High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.
High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.
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DOI：
10.1097/ede.0b013e3181a663cc
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发表时间：
2009-07
期刊：
影响因子：
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通讯作者：
Brookhart MA
中科院分区：
文献类型：
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作者：
Schneeweiss S;Rassen JA;Glynn RJ;Avorn J;Mogun H;Brookhart MA
调整从患者的医疗保健索赔数据中确定的大量协变量可能会改善对混杂因素的控制，因为这些变量可能共同代表未观察到的因素。在这里，我们开发和测试的算法，经验识别候选协变量，协变量的优先级，并将它们整合到一个基于倾向分数的混杂调整模型。我们开发了一个多步算法来实现索赔数据中的高维代理调整。步骤包括（1）识别数据维度，例如诊断、程序和药物，（2）凭经验识别候选协变量，（3）评估代码复发，（4）对协变量进行优先排序，（5）选择协变量进行调整，（6）估计暴露倾向评分，以及（7）估计结果模型。该算法在医疗保险索赔数据中进行了测试，包括一项关于考克斯-2抑制剂与非选择性非甾体抗炎药（NSAID）相比对胃毒性降低的影响的研究。在49，653例新使用考克斯-2抑制剂或非选择性NSAID的人群中，最初观察到上消化道毒性的粗相对风险（RR）（RR = 1.09 [95%置信区间= 0.91-1.30]）。调整15个预定义协变量后，可能产生胃保护作用（0.94[0.78-1.12]）。当调整额外的500个算法衍生协变量时，胃保护作用变得更强（0.88[0.73-1.06]）。一项关于他汀类药物降低死亡率的研究结果相似。使用算法调整证实流感疫苗接种和髋部骨折之间的结果为零（1.02[0.85-1.21]）。在典型的药物流行病学研究中，当以随机试验的预期结果为基准时，与仅限于预定义协变量的调整相比，提出的高维倾向评分导致效应估计值的改善。
Adjusting for large numbers of covariates ascertained from patients’ health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model. We developed a multi-step algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, e.g. diagnoses, procedures, and medications, (2) empirically identifying candidate covariates, (3) assess recurrence of codes, (4) prioritizing covariates, (5) selecting covariates for adjustment, (6) estimating the exposure propensity score, and (7) estimating an outcome model. This algorithm was tested in Medicare claims data, including a study on the effect of Cox-2 inhibitors on reduced gastric toxicity compared to nonselective nonsteroidal anti-inflammatory drugs (NSAIDs). In a population of 49,653 new users of Cox-2 inhibitors or nonselective NSAIDs, a crude relative risk (RR) for upper GI toxicity (RR = 1.09 [95% confidence interval = 0.91–1.30]) was initially observed. Adjusting for 15 predefined covariates resulted in a possible gastroprotective effect (0.94[0.78–1.12]). A gastroprotective effect became stronger when adjusting for an additional 500 algorithm-derived covariates (0.88[0.73–1.06]). Results of a study on the effect of statin on reduced mortality were similar. Using the algorithm adjustment confirmed a null finding between influenza vaccination and hip fracture (1.02[0.85–1.21]). In typical pharmacoepidemiologic studies, the proposed high-dimensional propensity score resulted in improved effect estimates compared with adjustment limited to predefined covariates, when benchmarked against results expected from randomized trials.