Causal Inference for the Comparison of Dynamic Treatment Regimens

• Activity Number: 212
• Type: Invited
• Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
• Sponsor: WNAR
• Author(s): Cecilia Cotton*+ and Patrick Heagerty
• Companies: University of Waterloo and University of Washington
• Keywords: Causal inference ; Survival analysis ; Observational Data

Abstract

In this talk I will consider the problem of comparing the causal effects of multiple treatment regimens on survival based on observational data. This problem is complicated by the fact that depending on the regimen definitions, subjects may have been adherent to multiple regimens at the same time. I propose a methodology in which each subject contributes follow-up data to each regimen to which they are continuously adherent before being artificially censored. A marginal structural Cox proportional hazards model or log rank test with an appropriate variance estimate can be used to compare survival. I will also discuss how g-estimation can be used in this setting to allow for flexible adherence structures. The methods will be illustrated through simulation as well as application to epoetin dosing regimens among hemodialysis subjects.