Causal Approaches to Surrogacy

• Activity Number: 219
• Type: Invited
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Jeremy Taylor* and Anna Conlon and Michael Elliott
• Companies: University of Michigan and University of Michigan and University of Michigan
• Keywords: surrogate endpoint ; potential outcome ; principal stratification ; copula

Abstract

In clinical trials, a surrogate outcome(S) can be measured before the outcome of interest (T) and may provide early information regarding the treatment (Z) effect on T. Some previous methods for surrogate validation rely on models for the distribution of T given Z and S. However, as S is a post-randomization variable, these methods may not have a causal interpretation. Using the principal surrogacy framework, we propose a Bayesian estimation strategy for surrogate validation when the joint distribution of potential surrogate and outcome measures is multivariate normal, and extend it using Gaussian copula's to an ordinal categorical variable for S and a censored failure time for T. We model the joint conditional distribution of the potential outcomes of T, given the potential outcomes of S and propose surrogacy validation measures from this model. By conditioning on principal strata of S, the resulting estimates are causal. As the model is not fully identifiable from the data, we propose some reasonable prior distributions and assumptions that can be placed on weakly identified parameters to aid in estimation. The method is applied to data from a colorectal cancer clinical trial.