaking literature reviews, special
studies, or small group instruction in topics not covered in formal courses. Hours to be arranged.
89260 Building Interdisciplinary Research Models 2 points
Interdisciplinary research is an approach to advancing scientific knowledge requiring mastery of specific
competencies. This seminar will introduce the students to competencies in interdisciplinary research through a
combination of readings and lectures in each necessary aspect, chosen from fields essential to successful
interdisciplinary research. This course will assist learners to understand why and how different professional
disciplines, each representing a body of scientific knowledge, must work together to generate and disseminate
knowledge. Learners will develop a set of skills specific to be an effective member and leader of an interdisciplinary
research team, and will become familiar with the advantages of team science.
FOR STUDENT INVOLVEMENT
opportunities
Colloquia
During the Fall and Spring semesters, the Department of Biostatistics holds seminars on a wide variety of topics
which are of interest to both students and faculty. The speakers are occasionally departmental faculty members
themselves but very often are invited guests who spend the day of their seminar discussing their research with
Biostatistics faculty and students. While all students are strongly urged to attend, doctoral student attendance is
mandatory.
Consulting Service
All MS/PHDS, PS, SG, TM and MPH are required to participate in the Biostatistics Consulting Service. This program is
designed to enable students to demonstrate their ability to integrate academic studies with the role of biostatistical
consultant/collaborator. The Biostatistics Consulting Service offers advice on data analysis and appropriate methods of
data presentation for publications, and provides design recommendations for public health and clinical research, including
preparation of grant proposals.
Participation in the Biostatistics Consulting Service meets the capstone requirement while providing students with an
opportunity to gain invaluable experience working with a diverse clientele on a variety of statistical problems.
Teaching Assistantships
Each semester, the Department makes available a limited number of Teaching Assistant (TA) positions. Upon
completion of one full semester of course work, eligible students may apply for a TA slot. Students are advised to
carefully consult the following policy on qualification, selection, and compensation of TAs before considering one of
these positions. All TA candidates must apply to the Assistant Director of Academic Programs.
To qualify for a TA position, students must:
• be registered as a full-time student during the the semester of the TA opportunity
• NOT be employed by another department at Columbia University for more than 20 hours/week
• have successfully completed the course of interest
• maintain a GPA of 3.3 or better
• be able to devote several hours per week to TA duties. This includes, but is not limited to:
o Attending class for lectures
o Recitation periods (for core teaching assistants)
o 1-2 regularly scheduled office hours
o Homework grading and preparation of teaching materials
Selection of TAs is made by the instructor. Priority is given to students in doctoral programs, students with greater
seniority, and students with previous TA experience who have received good evaluations from their former students
and course instructors.
TA compensation is taxable and is paid out over the course of the semester.
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INFORMATIONN
practicum
The Practicum Requirement
The intent of the practicum requirement is to engage students in activities aligned with their career goals, as well as
activities that demonstrate application of biostatistical methods and public health concepts relevant to the student’s
area of interest. Students will seek out activities that further develop their skill set and add new tools to their
professional toolkit. Upon completion of the practicum, the student will be able to provide evidence of application of
these skills to potential employers. Practicum placements are made on an individual basis in consultation with faculty
advisors who must approve both the proposed practicum prior to its initiation. Students and mentors must complete
an evaluation at the conclusion of the practicum experience.
Goals of the Practicum are:
For the Department of Biostatistics
• To provide the University with a part of the formative assessment of the student’s ability to function as a
Biostatistician;
• To serve as a means of continually evaluating the relevance and effectiveness of the curriculum, leading to
modifications of the formative and summative assessments when necessary.
For the Student
• To provide a continuing series of practical experiences geared to his or her level of expertise, which will offer a
chance to apply principles, skills, and techniques that have been acquired;
• To help the student learn how to assume professional roles in a variety of practice settings while becoming
accustomed to a range of organizational structures, working relationships, and job expectations;
• To help the student develop professional identification as a Biostatistician and gain experience in fulfilling his or her
role as a team member working with other professionals.
For the Practicum Institution/Organization
• To provide mentorship input into the university program and, thereby, allow staff to share in the development of
future Biostatisticians;
• To serve as a growth experience for the mentor’s staff through interaction with the students;
• To provide the mentor an opportunity to recruit employees and reduce the time needed for on-the-job training of
any students who, upon graduation, are hired.
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INFORMATIONN
practicum
Practicum Roles
The student is responsible for identifying potential practicum sites and making arrangements for his or her
practicum experience at an appropriate site. Appropriate sites will offer professional training and specialization.
The practicum project can include but not limited to:
• Statistical method development
• Simulation study to compare existing methods
• Novel application of existing statistical methods
• Collaborative research that can lead to significant scientific findings
Sites must be approved by the student’s faculty advisor. Ideally, the practicum placement should be approved no
less than a month before the beginning of the practicum.
In addition to the student, there are three individuals with roles in the practicum experience, they are:
1. Faculty Advisor: the student’s assigned advisor reviews and approves the proposed practicum as being relevant to
the student’s program track and career interests. The advisor must also endorse the student’s suitability as well as his
or her academic and technical abilities for a given practicum experience.
2. Department Practicum Coordinator: Corey Adams, ([email protected]). The Department Practicum
Coordinator is responsible for supervision of the practicum experience once the faculty and practicum advisors have
approved the general concept and basic objectives for a given practicum.
3. Practicum Advisor: the field supervisor who provides the educational experience and mentorship, which are at the
heart of the practicum experience. Practicum advisors should be motivated to host practicum experiences from a
sense of professional commitment to help students achieve professional skills and status. For the purposes of the
practicum, a qualified practicum advisor may include public health professionals, researchers, professors, doctors, etc.
