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Statistics in Biosciences

, Volume 8, Issue 1, pp 66–76 | Cite as

A Two-Stage Adaptive Targeted Clinical Trial Design for Biomarker Performance-Based Sample Size Re-Estimation

  • Zhong Gao
  • Anindya Roy
  • Ming TanEmail author
Article

Abstract

Biomarker-directed targeted clinical trial is aimed at developing pharmaceutical agents for a targeted patient subpopulation sharing a specific disease etiology. Biomarker plays a key role in patient enrichment for targeted trials. Biomarker performance substantially impacts heterogeneity of a targeted study population and consequently trial efficiency, statistical power, information accumulation, and early stopping decision-making (Simon and Maitournam in Clinical Cancer Res 10:6759-6763, 2004; Maitournam and Simon in Stat Med 24:329-339, 2005; Gao et al. in Contemp Clin Trials 42:119-131, 2015). Hence, accurate assessment of biomarker performance is crucial to sample size calculation in planning of targeted trials. However, prior knowledge of biomarker performance is often limited at the planning stage due to inadequacy of biomarker validation, differences between study populations in demographic characteristics and trial settings, etc. Under this circumstance, adaptive design would be useful in updating biomarker performance and re-estimating sample sizes when a targeted trial is ongoing. In this paper, we propose a two-stage adaptive design that provides flexibility in biomarker performance-based sample size adaption for targeted trials. The design can facilitate a targeted trial to achieve planned statistical power by re-assessment of actual biomarker performance and subsequent sample size adaption while preserving desired type-1 error.

Keywords

Positive Predictive Value Conditional Power Initial Sample Size Sample Size Adaption Patient Enrichment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© International Chinese Statistical Association 2016

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUniversity of Maryland, Baltimore CountyBaltimoreUSA
  2. 2.Department of Biostatistics, Bioinformatics, and BiomathematicsGeorgetown UniversityWashingtonUSA
  3. 3.Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringUSA

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