Effect Modification by Post-Treatment Variables in Mental Health Research

• Activity Number: 113
• Type: Topic Contributed
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Mental Health Statistics Section
• Author(s): Alisa J Stephens*+ and Marshall M. Joffe
• Companies: University of Pennsylvania and University of Pennsylvania
• Keywords: Causal Inference ; Structural Mean Models ; Time varying effect modification

Abstract

Standard approaches to effect modification consider how the effect of a treatment or exposure is modified by variables observable at the time of treatment decisions.  Such modeling approaches have are closely related to decision problems, in which only information in such variables can be used in making treatment decisions.  However, the effects of a treatment may sometimes be modified by post-treatment variables.  Such post-treatment effect modification may be of interest for explanatory purposes and for determining whether to abort or modify a treatment after initiation based on a patient's response.  We explain more fully the motivation behind such models and how to formulate these models, and apply the methods in a randomized trial in mental health research.