title: Multiple-objective response-adaptive repeated measurement designs in clinical trials for binary responses.
abstract: A multiple-objective allocation strategy was recently proposed for constructing response-adaptive repeated measurement designs for continuous responses. We extend the allocation strategy to constructing response-adaptive repeated measurement designs for binary responses. The approach with binary responses is quite different from the continuous case, as the information matrix is a function of responses, and it involves nonlinear modeling. To deal with these problems, we first build the design on the basis of success probabilities. Then we illustrate how various models can accommodate carryover effects on the basis of logits of response profiles as well as any correlation structure. Through computer simulations, we find that the allocation strategy developed for continuous responses also works well for binary responses. As expected, design efficiency in terms of mean squared error drops sharply, as more emphasis is placed on increasing treatment benefit than estimation precision. However, we find that it can successfully allocate more patients to better treatment sequences without sacrificing much estimation precision.
authors.affiliation: Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, TX, U.S.A.
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Liang
authors.forename: Yin
authors.initials: Y
authors.lastname: Li
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.forename: Keumhee C
authors.initials: KC
authors.lastname: Carriere
mesh_terms: D000959:Antihypertensive Agents / Q000627:therapeutic use; D001794:Blood Pressure / Q000187:drug effects; D003198:Computer Simulation; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design
chemical_list: D000959:Antihypertensive Agents
keywords: binary outcome; multiple-objective allocation strategy; response-adaptive repeated measurement designs