Talks focused on translating recent advances in biostatistics into practice. Biostatistics Continuing Education
At a glance
Opportunity for
  • Continuing education on recent advances in biostatistics
  • All members of the Harvard Catalyst community, but primarily geared toward biostatisticians
Session Dates
  • Varies; see below for details

The Harvard Catalyst Biostatistics Program will present seminars on current applied topics in biostatistics. These will include our monthly journal club and work in progress sessions, seminar series, symposia, and short courses.

Journal Club/Work in Progress

Michael Hughes, PhD
Professor of Biostatistics
Director, Center for Biostatistics in AIDS Research
Harvard T.H. Chan School of Public Health
January 25, 2018
Harvard T.H. Chan School of Public Health, Room 2-426

Seminar Series


Use of Electronic Health Records for Clinical Research: Issues of Study Design and Analysis

March 1, 2018, 8:30am-5:30pm
Armenise Amphitheater, Harvard Medical School

This symposium will explore the complex issues involved in designing and analyzing studies that sample from the electronic health record.

Holly Barr-Vermilya, MHA
Partners eCare Research Core (PeRC) Director

Tianxi Cai, ScD
Professor of Biostatistics, Harvard T.H. Chan School of Public Health

Victor Castro, MS
Team Lead, Research Information Science and Computing, Partners Healthcare

Sebastien Haneuse, PhD
Associate Professor of Biostatistics, Harvard T.H. Chan School of Public Health

Miguel Hernan, MD, DrPH
Kolokotrones Professor of Biostatistics and Epidemiology, Harvard T.H. Chan School of Public Health

Douglas MacFadden, MS
Chief Informatics Officer, Harvard Catalyst, Harvard Medical School

Elizabeth Mostofsky, ScD
Instructor, Department of Epidemiology, Harvard T.H. Chan School of Public Health

David Sontag, PhD
Assistant Professor, Department of Electrical Engineering and Computer Science, Institute for Medical Engineering & Science, Massachusetts Institute of Technology

Adrian Zai, MD, PhD
Assistant Professor of Medicine, Harvard Medical School, MGH

Jose Zubizarreta, PhD
Assistant Professor, Department of Health Care Policy, Harvard Medical School

Please email us to register.

Short Courses

Machine Learning and Bayesian Approaches for Data Science in Medicine

January 18, 2018
11:00am - 5:30pm
Kresge G1
Harvard T.H. Chan School of Public Health

The quantity and scope of data available for translational research are rapidly expanding, which provides both opportunities and challenges for researchers. In this course, statisticians Sherri Rose, PhD, and Laura Hatfield, PhD, will provide an overview of modern analytical methods for applied research in clinical and health policy topics. The course will begin with a broad introduction to posing research questions, evaluating data sources, and specifying and assessing causal inference assumptions. The rest of the course will focus on choosing methods that are best suited to particular research questions, with emphasis on the "why," "what," and "how" of machine learning and Bayesian estimation techniques and a brief overview of available software.

Sherri Rose, PhD
Associate Professor, Harvard Medical School

Laura Hatfield, PhD
Associate Professor, Harvard Medical School

Absolute Risk: Methods and Applications in Clinical Management and Public Health

April 23, 2018
8:30am 5:30pm
Minot Room, Countway Library

This course is an introduction to absolute risk, the probability of developing a specific outcome, over a specified time interval, in the presence of competing causes of mortality. This course will define absolute risk and discusses methodological issues relevant to the development and evaluation of absolute risk models. We will present the cause-specific and cumulative incidence approaches to incorporating covariates, and discuss various study designs and data for model building, including cohort, nested case-control, and case-control data combined with registry data. We will show how to evaluate the performance of risk prediction models and discuss the use of absolute risk in individual counseling for prevention strategies, including interventions that can have adverse effects. We also discuss the potential use of such models for disease prevention in the population, including designing prevention trials, estimating the absolute risk reduction in the population from modifying risk factor distributions, the "high risk" preventive intervention strategy, risk-based disease screening, and resource allocation.

Ruth Pfeiffer, PhD
Senior Investigator/Biostatistics Branch
Division of Cancer Epidemiology and Genetics
National Cancer Institute
Graduate of Technical University of Vienna, Austria (MA in applied mathematics) and University of Maryland, College Park (PhD in mathematical statistics)

Mitchell H. Gail, MD, PhD
Senior Investigator
Division of Cancer Epidemiology and Genetics
National Cancer Institute
Graduate of Harvard Medical School (MD) and
George Washington University (PhD in statistics)