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Five criteria for using a surrogate endpoint to predict treatment effect based on data from multiple previous trials.

ABSTRACT: A surrogate endpoint in a randomized clinical trial is an endpoint that occurs after randomization and before the true, clinically meaningful, endpoint that yields conclusions about the effect of treatment on true endpoint. A surrogate endpoint can accelerate the evaluation of new treatments but at the risk of misleading conclusions. Therefore, criteria are needed for deciding whether to use a surrogate endpoint in a new trial. For the meta-analytic setting of multiple previous trials, each with the same pair of surrogate and true endpoints, this article formulates 5 criteria for using a surrogate endpoint in a new trial to predict the effect of treatment on the true endpoint in the new trial. The first 2 criteria, which are easily computed from a zero-intercept linear random effects model, involve statistical considerations: an acceptable sample size multiplier and an acceptable prediction separation score. The remaining 3 criteria involve clinical and biological considerations: similarity of biological mechanisms of treatments between the new trial and previous trials, similarity of secondary treatments following the surrogate endpoint between the new trial and previous trials, and a negligible risk of harmful side effects arising after the observation of the surrogate endpoint in the new trial. These 5 criteria constitute an appropriately high bar for using a surrogate endpoint to make a definitive treatment recommendation.

SUBMITTER: Baker SG

PROVIDER: S-EPMC5771803 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Five criteria for using a surrogate endpoint to predict treatment effect based on data from multiple previous trials.

Baker Stuart G SG

Statistics in medicine 20171121 4

A surrogate endpoint in a randomized clinical trial is an endpoint that occurs after randomization and before the true, clinically meaningful, endpoint that yields conclusions about the effect of treatment on true endpoint. A surrogate endpoint can accelerate the evaluation of new treatments but at the risk of misleading conclusions. Therefore, criteria are needed for deciding whether to use a surrogate endpoint in a new trial. For the meta-analytic setting of multiple previous trials, each with ...[more]

PMID: 29164641

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Project description:BackgroundRobust identification of surrogate endpoints can help accelerate the development of pharmacotherapies for diseases traditionally evaluated using true endpoints associated with prolonged follow-up. The meta-analysis-based surrogate endpoint evaluation (SEE) integrates data from multiple, usually smaller, trials to statistically confirm a surrogate endpoint as a robust proxy for the true endpoint. To test the applicability of SEE when only a single, larger trial is available, we analysed the cardiovascular (CV) survival endpoint from the large multinational trial LEADER (9340 subjects) that confirmed the CV safety of a diabetes drug (liraglutide). We evaluated if using country as a trial unit adequately facilitated the meta-analysis and calculation of R2 by country group.MethodsData were grouped by country, ensuring at least 30 CV deaths (497 in total) in each of the nine resulting by-country groups. In a two-step SEE on the grouped dataset, we first fitted the group-specific Cox proportional hazard models; next, on the trial-level, we regressed the estimated hazard ratio (HR; liraglutide vs placebo) of the true endpoints (CV death: 497 events, or all-cause death: 828 events) on the HR of the surrogate endpoint (major CV adverse event [MACE]: 1302 events) and derived the group-specific R2 and its 95% confidence interval (CI).ResultsGroup-level surrogacy of MACE was supported for CV death but not for all-cause death, with [Formula: see text] values of 0.85 [0.63;1.00]95% CI and 0.23 [0.00;0.67]95% CI, respectively. Sensitivity analyses using different grouping approaches (e.g. grouping by region) corroborated the robustness of the conclusions as well as the appropriateness of the data-grouping approaches.ConclusionsWe derived a specific grouping approach to successfully apply SEE on data from a single trial. This may allow for the statistically robust identification and validation of surrogate endpoints based on the abundance of large monolithic outcome trials conducted as part of drug development programmes in, for example, diabetes.

Dataset Information

Five criteria for using a surrogate endpoint to predict treatment effect based on data from multiple previous trials.

Publications

Five criteria for using a surrogate endpoint to predict treatment effect based on data from multiple previous trials.

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