A series of talks presented by the Harvard Catalyst Biostatistical Science Program based on recent advances in biostatistics, but with a focus on translation of those ideas to biostatistical practice.
Harvard Catalyst Biostatistics Seminar Series
During the 2009–10 academic year, the Harvard Catalyst Biostatistical Science Program will present a series of talks based on recent advances in biostatistics, but with a focus on translation of those ideas to biostatistical practice. Speakers will provide detailed examples of the application of methods, often including discussion of software, code, and worked examples.
A reception will follow each seminar.
Translating research to practice: An introduction to causal inference, with extensions to longitudinal data
Tyler VanderWeele, PhD
Associate Professor of Epidemiology
Harvard School of Public Health
Wednesday, November 18, 2009, 3:00–4:30pm
Trustman Boardroom
East Campus, Feldberg / Reisman Complex - 2nd Floor
Beth Israel Deaconess Medical Center
The first talk in the series, to be presented by Tyler VanderWeele, PhD, will discuss causal inference in the context of longitudinal data. The lecture will give a brief overview of how the "counterfactual" or "potential outcomes" framework can be useful in distinguishing association from causation. Issues concerning time-dependent confounding that can arise in longitudinal data will be discussed, and an introduction to causal methods to handle time-dependent confounding will be given. The ideas will be illustrated by a detailed discussion of an example using longitudinal data to distinguish the relative persistence of the effect of loneliness on depression versus on subjective well-being.
Musings about missing data: Challenges for the analysis of observational and randomized studies
Nicholas Horton, ScD
Associate Professor, Department of Mathematics & Statistics
Smith College
Tuesday, December 15, 2009, 3:00–4:30pm
Ledge Room 4-002B, One Brigham Circle
Brigham and Women's Hospital
Missing data arise in almost all real-world situations and can cause bias or lead to inefficient analyses. The development of statistical methods to address missingness has been actively pursued in recent years. This talk will (1) address complications in observational studies when there are many patterns of missing values for categorical and continuous predictors, (2) discuss issues in implementing analyses that are consistent with the intention to treat principle in randomized trials, and (3) demonstrate how these methods can be implemented through detailed discussion of examples.
Overview of methods for analyzing cluster-correlated data
Garrett Fitzmaurice, ScD
Professor in the Department of Biostatistics
Harvard School of Public Health
Wednesday, January 27, 2010, 3:30-5:00pm
(Please note different time)
Kresge G2
Harvard School of Public Health
How many participants? How many measurements?: The design of longitudinal studies
Donna Spiegelman, ScD
Professor of Epidemiologic Methods
Harvard School of Public Health
Tuesday, February 23, 2010, 3:00-4:30pm
Yawkey Room 10-660
Massachusetts General Hospital