title: Surrogacy assessment using principal stratification and a Gaussian copula model.
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. Many methods of surrogacy validation rely on models for the conditional distribution of T given Z and S. However, S is a post-randomization variable, and unobserved, simultaneous predictors of S and T may exist, resulting in a non-causal interpretation. Frangakis and Rubin developed the concept of principal surrogacy, stratifying on the joint distribution of the surrogate marker under treatment and control to assess the association between the causal effects of treatment on the marker and the causal effects of treatment on the clinical outcome. Working within the principal surrogacy framework, we address the scenario of an ordinal categorical variable as a surrogate for a censored failure time true endpoint. A Gaussian copula model is used to model the joint distribution of the potential outcomes of T, given the potential outcomes of S. Because the proposed model cannot be fully identified from the data, we use a Bayesian estimation approach with prior distributions consistent with reasonable assumptions in the surrogacy assessment setting. The method is applied to data from a colorectal cancer clinical trial, previously analyzed by Burzykowski et al.
authors.affiliation: 1 Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Asc
authors.initials: A
authors.lastname: Conlon
authors.affiliation: 1 Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Jmg
authors.initials: J
authors.lastname: Taylor
authors.affiliation: 1 Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.|2 Survey Methodology Program, Institute for Social Research, Ann Arbor, MI, USA.
authors.forename: M R
authors.initials: MR
authors.lastname: Elliott
mesh_terms: D001499:Bayes Theorem; D014408:Biomarkers, Tumor; D002986:Clinical Trials as Topic / Q000379:methods*; D015179:Colorectal Neoplasms / Q000188:drug therapy; D018450:Disease Progression; D006801:Humans; D016011:Normal Distribution; D016019:Survival Analysis; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor
keywords: Gaussian copula; causal inference; potential outcomes; surrogate endpoint