Centre for Big Data Research in Health (CBDRH), NSW, Australia.
Centre for Big Data Research in Health (CBDRH), NSW, Australia.
J Biomed Inform. 2020 Jul;107:103474. doi: 10.1016/j.jbi.2020.103474. Epub 2020 Jun 18.
The aim of clinical effectiveness research using repositories of electronic health records is to identify what health interventions 'work best' in real-world settings. Since there are several reasons why the net benefit of intervention may differ across patients, current comparative effectiveness literature focuses on investigating heterogeneous treatment effect and predicting whether an individual might benefit from an intervention. The majority of this literature has concentrated on the estimation of the effect of treatment on binary outcomes. However, many medical interventions are evaluated in terms of their effect on future events, which are subject to loss to follow-up. In this study, we describe a framework for the estimation of heterogeneous treatment effect in terms of differences in time-to-event (survival) probabilities. We divide the problem into three phases: (1) estimation of treatment effect conditioned on unique sets of the covariate vector; (2) identification of features important for heterogeneity using non-parametric variable importance methods; and (3) estimation of treatment effect on the reference classes defined by the previously selected features, using one-step Targeted Maximum Likelihood Estimation. We conducted a series of simulation studies and found that this method performs well when either sample size or event rate is high enough and the number of covariates contributing to the effect heterogeneity is moderate. An application of this method to a clinical case study was conducted by estimating the effect of oral anticoagulants on newly diagnosed non-valvular atrial fibrillation patients using data from the UK Clinical Practice Research Datalink.
使用电子健康记录存储库进行临床效果研究的目的是确定在真实环境中哪些健康干预措施“效果最好”。由于干预的净效益在患者之间存在多种差异的原因，目前的比较效果文献侧重于研究异质治疗效果，并预测个体是否可能从干预中受益。该文献的大部分内容都集中在估计治疗对二项结局的效果上。然而，许多医疗干预措施都是根据其对未来事件的效果来评估的，这些事件会因随访丢失而受到影响。在本研究中，我们描述了一种用于估计基于时间事件（生存）概率差异的异质治疗效果的框架。我们将问题分为三个阶段：（1）根据独特的协变量向量集来估计治疗效果；（2）使用非参数变量重要性方法识别对异质有重要影响的特征；（3）使用一步靶向最大似然估计，根据先前选择的特征来估计参考类别的治疗效果。我们进行了一系列模拟研究，发现当样本量或事件率足够高，且对效应异质性有影响的协变量数量适中时，这种方法的效果很好。通过使用来自英国临床实践研究数据链接的新诊断非瓣膜性心房颤动患者的数据，我们对该方法在临床案例研究中的应用进行了估计，结果表明口服抗凝剂对患者的治疗效果存在差异。
