Principal causal effect identification and surrogate endpoint evaluation by multiple trials

TL;DR

This paper develops methods to identify and estimate principal causal effects across multiple trials without relying on traditional assumptions, applied to colon cancer clinical trial data to evaluate surrogate endpoints.

Contribution

It introduces a novel approach that removes the exclusion restriction and monotonicity assumptions by leveraging multiple trials and homogeneity, with a Bayesian sensitivity analysis.

Findings

Monotonicity assumption is not tenable in the data.

Three-year disease-free survival is a valid surrogate for five-year overall survival.

Homogeneity assumption can be assessed via Bayesian hierarchical models.

Abstract

Principal stratification is a causal framework to analyze randomized experiments with a post-treatment variable between the treatment and endpoint variables. Because the principal strata defined by the potential outcomes of the post-treatment variable are not observable, we generally cannot identify the causal effects within principal strata. Motivated by a real data set of phase III adjuvant colon clinical trials, we propose approaches to identifying and estimating the principal causal effects via multiple trials. For the identifiability, we remove the commonly-used exclusion restriction assumption by stipulating that the principal causal effects are homogeneous across these trials. To remove another commonly-used monotonicity assumption, we give a necessary condition for the local identifiability, which requires at least three trials. Applying our approaches to the data from adjuvant colon clinical trials, we find that the commonly-used monotonicity assumption is untenable, and disease-free survival with three-year follow-up is a valid surrogate endpoint for overall survival with five-year follow-up, which satisfies both the causal necessity and the causal sufficiency. We also propose a sensitivity analysis approach based on Bayesian hierarchical models to investigate the impact of the deviation from the homogeneity assumption.