Efficiency of Enrichment Design for Pre–Post Trials with Binary Endpoint

Yifan Wang (), Huisong Sun (), Hongkun Wang () and Aiyi Liu ()
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Yifan Wang: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Huisong Sun: Georgetown University
Hongkun Wang: Georgetown University
Aiyi Liu: Eunice Kennedy Shriver National Institute of Child Health and Human Development

Statistics in Biosciences, 2018, vol. 10, issue 1, No 7, 107-116

Abstract: Abstract Enrichment design is a common strategy in personalized medicine in which treatments are given only to patients who are tested positive on a genomic biomarker. Such a targeted trial, as compared to the untargeted trials in which all patients receive the treatments, can substantially reduce the sample size needed for a study as demonstrated in the literature for two-arm trials. To fill in the gaps in the existing literature, we consider trials to evaluate a targeted treatment by comparing pre–post trial outcomes to investigate the intervention effect after the treatment. We investigate the relative efficiency in terms of sample size reduction of an enrichment design against the conventional design, focusing on binary endpoints. The effects of misclassification of the genomic classifier on the relative efficiency are also investigated.

Keywords: Enrichment designs; Genomic biomarkers; Misclassification; Personalized medicine; Sample sizes and power; Targeted treatment (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s12561-015-9130-z

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