Surrogacy Validation for Time-to-Event Outcomes with Illness-Death Frailty Models

TL;DR

This paper develops a causal framework using illness-death frailty models to validate intermediate surrogate endpoints in time-to-event clinical trials, accounting for censored and semi-competing risks.

Contribution

It introduces a novel causal modeling approach with estimable counterfactual frailty terms for surrogate validation in survival data.

Findings

Bayesian estimation method performs well in simulations

Model sensitivity analysis highlights key assumptions

Application to prostate cancer trial demonstrates practical utility

Abstract

A common practice in clinical trials is to evaluate a treatment effect on an intermediate endpoint when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate endpoints in a causally-valid way when the trial outcomes are time-to-event. Using counterfactual outcomes, those that would be observed if the counterfactual treatment had been given, the causal association paradigm assesses the relationship of the treatment effect on the surrogate $S$ with the treatment effect on the true endpoint $T$. In particular, we propose illness death models to accommodate the censored and semi-competing risk structure of survival data. The proposed causal version of these models involves estimable and counterfactual frailty terms. Via these multi-state models, we characterize what a valid surrogate would look like using a causal effect predictiveness plot. We evaluate the estimation properties of a Bayesian method using Markov Chain Monte Carlo and assess the sensitivity of our model assumptions. Our motivating data source is a localized prostate cancer clinical trial where the two survival endpoints are time to distant metastasis and time to death.