Schneeweiss Sebastian, Rassen Jeremy A, Glynn Robert J, Avorn Jerry, Mogun Helen, Brookhart M Alan
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Epidemiology. 2009 Jul;20(4):512-22. doi: 10.1097/EDE.0b013e3181a663cc.
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 multistep algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, eg, diagnoses, procedures, and medications; (2) empirically identifying candidate covariates; (3) assessing 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 with 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.
根据患者医疗保健理赔数据确定大量协变量进行调整，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。在此，我们开发并测试了一种算法，该算法可凭经验识别候选协变量、对协变量进行优先级排序，并将其整合到基于倾向评分的混杂因素调整模型中。
我们开发了一种多步骤算法，以在理赔数据中实施高维代理调整。步骤包括：(1)识别数据维度，如诊断、手术和药物；(2)凭经验识别候选协变量；(3)评估代码的重现性；(4)对协变量进行优先级排序；(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。该算法在医疗保险理赔数据中进行了测试，包括一项关于与非选择性非甾体抗炎药（NSAIDs）相比，Cox-2抑制剂对降低胃毒性影响的研究。
在49653名Cox-2抑制剂或非选择性NSAIDs新用户中，最初观察到上消化道毒性的粗相对风险（RR）为1.09[95%置信区间=0.91-1.30]。对15个预定义协变量进行调整后，出现了可能的胃保护作用（0.94[0.78-1.12]）。当对另外500个由算法得出的协变量进行调整时，胃保护作用更强（0.88[0.73-1.06]）。一项关于他汀类药物对降低死亡率影响的研究结果相似。使用算法调整证实了流感疫苗接种与髋部骨折之间无关联（1.02[0.85-1.21]）。
在典型的药物流行病学研究中，与仅限于预定义协变量的调整相比，当以随机试验预期的结果为基准时，所提出的高维倾向评分可改善效应估计。
