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比较使用行政数据的高维倾向评分与从高质量临床数据得出的倾向评分。

Comparing the high-dimensional propensity score for use with administrative data with propensity scores derived from high-quality clinical data.

机构信息

ICES, Toronto, ON, Canada.

Institute of Health Management, Policy and Evaluation, University of Toronto, ON, Canada.

出版信息

Stat Methods Med Res. 2020 Feb;29(2):568-588. doi: 10.1177/0962280219842362. Epub 2019 Apr 11.

Abstract

Administrative healthcare databases are increasingly being used for research purposes. When used to estimate the effects of treatments and interventions, an important limitation of these databases is the lack of information on important confounding variables. The high-dimensional propensity score (hdPS) is an algorithm that generates a large number of empirically-derived covariates using administrative healthcare databases. The hdPS has been described as enabling adjustment by proxy, in which a large number of empirically-derived covariates may serve as proxies for unmeasured confounding variables. We examined the validity of this assumption using samples of patients hospitalized with acute myocardial infarction (AMI) and congestive heart failure (CHF), for whom both administrative data and detailed clinical data were available. We considered three treatments in AMI patients: angiotensin-converting enzyme inhibitors, beta-blockers, and statins, while the first two treatments were also considered in CHF patients. We considered three propensity scores: (a) one derived using detailed clinical data; (b) the hdPS derived from administrative data; and (c) one derived from administrative data using expert opinion. Using each propensity score, we estimated inverse probability of treatment (IPT) weights. For each sample and treatment combination, and for each of the two propensity scores derived using administrative data, there were clinical variables not measured in administrative data that remained imbalanced after incorporating the IPT weights. However, the propensity score derived using clinical data always resulted in all clinical variables being balanced. When estimating hazard ratios, for some samples and treatment combinations, the hazard ratios estimated using the hdPS were more similar to those obtained using the clinical propensity score than were those obtained using the expert-derived propensity score. However, for other combinations, the effects estimated using the expert-derived propensity score were more similar to those obtained using the clinical propensity score than were those derived using the hdPS.

摘要

管理式医疗保健数据库越来越多地被用于研究目的。当用于估计治疗和干预措施的效果时,这些数据库的一个重要局限性是缺乏关于重要混杂变量的信息。高维倾向评分(hdPS)是一种算法,它使用管理式医疗保健数据库生成大量经验衍生的协变量。hdPS 被描述为通过代理进行调整,其中大量经验衍生的协变量可以作为未测量混杂变量的代理。我们使用急性心肌梗死(AMI)和充血性心力衰竭(CHF)住院患者的样本检验了这一假设的有效性,这些患者既有管理数据又有详细的临床数据。我们考虑了 AMI 患者的三种治疗方法:血管紧张素转换酶抑制剂、β受体阻滞剂和他汀类药物,而 CHF 患者也考虑了前两种治疗方法。我们考虑了三种倾向评分:(a)一种使用详细临床数据得出的评分;(b)一种使用管理式数据得出的 hdPS;(c)一种使用管理式数据和专家意见得出的评分。使用每个倾向评分,我们估计了逆概率治疗(IPT)权重。对于每个样本和治疗组合,以及使用管理式数据得出的两种倾向评分中的每一种,在纳入 IPT 权重后,仍有一些未在管理式数据中测量的临床变量不平衡。然而,使用临床数据得出的倾向评分始终导致所有临床变量平衡。在估计风险比时,对于一些样本和治疗组合,使用 hdPS 估计的风险比与使用临床倾向评分获得的风险比更相似,而不是与使用专家得出的倾向评分获得的风险比更相似。然而,对于其他组合,使用专家得出的倾向评分估计的效果与使用临床倾向评分获得的效果更相似,而不是与使用 hdPS 获得的效果更相似。

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