In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit non-systematic covariate monitoring in EHR-based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR-based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment-monitoring interventions, due to a large decrease in data support and concerns over finite-sample bias from near-violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect (NDE) assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process. This article is protected by copyright. All rights reserved.

中文翻译：

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利用非系统协变量监测扩大适应性治疗策略因果效应的证据范围

在基于电子健康记录 (EHR) 的研究中，协变量监测的频率可能因协变量类型、患者之间以及随时间而异，这可能会限制对适应性治疗策略效果的推断的普遍性。此外，监测本身就是一种具有成本和收益的健康干预措施，利益相关者可能会对采用适应性治疗策略时监测的效果感兴趣。本文展示了如何在基于 EHR 的研究中利用非系统协变量监测来提高因果推理的普遍性，并在评估适应性治疗策略时评估监测对健康的影响。使用真实世界、基于 EHR 的 II 型糖尿病患者的比较有效性研究 (CER) 研究，我们说明了联合动态治疗和静态监测干预的评估如何改进 CER 证据，并描述了基于逆概率加权 (IPW) 的两种替代估计方法。首先，我们证明了联合治疗监测干预效果的标准估计量表现不佳，这是由于数据支持的大幅减少以及对几乎违反监测的阳性假设 (PA) 造成的有限样本偏差的担忧过程。其次，我们使用无直接影响 (NDE) 假设详细说明了替代 IPW 估计量。我们证明了这个估计器可以提高效率，但可能会增加治疗过程中违反 PA 的偏差。本文受版权保护。版权所有。