22: Incorporating conditional independence assumptions for surrogate endpoint validation

Thomas M. Menino Convention & Exhibition Center 

In clinical studies, it is challenging to evaluate treatment efficacy when the outcome of interest is measured long-term or is otherwise difficult to measure. One solution is to replace the primary outcome with a more accessible surrogate endpoint (e.g. biomarker). Once validated, surrogate endpoints can accelerate clinical decision-making in future trials. In this work, we approach the validation of surrogates through a causal principal stratification framework with joint Gaussian outcomes. Assessment of surrogate endpoints is done using two causal quantities, average causal necessity (ACN) and average causal sufficiency (ACS), which we estimate using a Bayesian imputation procedure. However, estimating ACN and ACS via principal stratification relies on a correlation matrix that is not fully identifiable without making untestable assumptions. To improve identifiability, we propose incorporating conditional independence assumptions, such as through Bayesian priors on individual correlation parameters or by imposing them within Metropolis Hastings steps. We also condition on baseline covariates which may make the assumptions more plausible. Impacts on estimation are assessed.

surrogate endpoints

principal stratification

Bayesian methods 

Section on Bayesian Statistical Science