title: A simple, flexible, and effective covariate-adaptive treatment allocation procedure.
abstract: We present a method for allocating treatment when subjects arrive in sequence. Based on the theory of propensity scores more commonly used in observational studies, the method balances both discrete and continuous covariates without assuming a model for the outcome. Although we allow for a number of possible specifications, we explore some specific instances in depth. The proposed method is compared with previously suggested sequential randomization and allocation procedures with relationships to some well-known methods highlighted. Through simulations, the deterministic version is shown to achieve both covariate balance and near optimum efficiency with minimal assumptions. We also investigate the properties of selected randomized versions with respect to both optimality and selection bias. We conclude with an application to a pilot study on weight loss. The proposed method is shown to be robust to the number of covariates balanced and the marginal and joint distributions of those covariates.
authors: Department of Statistics and Applied Probability, University of California - Santa Barbara, Santa Barbara, CA, 93106, U.S.A.
authors: Travis M
authors: TM
authors: Loux
mesh_terms: D003198:Computer Simulation; D003376:Counseling; D003627:Data Interpretation, Statistical; D006801:Humans; D010865:Pilot Projects; D057216:Propensity Score; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D015983:Selection Bias; D015431:Weight Loss
keywords: biased coin design; covariate balance; propensity score; sequential randomization