Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, 27599-7400, USA.
Division of Biostatistics, University of California Berkeley, Berkeley, USA.
Int J Biostat. 2022 Jun 6;19(1):239-259. doi: 10.1515/ijb-2020-0128. eCollection 2023 May 1.
Given an (optimal) dynamic treatment rule, it may be of interest to evaluate that rule - that is, to ask the causal question: what is the expected outcome had every subject received treatment according to that rule? In this paper, we study the performance of estimators that approximate the true value of: (1) an known dynamic treatment rule (2) the true, unknown optimal dynamic treatment rule (ODTR); (3) an estimated ODTR, a so-called "data-adaptive parameter," whose true value depends on the sample. Using simulations of point-treatment data, we specifically investigate: (1) the impact of increasingly data-adaptive estimation of nuisance parameters and/or of the ODTR on performance; (2) the potential for improved efficiency and bias reduction through the use of semiparametric efficient estimators; and, (3) the importance of sample splitting based on the cross-validated targeted maximum likelihood estimator (CV-TMLE) for accurate inference. In the simulations considered, there was very little cost and many benefits to using CV-TMLE to estimate the value of the true and estimated ODTR; importantly, and in contrast to non cross-validated estimators, the performance of CV-TMLE was maintained even when highly data-adaptive algorithms were used to estimate both nuisance parameters and the ODTR. In addition, we apply these estimators for the value of the rule to the "Interventions" study, an ongoing randomized controlled trial, to identify whether assigning cognitive behavioral therapy (CBT) to criminal justice-involved adults with mental illness using an ODTR significantly reduces the probability of recidivism, compared to assigning CBT in a non-individualized way.
给定一个（最优）动态治疗规则，可能有兴趣评估该规则，也就是说，要问一个因果问题：如果每个患者都按照该规则接受治疗，预期的结果是什么？在本文中，我们研究了估计量的性能，这些估计量近似于以下内容的真实值：（1）已知的动态治疗规则；（2）真实的、未知的最优动态治疗规则（ODTR）；（3）一个估计的 ODTR，一个所谓的“数据自适应参数”，其真实值取决于样本。通过对单点治疗数据的模拟，我们特别研究了：（1）日益数据自适应估计干扰参数和/或 ODTR 对性能的影响；（2）通过使用半参数有效估计量来提高效率和降低偏差的潜力；（3）基于交叉验证靶向最大似然估计量（CV-TMLE）的样本分割对准确推断的重要性。在所考虑的模拟中，使用 CV-TMLE 来估计真实和估计的 ODTR 的值几乎没有成本，但有很多好处；重要的是，与非交叉验证估计量相比，即使使用高度数据自适应算法来估计干扰参数和 ODTR，CV-TMLE 的性能也得以保持。此外，我们将这些估计器应用于“干预”研究中的规则值，这是一项正在进行的随机对照试验，以确定使用 ODTR 将认知行为疗法（CBT）分配给涉及刑事司法的患有精神疾病的成年人是否会显著降低累犯的概率，与以非个体化的方式分配 CBT 相比。
