论文标题
一步加权以概括和运输治疗效应估计到目标人群
One-Step weighting to generalize and transport treatment effect estimates to a target population
论文作者
论文摘要
从研究样本到目标人群的概括和运输效果估计的问题对于经验研究和统计方法是至关重要的。在随机实验和观察性研究中，通常将加权方法用于该目标。传统方法通过分别对治疗分配和研究选择概率进行建模，然后乘以其估计值的功能（例如对逆）来构建权重。在这项工作中，我们提供了一个理由和实施，用于一步一步。我们显示了这种一步方法与逆概率和反相反的权重之间的正式联系。我们证明，目标平均治疗效果的估计量是一致的，渐近正常的，多重的稳定性和半合理效率的。我们在模拟研究中评估了一步估计器的性能。我们在案例研究中说明了它在加利福尼亚州黑人男性中种族多样性对预防性医疗保健利用的影响的案例研究。我们提供实施方法的R代码。
The problem of generalization and transportation of treatment effect estimates from a study sample to a target population is central to empirical research and statistical methodology. In both randomized experiments and observational studies, weighting methods are often used with this objective. Traditional methods construct the weights by separately modeling the treatment assignment and study selection probabilities and then multiplying functions (e.g., inverses) of their estimates. In this work, we provide a justification and an implementation for weighting in a single step. We show a formal connection between this one-step method and inverse probability and inverse odds weighting. We demonstrate that the resulting estimator for the target average treatment effect is consistent, asymptotically Normal, multiply robust, and semiparametrically efficient. We evaluate the performance of the one-step estimator in a simulation study. We illustrate its use in a case study on the effects of physician racial diversity on preventive healthcare utilization among Black men in California. We provide R code implementing the methodology.
