title: Optimal two-stage enrichment design correcting for biomarker misclassification.
abstract: The enrichment design is an important clinical trial design to detect the treatment effect of the molecularly targeted agent (MTA) in personalized medicine. Under this design, patients are stratified into marker-positive and marker-negative subgroups based on their biomarker statuses and only the marker-positive patients are enrolled into the trial and randomized to receive either the MTA or a standard treatment. As the biomarker plays a key role in determining the enrollment of the trial, a misclassification of the biomarker can induce substantial bias, undermine the integrity of the trial, and seriously affect the treatment evaluation. In this paper, we propose a two-stage optimal enrichment design that utilizes the surrogate marker to correct for the biomarker misclassification. The proposed design is optimal in the sense that it maximizes the probability of correctly classifying each patient's biomarker status based on the surrogate marker information. In addition, after analytically deriving the bias caused by the biomarker misclassification, we develop a likelihood ratio test based on the EM algorithm to correct for such bias. We conduct comprehensive simulation studies to investigate the operating characteristics of the optimal design and the results confirm the desirable performance of the proposed design.
authors.affiliation: 1 Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA.
authors.forename: Yong
authors.initials: Y
authors.lastname: Zang
authors.affiliation: 2 Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA, USA.
authors.forename: Beibei
authors.initials: B
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D015982:Bias; D015415:Biomarkers; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D015233:Models, Statistical; D057285:Precision Medicine
chemical_list: D015415:Biomarkers
keywords: Biomarker; clinical trial; enrichment design; measurement error; optimal design; personalized medicine