Biostatistics involves the theory and application of statistical science to analyze public health problems and to further biomedical research. Biostatistics faculty include leaders in the development of statistical methods for clinical trials and observational studies, studies on the environment, genomics/genetics, and the decision sciences. The department’s research in statistical methods and interdisciplinary collaborations provide many opportunities for student participation.

Current departmental research on statistical and computing methods for observational studies and clinical trials includes survival analysis, missing-data problems, and causal inference. Other areas of investigation are environmental research (methods for longitudinal studies, analyses with incomplete data, and meta-analysis); statistical aspects of the study of AIDS and cancer; quantitative problems in health-risk analysis, technology assessment, and clinical decision making; statistical methodology in psychiatric research and in genetic studies; Bayesian statistics; statistical computing; statistical genetics; and computational biology.

Collaborative research activities include coordination of national and international clinical trials, participation in studies of potential environmental hazards, design of health surveys, evaluation of health interventions and medical technologies, and consultation with federal, state, and local agencies. Many of these collaborations involve biomedical scientists in other Harvard-affiliated institutions.

Degree Programs in Biostatistics

The department offers a PhD program. In addition, the department offers the AM degree to students in the PhD program who have completed the AM requirements. Students interested in a terminal master’s degree program in biostatistics should apply for the master of science (SM) program in biostatistics through the Harvard School of Public Health. The department also offers a doctor of science (SD) program in biostatistics through the Harvard School of Public Health.

The programs offered by the Department of Biostatistics provide rigorous training in the development of methodology, collaboration, teaching, and consultation on a broad spectrum of health-related problems. The department prepares students for academic and private-sector research careers in the fields of biostatistics and health decision sciences. Recent graduates have assumed faculty posts at universities, as well as positions in research laboratories, federal government centers, pharmaceutical companies, and research -institutes.

Doctor of Philosophy (PhD)

The doctoral program in biostatistics is designed for those who have demonstrated both interest and ability in scholarly research. Qualified applicants may apply to the doc-toral program without a prior advanced degree.

Program of Study. The coursework for the PhD program is built on a core curriculum
of courses in probability theory and applications, statistical inference, and statistical methods. In addition, students must complete a selection of advanced coursework in biostatistics. These courses are chosen in consultation with the faculty advisor. Given the increasing reliance of statistical practice on computing technology, students are recommended to take one or more courses in statistical computing as part of their program. Courses in statistical genetics and computational biology can be included in the program. Detailed information about specific requirements and specialized tracks for the PhD degree is outlined in the Biostatistics Graduate Student Handbook.

Qualifying Examinations. In addition to coursework and residency requirements, other
formal requirements for the degree include passing of both a written and oral qualifying examination and the completion of a PhD dissertation. The written qualifying examination assesses the student’s background in probability and statistical theory and in applications. The oral qualifying examination assesses the student’s potential to perform research in a chosen field, and examines the student’s knowledge of his or her fields of study.

Dissertation. Each student is expected to complete a dissertation. The dissertation should be an original contribution to scientific knowledge in biostatistics. It can contribute to a subject matter field through innovative application of existing methodology, can produce an original methodologic contribution, or be a combination of the two. When the dissertation is complete, the student defends it to the Research Committee at a public presentation. The defense must be scheduled at least three weeks in advance. Copies of the dissertation should be given to members of the Research Committee and the department chair at least two weeks before the defense.

Master of Arts (AM)

No one is admitted as a candidate for the AM, only for the PhD. Nevertheless, the requirements for the AM degree must be satisfied by all students as they move toward the doctorate, and are expected to be completed by the end of the fourth term. The AM degree may be granted when these requirements are fulfilled. In addition, the department may confer a terminal AM on students who will not be completing the requirements for the PhD.

There are specific course requirements, but no qualifying examination or dissertation requirements for the AM degree. Detailed information about specific requirements and specialized tracks for the AM degree is outlined in the Biostatistics Graduate Student Handbook.

Admissions and Financial Aid

Students are admitted for the fall term only; applications must be received by December 15 for admission in the following fall. Applications received after December 15 cannot be guaranteed consideration. For more detailed information and forms, write to Admissions Office, Harvard Graduate School of Arts and Sciences, Holyoke Center, 3rd floor, 1350 Massachusetts Avenue, Cambridge, MA 02138. We encourage online submission of the application. See www.gsas.harvard.edu. Graduate Record Examination (GRE) General scores are required. GREs should be taken by October so that examination score reports arrive in time for admissions decisions. Applicants whose native language is other than English and who do not hold a Bachelor degree or its equivalent from an institution at which English is the language of instruction must submit scores from the Test of English as a Foreign Language (TOEFL) administered by the Educational Testing Service (ETS), Box 899, Princeton, NJ 08541. For financial aid, the appropriate financial aid application should be completed.

