Adaptive sequential selection procedures with random subset sizes. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Leu, Cheng-Shiun; Levin, Bruce
• Is Part Of: Sequential analysis. Volume 36:Issue 3(2017)
• Page Start: 384
• Page End: 396
• Publication Date: 2017-07-03
• Subjects: Elimination and recruitment -- Gupta approach -- random subset size -- sequential subset selection procedure; 62F07 -- 62F35 -- 62L10 -- 74Q20; Sequential analysis -- Periodicals; 519.54
• DOI: 10.1080/07474946.2017.1360090
• Languages: English
• ISSNs: 0747-4946

Abstract

ABSTRACT: We introduce a new family of sequential selection procedures wherein the subsets selected have random sizes. In comparison to subset selection procedures that select subsets of fixed size, the new procedures alleviate the need to specify the subset size prior to the experiment. We discuss the application of such procedures in the context of early phase clinical trials. The new procedures retain the adaptive features of the Levin-Robbins-Leu family of sequential subset selection procedures for selecting subsets of fixed size, namely, sequential elimination of inferior treatments and sequential recruitment of superior treatments. These two adaptive features respectively address ethical concerns that diminish interest in nonadaptive procedures and also allow promising treatments to be brought forward for further testing without having to wait until the end of the trial. The new procedures differ from the classical subset selection procedures of Shanti S. Gupta in terms of their respective goals and operating characteristics and we compare the two approaches in a simulation study. The findings suggest that whereas Gupta's procedure achieves its goal of including the single best treatment in the final selected subset with high probability, it does so by virtue of a nonadaptive, fixed sample size procedure that lacks necessary flexibility in the context of clinical research. By contrast, the new procedures aim to select treatment subsets that satisfy a different criterion, that of acceptable subset selection with high probability, while allowing adaptive elimination and recruitment and other flexibilities which we discuss to fit the practical needs of selection methods in clinical research.