Simple Techniques to Assess the Principal Strata Effect: Estimation, Sensitivity Analysis, and Bounds

• Activity Number: 283
• Type: Invited
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Committee on Applied Statisticians
• Author(s): Yasutaka Chiba*+
• Companies: Kinki University School of Medicine
• Keywords: Causal Inference ; Large sample bounds ; Potential Outcome ; Principal Stratification

Abstract

Issues of post-randomization selection bias and truncation-by-death can arise in randomized clinical trials; for example, in a cancer prevention trial, an outcome such as cancer severity is undefined for individuals who do not develop cancer. Restricting analysis to a subpopulation selected after randomization can give rise to biased outcome comparisons. One approach to deal with such issues is to consider the principal strata effect (PSE, or equally, the survivor average causal effect). PSE is defined as the effect of treatment on the outcome among the subpopulation that would have been selected under either treatment arm. Unfortunately, the PSE cannot generally be estimated without the identifying assumptions. In this talk, we describe a very simple sensitivity analysis technique that can be used to assess the PSE. As it may be troublesome to determine the plausible range of the sensitivity parameter, we present bounds for the sensitivity parameter under some reasonable assumptions. Furthermore, we present a marginal structural model to estimate the PSE with an additional assumption, which is also simple.