Applicants to the department should have successfully completed calculus through multi-
variable integration and at least one semester of linear algebra and have knowledge of a programming language such as C or FORTRAN. In addition, applicants are strongly encouraged to have completed courses in probability, statistics, advanced calculus, and numerical analysis. Practical knowledge of a statistical computing package such as SAS, S, Stata, or SPSS is also desirable.

Funding is available to qualified students pursuing the doctoral degree. Most of the funding is through six biostatistics training grants in AIDS, cancer, the environment, mental health, neurostatistics, and public health training for underrepresented minorities. These traineeships require US citizenship or permanent residency. Other funding is awarded on a competitive basis to qualified applicants, e.g., tuition scholarships and teaching and research assistantships.

The Department of Biostatistics ordinarily provides adequate financial support, which includes tuition, health fees, and living expenses, to full-time PhD students in good standing. As financial resources are limited, applicants are expected to apply for all non-Harvard and competitive Harvard scholarships for which they are eligible.

Students with an interest in a terminal master’s program (SM) in biostatistics should apply through the Harvard School of Public Health. To request detailed information and forms, write to Admissions Office, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, or see www.hsph.harvard.edu.

For additional information about research and training in biostatistics, please contact David Wypij, PhD, Director of Graduate Studies, Department of Biostatistics, 655 Huntington Avenue, Boston, MA 02115, 617-432-1056 (phone), 617-432-5619 (fax), This e-mail address is being protected from spam bots, you need JavaScript enabled to view it
(e-mail), or at www.biostat.harvard.edu.

Biostatistics Faculty

Betensky, Rebecca A., PhD, Professor of Biostatistics, Harvard School of Public Health.
Sequential analysis; correlated binary data.

Cai, Tianxi, ScD, Associate Professor of Bio-statistics, Harvard School of Public Health.
Survival data analysis; model selection and model checking; medical diagnostic testing.

Catalano, Paul J., ScD, Senior Lecturer in Biostatistics, Harvard School of Public Health.
Repeated measures; multivariate models; dose-response modeling; risk assessment; environmental statistics.

Coull, Brent, PhD, Associate Professor of Bio-statistics, Harvard School of Public Health.
Categorical data analysis; generalized linear mixed models; generalized additive models; capture-recapture methodology; exact categorical inference.

Davis, Roger, ScD, Associate Professor of Medicine, Har-vard Medical School; Associate Professor of Bio-statistics, Harvard School of Public Health.
Design and analysis of clinical trials; recursive partitioning methods.

DeGruttola, Victor, ScD, Professor of Bio-statistics, Harvard School of Public Health.
Analysis of repeated measures from longitudinal studies; methods for epidemiological analysis of AIDS.

DiRienzo, A. Gregory, PhD, Assistant Pro-fes-sor of Biostatistics. Bioinformatics, biological modeling, clinical trials; longitudinal studies and survival analysis; empirical processes, semiparametric models.

Finkelstein, Dianne, PhD, Professor, Harvard Medical School; Professor of Bio-statistics, Harvard School of Public Health. Analysis of multivariate failure time data; methods for analysis of interval censored and truncated data.

Fitzmaurice, Garrett Martin, ScD, Associate Professor of Medicine (Biostatistics), Harvard Medical School; Associate Professor of Biostatistics, Harvard School of Public Health. Analysis of multivariate binary outcomes; methods for analyzing mixed discrete and continuous outcomes.

Gauvreau, Kimberlee, ScD, Assistant Professor of Pediatrics, Harvard Medical School; Assistant Professor of Biostatistics, Harvard School of -Public Health. Pediatric cardiology; institutional variability following surgery for con-
genital heart disease.

Gelber, Richard, PhD, Professor of Pediatrics, Harvard Medical School; Professor of Biostatistics, Harvard School of Public Health. Design and analysis of clinical trials.

Gelman, Rebecca, PhD, Associate Professor of Radiation Therapy, Harvard Medical School; Associate Professor of Biostatistics, Harvard School of Public Health. Clinical trials; disease screening; survival methods.

Glynn, Robert J., ScD, Associate Professor of Medicine, Harvard Medical School; Associate Professor of Biostatistics, Harvard School of Pub-lic Health. Analysis of longitudinal data; non-response in sample surveys; epidemiology of eye diseases.

