title: Simple sequential boundaries for treatment selection in multi-armed randomized clinical trials with a control.
abstract: In situations when many regimens are possible candidates for a large phase III study, but too few resources are available to evaluate each relative to the standard, conducting a multi-armed randomized selection trial is a useful strategy to remove inferior treatments from further consideration. When the study has a relatively quick endpoint such as an imaging-based lesion volume change in acute stroke patients, frequent interim monitoring of the trial is ethically and practically appealing to clinicians. In this article, I propose a class of sequential selection boundaries for multi-armed clinical trials, in which the objective is to select a treatment with a clinically significant improvement upon the control group, or to declare futility if no such treatment exists. The proposed boundaries are easy to implement in a blinded fashion, and can be applied on a flexible monitoring schedule in terms of calendar time. Design calibration with respect to prespecified levels of confidence is simple, and can be accomplished when the response rate of the control group is known only up to an interval. One of the proposed methods is applied to redesign a selection trial with an imaging endpoint in acute stroke patients, and is compared to an optimal two-stage design via simulations: The proposed method imposes smaller sample size on average than the two-stage design; this advantage is substantial when there is in fact a superior treatment to the control group.
authors.affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, New York 10032, U.S.A. email:yc632@columbia.edu.
authors.forename: Ying Kuen
authors.initials: YK
authors.lastname: Cheung
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D020521:Stroke / Q000473:pathology / Q000628:therapy; D016896:Treatment Outcome