论文标题
通过不完美的协变量自适应随机化来提高估计效率
Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance
论文作者
论文摘要
我们研究了如何使用与不完美的受试者合规性的协变量随机化（CAR）中的协变量调整来提高效率。我们的回归调整估计量基于局部平均治疗效果的双重稳健力矩，即使具有分配的异质概率和错误指定的回归调整，也是一致且渐近地正常的。我们提出了一种最佳但可能误认为的线性调整及其通过非线性调整的进一步改进，这两者都比没有调整的估计值更有效。我们还提供了非参数和正规化调整的条件，以实现在汽车下结合的半参数效率。
We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probability of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments. We also provide conditions for nonparametric and regularized adjustments to achieve the semiparametric efficiency bound under CARs.