Gray, Robert, PhD, Professor of Biostatistics. Clinical trials; survival analysis; techniques for exploratory data analysis and model building.

Harrington, David, PhD, Professor of Bio-statistics, Harvard School of Public Health. Nonparametric methods for censored data; sequential designs for clinical trials.

Hu, Chengcheng, PhD, Assistant Professor of Biostatistics, Harvard School of Public Health. Design and analysis of clinical trials.

Hughes, Michael, PhD, Professor of Biostatistics, Harvard School of Public Health. Statistical methods in the design, analysis, and reporting of clinical trials and overviews.

Jiang, Hongyu, PhD, Assistant Professor of Biostatistics, Harvard School of Public Health. Design and analysis of clinical trials.

Kuntz, Karen K., ScD, Adjunct Associate Professor of Decision Science, Harvard School of Public Health. Cost-effectiveness analysis of medical technology; development of cancer prevention policy model; evaluation of biases in clinical decision modeling.

Lagakos, Stephen, PhD, Professor of Biostatistics, Harvard School of Public Health. Analysis of time-to-event data; stochastic processes; clinical trials; HIV/AIDS.

Laird, Nan, PhD, Professor of Biostatistics, Harvard School of Public Health. Longitudinal studies; nonresponse and missing data methods; discrete data analysis; Bayesian methods; meta-analysis; statistical genetics.

Lange, Christoph, PhD, Assistant Professor of Biostatistics, Harvard School of Public Health. Statistical methods in genetics; generalized linear models; robust statistics; time series analysis.

Lange, Nicholas, ScD, Associate Professor of Psychiatry, Harvard Medical School; Associate Professor of Biostatistics, Harvard School of Public Health. Statistical methodology for brain mapping; spatial point processes in two and three dimensions; longitudinal data analysis; computer-intense methods.

Lee, Mei-Ling Ting, PhD, Adjunct Associate Professor of Biostatistics, Harvard School of Pub-lic Health. Lifetime data analysis; categorical data analysis.

Li, Cheng, PhD, Assistant Professor of Bio-statistics, Harvard School of Public Health.
Computational biology.

Li, Yi, PhD, Associate Professor of Biostatistics, Harvard School of Public Health. Survival analysis; longitudinal and spatial data analysis.

Lin, Xihong, PhD, Professor of Biostatistics, Harvard School of Public Health. Environmental statistics.

Liu, Jun, PhD, Professor of Statistics, Faculty of Arts and Sciences; Professor of Biostatistics, Har-vard School of Public Health. Predicting gene regulatory binding motifs; homology modeling and sequence-based protein analysis; linkage disequilibrium studies; phylogenetic studies.

Liu, Xiaole (Shirley), PhD, Assistant Professor of Biostatistics, Harvard School of Public Health. Computational genomics, especially sequence analysis related to transcription and translation regulations.

Judith Lok, PhD, Assistant Professor of Biostatistics, Harvard School of Public Health. Continuous and discrete time measurements; causality; counterfactuals; longitudinal data; observational studies; competing risks; HIV.

Neuberg, Donna, ScD, Senior Lecturer in Biostatistics, Harvard School of Public Health. Cancer clinical trials; genetic epidemiology.

Normand, Sharon-Lise, PhD, Pro-fes-sor of Biostatistics, Harvard Medical School; Professor of Biostatistics, Harvard School of Public Health. Bayesian inference; graphical models; meta-analysis.

Orav, John, PhD, Associate Professor of Medicine, Harvard Medical School; Associate Profes-sor of Biostatistics, Harvard School of Public Health. Statistical computing and simulation; stochastic modeling; bioassay.

Paciorek, Christopher, PhD, Assistant Professor of Biostatistics, Harvard School of Public Health. Bayesian statistics, spatial statistics, and statistical computing, the environmental sciences.

Pagano, Marcello, PhD, Professor of Biostatistics, Harvard School of Public Health. Statistical computing; clinical trials; epidemic modeling.

Quackenbush, John, PhD, Professor of Computational Biology and Bioinformatics, Harvard School of Public Health. Computational biology, bioinformatics, functional genomics.

Robins, James, MD, Professor of Epidemiology and Biostatistics, Harvard School of Public Health. Analytic methods for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments.

Rosner, Bernard, PhD, Professor of Medicine, Harvard Medical School; Professor of Biostatistics, Harvard School of Public Health. Analysis of clustered binary data; longitudinal data analysis.

Rotnitzky, Andrea, PhD, Adjunct Professor of Biostatistics, Harvard School of Public Health. Longitudinal data analysis; analysis of repeated categorical data and cluster correlated data.

