Department of Statistics and Applied Probability, University of California - Santa Barbara, Santa Barbara, CA, 93106, U.S.A.
Stat Med. 2013 Sep 30;32(22):3775-87. doi: 10.1002/sim.5837. Epub 2013 May 2.
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.
我们提出了一种在受试者按序到达时进行治疗分配的方法。该方法基于更常用于观察性研究的倾向评分理论，在不假设结果模型的情况下平衡离散和连续协变量。尽管我们允许许多可能的规格，但我们深入探讨了一些具体实例。所提出的方法与先前提出的序贯随机化和分配程序进行了比较，并突出了与一些著名方法的关系。通过模拟，确定性版本显示出在最小假设下实现了协变量平衡和接近最佳效率。我们还研究了选定的随机化版本在最优性和选择偏差方面的特性。最后，我们对一项关于减肥的初步研究进行了应用。结果表明，该方法对平衡的协变量数量以及协变量的边缘和联合分布具有鲁棒性。
