| INSTRUCTOR: |
James Robins, M.D. |
| DATES: |
Friday, October 21st
|
| TIME: |
9:00 am -11:30 am and 2:00 pm -4:30 pm |
| LOCATION: |
Irving Center Conference Room.
Presbyterian Hospital Building
622 W 168th St, 10th floor
|
TOPIC
This course will provide an in depth investigation of statistical methods for drawing causal inferences from observational studies. Informal epidemiologic concepts such as confounding, comparability, overall effects, direct effects, intermediate variables, and selection bias will be formally defined within the context of a counterfactual causal model. Methods for the analysis of the causal effects of time-varying exposures in the presence of time dependent covariates that are simultaneously confounders and intermediate variables will be emphasized. These methods include g-estimation of structural nested models, inverse probability weighted estimators of marginal structural models, optimal treatment regime structural nested models, and g-computation algorithm estimators. The mathematical level will be non-technical.
AUDIENCE
This short course is targeted at researchers from the social and behavioral sciences and medicine who investigate questions that are causal in nature.The course will assume the participant has the mathematical and statistical sophistication typical of graduates of Ph.D. programs in psychology, political science and sociology.
INSTRUCTOR
The principal focus of Dr. Robins' research has been the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments. The new methods are to a large extent based on the estimation of the parameters of a new class of causal models - the structural nested models - using a new class of estimators - the G estimators. The usual approach to the estimation of the effect of a time-varying treatment or exposure on time to disease is to model the hazard incidence of failure at time t as a function of past treatment history using a time-dependent Cox proportional hazards model. Dr. Robins has applied his methods to analyze the effect of a non-randomized treatment aerosolized pentamidine on the survival of AIDS patients in ACTG Trial 002; the effect of arsenic exposure on the mortality experience of a cohort of Montana copper smelter workers; the effect of formaldehyde on the respiratory disease mortality of a cohort of U.S. chemical workers; and the effect of smoking cessation on subsequent myocardial infarction and death within the MRFIT randomized trial.
TO REGISTER
This short course is open free of charge to faculty, postdoctoral fellows, and graduate students at Columbia University as well as faculty and postdoctoral fellows at other sites of the Robert Wood Johnson Health & Society Scholars (H&SS) Program.Enrollment is limited and on a first come first serve basis; H&SS affiliates will have priority.
To register, please send an email to: chssp@columbia.edu.Please include your mailing address, as readings will be sent to course participants.
|
The Health & Society Scholars Program at Columbia University is a postdoctoral program funded by the Robert Wood Johnson Foundation. It is a joint initiative of the Mailman School of Public Health and the Institute for Social and Economic Research and Policy (ISERP) at Columbia, and is co-directed by Bruce Link and Peter Bearman. For more information call 212-854-3694 or email chssp@columbia.edu.
|
