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Surrogacy validation for time‐to‐event outcomes with illness‐death frailty models
Journal article   Open access   Peer reviewed

Surrogacy validation for time‐to‐event outcomes with illness‐death frailty models

Emily K. Roberts, Michael R. Elliott and Jeremy M. G. Taylor
Biometrical journal, Vol.66(1), 2200324
01/2024
DOI: 10.1002/bimj.202200324
PMCID: PMC10873101
PMID: 37776057
Appears in  UI Libraries Support Open Access
url
https://doi.org/10.1002/bimj.202200324View
Published (Version of record) Open Access

Abstract

A common practice in clinical trials is to evaluate a treatment effect on an intermediate outcome when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate outcomes 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 outcome with the treatment effect on the true, primary outcome. In particular, we propose illness‐death models to accommodate the censored and semicompeting risk structure of survival data. The proposed causal version of these models involves estimable and counterfactual frailty terms. Via these multistate 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 outcomes are time to distant metastasis and time to death.
 Bayesian methods clinical trial illness-death model surrogacy validation time-to-event data UIOWA OA Agreement

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