Linden Ariel, Yarnold Paul R
Linden Consulting Group, LLC, San Francisco, California, USA.
Optimal Data Analysis, LLC, Chicago, Illinois, USA.
J Eval Clin Pract. 2018 Apr;24(2):380-387. doi: 10.1111/jep.12859. Epub 2017 Dec 12.
RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then estimate treatment effects using Cox regression. A machine-learning alternative-classification tree analysis (CTA)-used to generate propensity scores and to estimate treatment effects in time-to-event data may identify complex relationships between covariates not found using conventional regression-based approaches.
Using empirical data, we identify all statistically valid CTA propensity score models and then use them to compute strata-specific, observation-level propensity score weights that are subsequently applied in outcomes analyses. We compare findings obtained using this framework to the conventional method, by evaluating covariate balance and treatment effect estimates obtained using Cox regression and a weighted CTA outcomes model.
All models had some imbalanced covariates. Nevertheless, treatment effect estimates were generally consistent across all weighted models.
In the study sample, given that all approaches elicited similar results, using CTA increased confidence that bias could not be reduced any further. Because the CTA algorithm identifies all statistically valid propensity score models for a sample, it is most likely to identify a correctly specified propensity score model-and therefore should be used either to confirm results using traditional methods, or to reveal biases that may be missed by traditional methods. Moreover, given that the true treatment effect is never known in observational data, CTA should be considered for estimating outcomes because no statistical assumptions are required.
原理、目的和目标：在具有事件发生时间结局的非随机研究中，评估治疗效果的一种常见方法是估计倾向得分并使用逻辑回归计算权重，检验协变量平衡，然后使用Cox回归估计治疗效果。一种机器学习替代方法——分类树分析（CTA）——用于生成倾向得分并估计事件发生时间数据中的治疗效果，它可能会识别出使用传统基于回归的方法未发现的协变量之间的复杂关系。
利用实证数据，我们识别出所有统计有效的CTA倾向得分模型，然后使用它们来计算特定分层的、观察水平的倾向得分权重，随后将这些权重应用于结局分析。我们通过评估使用Cox回归和加权CTA结局模型获得的协变量平衡和治疗效果估计值，将使用此框架获得的结果与传统方法进行比较。
所有模型都存在一些不平衡的协变量。然而，所有加权模型的治疗效果估计总体上是一致的。
在研究样本中，鉴于所有方法都得出了相似的结果，使用CTA增加了对偏差无法进一步降低的信心。由于CTA算法为样本识别出所有统计有效的倾向得分模型，它最有可能识别出正确设定的倾向得分模型——因此，它应要么用于使用传统方法确认结果，要么用于揭示传统方法可能遗漏的偏差。此外，鉴于在观察性数据中真实的治疗效果永远未知，在估计结局时应考虑使用CTA，因为它不需要统计假设。