It is necessary, that a mentor fully operate effectively at a professional level in his/her field.
Student Requirements for Completing the Practicum Requirements
1. The student is responsible for finding a practicum. MS students must complete the Practicum Approval Form, at
least one month before the start of the practicum. The form should be submitted no later than December 1st of the
student’s second year. MPH students must complete the Scope of Work (SOW) form with the Office of Careers and
Practice (OCP).
2. Once approved, the student begins his or her practicum experience. Before completing the practicum, the student
and his or her faculty advisor should meet at the midpoint to discuss progress.
3. After the completion of the practicum, the MS student submits a practicum report to his or her advisor for
approval. MPH and MS students will present his or her practicum experience at the Annual Practicum Symposium
which is held in late April/early May.
5. NOTE TO INTERNATIONAL STUDENTS
CPT and OPT forms must be obtained from ISSO and submitted to the Department Practicum Coordinator. Keep in
mind that CPT is only authorized for dates that are within a semester. If you have already completed a CPT but
have another opportunity that is different from your completed practicum, then you must register for
PUBH 8086-Public Health Practice Seminar (0.5 cr).
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INFORMATION
practicum
PRACTICUM FAQs
Q: Which degree programs are required to do a practicum in the Biostatistics Department?
A: MPH and MS/Pharmaceutical Statistics, Public Health Data Science, Statistical Genetics, and Theory &
Methods tracks.
Q: What is the process to apply for a Practicum?
A: It is the student responsibility to locate and secure an internship by December 1st of the students second
year. Once the internship is secured the student must send an email to their assigned faculty advisor with the
details of internship. Once the assigned faculty advisor reviews the duties of the internship—you will receive an
email stating it is a yes or no. The practicum proposal form can be requested from the Biostatistics Practicum
Coordinator, Corey Adams, [email protected].
International students will be required to complete a CPT form if the practicum experience is outside of
Columbia University. Once the CPT form is completed, the student will then send to Corey Q. Adams, Practicum
Coordinator at [email protected] in a pdf form. You will receive the signed CPT form from Corey Q.
Adams, and you will begin loading all required documents in Compass on the ISSO website.
Q: How many hours are required for a practicum in the department?
A: For MS students, there is no set number of hours required. For MPH students, you are required to work at
least 280 hours in your practicum. During the academic year, full-time students can work no more than 20
hours/week. During the summer students can work over 20 hours/week at their practicum site.
Q: What is the Scope of Work (SOW)?
A: Completed by MPH students only. The SOW, which is managed by the Mailman’s Office of Careers and
Practice (OCP), is an important tool for planning the practicum and meeting the School’s requirements for
engaging in a structured practicum process. Students must develop a practicum SOW in collaboration with the
practicum organization.
Q: How many Clinical Practice Trainings (CPT) am I allowed to do?
A: Students are allowed two CPT’s. One will suffice, however if students need to do another one for more
practice or to extend for more time they can do so. You will need to submit a new application if you change
employers or need to extend the time. Students who are seeking to extend or do a second practicum is
expected to resubmit a new application and will need to enroll in the PUBH 8086 Seminar Course, 0.5 credit
hours with Heather Krasna in the Office of Career Services. Student must be registered for course prior to
receiving approval and signature on second CPT form submission.
Q: When should students apply for CPT?
A: Apply at least 10 business days before your requested start date. CPT cannot be authorized retroactively, so
plan ahead!
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INFORMATION
practicum
PRACTICUM FAQs CONTINUED
Q: What is expected to be on the CPT employer letter when gathering ISSO documents are the following:
A:
• be on official company letterhead
• have a specific start date and a specific end date
• include the complete US address of your employer even if you are working remotely
• include the number of hours per week you will work
• include a detailed description or list of position duties
• be signed and dated by your employer
Q: How far in advance can I change my practicum for CPT?
A: Students will not be allowed to change their practicum 45 days before the final submission is due. If
students submit in time, the student is expected to review the details of the project with their assigned
faculty advisor and resubmit a new practicum proposal form with approval from the Biostatistics
Practicum Coordinator, Corey Q. Adams.
Q: I'm an international student, when should I apply for Optional Practical Training (OPT)?
A: By, regulation, the earliest you may apply for pre-completion OPT is 90 days before your requested
OPT start date. We recommend requesting a start date a few weeks earlier than the actual date you
want to begin employment.
Q: When can I use OPT?
A: You may use OPT during and/or after your degree program if it is longer than one academic year (2
semesters)
Q: What’s the maximum time allowed for an OPT?
A: You get 12 months of OPT for each higher degree level you complete (Bachelor's, Master's, and
PhD Degree).
Q: Do I need a job offer to apply for an OPT?
A: You do not need a job offer to apply for OPT, but if you do not work you forfeit the authorized OPT
time.
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INFORMATION
practicum
Finding a
practicum
(Student)
Student
searches for
practicum
opportunities
Student
completes a SOW
(MPH) or
Practicum
Approval Form
(MS)
Approving &
Completing
the Practicum
(Student & Faculty
Advisor)
Faculty advisor
reviews and
approves
practicum, and
then student
begins the
practicum
Faculty advisor
follows up at
midpoint with
student to discuss
progress
Student
completes the
practicum
Evaluating &
Presenting the
Practicum
(Student & Faculty
Advisor)
Faculty
Advisor
approves
practicum
report
Student
presents
experience at
Annual
Practicum
Symposium
The Process of Completing the Practicum Requirement
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2022-2023 STUDENT HANDBOOK
revised August 2022
