{"title":"Sample Size for Enriched Biomarker Designs With Measurement Error for Time-to-Event Outcomes.","authors":"Siyuan Guo, Susan Halabi, Aiyi Liu","doi":"10.1002/pst.70027","DOIUrl":null,"url":null,"abstract":"<p><p>A major emphasis in personalized medicine is to optimally treat subgroups of patients who may benefit from certain therapeutic agents. One relevant study design is the targeted design, in which patients have consented for their specimens to be obtained at baseline and the specimens are sent to a laboratory for assessing the biomarker status prior to randomization. Here, only biomarker-positive patients will be randomized to either an experimental or the standard of care arms. Many biomarkers, however, are derived from patient tissue specimens, which are heterogeneous leading to variability in the biomarker levels and status. This heterogeneity would have an adverse impact on the power of an enriched biomarker clinical trial. In this article, we show the adverse effect of using the uncorrected sample size and overcome this challenge by presenting an approach to adjust for misclassification for the targeted design. Specifically, we propose a sample size formula that adjusts for misclassification and apply it in the design of two phase III clinical trials in renal and prostate cancer.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 5","pages":"e70027"},"PeriodicalIF":1.4000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pst.70027","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
A major emphasis in personalized medicine is to optimally treat subgroups of patients who may benefit from certain therapeutic agents. One relevant study design is the targeted design, in which patients have consented for their specimens to be obtained at baseline and the specimens are sent to a laboratory for assessing the biomarker status prior to randomization. Here, only biomarker-positive patients will be randomized to either an experimental or the standard of care arms. Many biomarkers, however, are derived from patient tissue specimens, which are heterogeneous leading to variability in the biomarker levels and status. This heterogeneity would have an adverse impact on the power of an enriched biomarker clinical trial. In this article, we show the adverse effect of using the uncorrected sample size and overcome this challenge by presenting an approach to adjust for misclassification for the targeted design. Specifically, we propose a sample size formula that adjusts for misclassification and apply it in the design of two phase III clinical trials in renal and prostate cancer.
期刊介绍:
Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics.
The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.