We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package "SeqExpMatch" for use by practitioners is available.

翻译：我们建议一个动态分配程序,在利用某些对象先前评估的答复,测量在顺序随机试验中的平均治疗效果时,提高权力和效率。对象按顺序抵达,或者随机或配对于先前随机试验对象,并管理替代治疗。配对是通过动态匹配标准进行的,该标准反复学习哪些具体的共变对反应很重要。我们为平均治疗效果和精确测试制定估计值。我们说明了我们的方法在模拟情景和临床试验数据集中对其他分配程序的效率和权力的提高。有一个供执行人员使用的R包“SeqExpMatch”。