ArticleStatistics in biopharmaceutical research2024
A Two-Stage Covariate-Adjusted Response-Adaptive Enrichment Design.
Article in Statistics in biopharmaceutical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
In the precision medicine paradigm, it is of interest to identify subgroups that benefit most from the treatment. However, the subgroup often cannot be identified until after a large-scale clinical trial. Clinical trials are often designed under the assumption of no treatment-by-covariate interaction effect and enroll all comers. This makes many patients go through unnecessary treatment and may decrease the efficiency of the trial. We propose a two-stage enrichment design that uses covariate-adjusted response-adaptive (CARA) allocation and a novel interaction pseudo-randomization test to evaluate the interaction effect in the interim analysis for binary and continuous outcomes. A pre-defined alpha level is used as the threshold to decide whether a subgroup will be identified and recruited in the second stage. If a below-threshold interaction effect is found, a regression model will be fit and the stratum with the largest treatment effect will be chosen as the best stratum. The trial will continue to the second stage with patients from the best stratum only. If the
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