Abstract:

In a clinical trial, an intermediate marker may serve as a surrogate for a true clinical outcome of interest with the goal of making the trial run more efficiently. We consider how to validate intermediate endpoints in a causally-valid way when the trial outcomes are time-to-event. Using counterfactual outcomes, we use principal stratification methods to assess the relationship of the treatment effect with the treatment effect on the true endpoint. 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 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 and assess the sensitivity of our model assumptions. Finally, we apply our proposed methods to a prostate cancer randomized clinical trial where the two survival endpoints are time to distant metastasis and time to death.