Ryan, Louise, PhD, Professor of Biostatistics,
Harvard School of Public Health. Environmental statistics; tumorgenicity and teratology experiments; clinical trials; goodness-of-fit tests; survival analysis.

Schoenfeld, David, PhD, Professor of Medicine, Harvard Medical School; Professor of Bio-statistics, Harvard School of Public Health. Statistics in medical research; linear models; bioassay; survival theory.

Armin Schwartzman, PhD, Assistant Professor of Biostatistics, Harvard School of Public Health. Modern multivariate statistics; large scale multiple testing; directional statistics; time series; spatial statistics; functional data analysis; shape analysis; applications to imaging, signal processing and bioinformatics.

Spiegelman, Donna, ScD, Professor of Epidemiology and Biostatistics, Harvard School of Public Health. Binary data models with measurement error and misclassification in model covariates.

Testa, Marcia, PhD, Senior Lecturer in Biostatistics, Harvard School of Public Health. Evaluation of quality-of-life indices in therapeutic clinical trials; clinical database information management systems.

Wang, Molin, PhD, Assistant Professor of Biostatistics, Harvard School of Public Health. Analysis of sparse dependent data; theory and application of estimating functions.

Ware, James, PhD, Professor of Biostatistics, Harvard School of Public Health. Design and analysis of longitudinal studies; statistical aspects of environmental health research.

Wei, Lee-Jen, PhD, Professor of Biostatistics, Harvard School of Public Health. Design and analysis of clinical trials; repeated measurements analysis; survival analysis.

Weinstein, Milton, PhD, Professor of Health Policy and Management and Biostatistics, Harvard School of Public Health. Cost-effectiveness of health practices and technologies.

Williams, Paige, PhD, Senior Lecturer in Biostatistics, Harvard School of Public Health. Cancer risk assessment; animal carcinogenicity bioassays.

Wypij, David, PhD, Associate Professor of Pediatrics, Harvard Medical School; Senior Lecturer in Biostatistics, Harvard School of Public Health. Longitudinal data analysis; discrete data; clinical trials; applications in cardiology, psychology, and malaria.

Wyshak, Grace, PhD, Associate Professor of Psychiatry, Harvard Medical School; Associate Professor of Biostatistics, Harvard School of Public Health. Biostatistical and demographic methods; women’s reproductive health.

Guocheng Yuan, PhD, Assistant Professor of Computational Biology and Bioinformatics, Harvard School of Public Health. Statistical and computational methodologies for genomic data analysis and integration; building predictive models for important biological pathways.

Zelen, Marvin, PhD, Professor of Biostatistics, Harvard School of Public Health. Theory and practice of clinical trials; methodology for early detection of disease.

Dissertation Titles

Detecting Spatial Clustering for Discrete, Censored, or Longitudinal Outcomes

Statistical Methods for the Analysis of HIV Drug-Resistance Data

Using Functional Regression Models to Analyze Longitudinal Data

Semiparametric Methods for Inferring Treatment Effects on Outcomes Defined Only If a Post-Randomization Event Occurs

Computational and Statistical Approaches to the Study of the Genetic Bases of Human Diseases

Microarray Analysis: Choice of Metric, New Clustering Algorithm and Identification of Transcription Factors

Robust Inference and Model Checking Techniques for Censored Linear Regression Models

Statistical Methods in SNP-Array-Based Loss-of-Heterozygosity Studies

Estimation of Marginal Regression Models with Multiple Source Predictors

Mixed Effects Mean Score Method, Optimal Design for Two-Stage Longitudinal Studies in a GEE Framework, and Addition of Covariates to a Markov Model Approach for Characterizing Progression of HIV Genetic Mutations

Analysis of Family Studies of Disease

Doubly Robust Estimation: Structural Nested Cumulative Failure Time Models, Correction of the Diagnostic Likelihood Ratio for Verification Bias

Methodology for Failure Time Data With Missing Cause of Failure or Dependent Censoring

On Maximum Attainable Correlation for the Sarmanov Family of Bivariate Distributions, Bayesian Analysis for Markers and Degradation, and Threshold Models with Markers Measured Before Observed Event Times

Statistical Models for Fertility-Related Issues in Adjuvant Treatment for Breast Cancer

Statistical Methods with Unrecognized Heterogeneity in Survival Data Analysis, Identifying Family Relationships in Genetic Studies, and Response-Related Incomplete Data

Contributions To Analysis Of Randomized Multi-Center Clinical Trials: The Role Of Conditioning

Computational and Statistical Approaches to Study Gene Regulation and Gene Function