Statistics in Biosciences

, Volume 10, Issue 1, pp 107–116 | Cite as

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

Efficiency of Targeted Trials
  • Yifan Wang
  • Huisong Sun
  • Hongkun Wang
  • Aiyi LiuEmail author


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.


Enrichment designs Genomic biomarkers Misclassification Personalized medicine Sample sizes and power Targeted treatment 



The research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), the National Institutes of Health (NIH). The authors thank the Guest Editor-in-Chief and two referees for their helpful comments that improved the paper.


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Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  • Yifan Wang
    • 1
  • Huisong Sun
    • 2
  • Hongkun Wang
    • 2
  • Aiyi Liu
    • 1
    Email author
  1. 1.Biostatistics and Bioinformatics BranchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentRockvilleUSA
  2. 2.Department of Biostatistics, Bioinformatics and BiomathematicsGeorgetown UniversityWashingtonUSA

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