Causal Inference in HIV Vaccine Trials: Comparing Biomarkers Measured only in a Subgroup Chosen Post-Randomization

• Activity Number: 247
• Type: Contributed
• Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Bryan E. Shepherd*+ and Peter B. Gilbert
• Companies: University of Washington and Fred Hutchinson Cancer Research Center
• Keywords: selection bias ; causal inference ; EM algorithm ; vaccine efficacy

Abstract

In many trials, researchers want to perform treatment comparisons in subgroups selected after randomization. For example, in vaccine efficacy trials, it may be of interest to compare viral load between vaccine and placebo recipients who become infected with HIV during the trial. To account for this potential selection bias we propose a sensitivity analysis following the principal stratification framework set forth by Frangakis and Rubin (2002). The average causal effect of treatment assignment at a given covariate level can be estimated in the always infected stratum (those individuals who would have been infected whether they had been assigned to vaccine or to placebo). Assignment to the always infected stratum is unknown, but can be modeled conditional on randomization arm, infection status, covariates, the observed viral load, and a specified sensitivity parameter. The potential viral load as a function of covariates and given treatment assignment is also modeled. Under the assumption that being randomized to the vaccine arm does not increase the risk of infection, the EM algorithm is applied and maximum likelihood estimates can be obtained.