INFERENCE FOR A TWO-STAGE ENRICHMENT DESIGN

• 成果类型: Article
• 署名作者: Lin, Zhantao; Flournoy, Nancy; Rosenberger, William F.
• 署名单位: George Mason University; University of Missouri System; University of Missouri Columbia
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/21-AOS2051
• 发表日期: 2021
• 页码: 2697-2720
• 关键词: dummy variables; clinical-trials; EFFICIENCY; selection

摘要

Two-stage enrichment designs can be used to target the benefiting population in clinical trials based on patients' biomarkers. In the case of continuous biomarkers, we show that using a bivariate model that treats biomarkers as random variables more accurately identifies a treatment-benefiting enriched population than assuming biomarkers are fixed. Additionally, we show that under the bivariate model, the maximum likelihood estimators (MLEs) follow a randomly scaled mixture of normal distributions. Using random normings, we obtain asymptotically standard normal MLEs and construct hypothesis tests. Finally, in a simulation study, we demonstrate that our proposed design is more powerful than a single stage design when outcomes and biomarkers are correlated; the model-based estimators have smaller bias and mean square error (MSE) than weighted average estimators.