*Department of Epidemiology, Merck Sharp & Dohme, North Wales, PA †Department of Epidemiology, University of North Carolina, Chapel Hill, NC ‡Department of Global Pharmacovigilance & Epidemiology, Bristol Meyers Squibb, Hopewell, NJ.
Med Care. 2014 Mar;52(3):280-7. doi: 10.1097/MLR.0000000000000064.
Researchers are often interested in estimating treatment effects in subgroups controlling for confounding based on a propensity score (PS) estimated in the overall study population.
To evaluate covariate balance and confounding control in sulfonylurea versus metformin initiators within subgroups defined by cardiovascular disease (CVD) history comparing an overall PS with subgroup-specific PSs implemented by 1:1 matching and stratification.
We analyzed younger patients from a US insurance claims database and older patients from 2 Medicare (Humana Medicare Advantage, fee-for-service Medicare Parts A, B, and D) datasets. Confounders and risk factors for acute myocardial infarction were included in an overall PS and subgroup PSs with and without CVD. Covariate balance was assessed using the average standardized absolute mean difference (ASAMD).
Compared with crude estimates, ASAMD across covariates was improved 70%-94% for stratification for Medicare cohorts and 44%-99% for the younger cohort, with minimal differences between overall and subgroup-specific PSs. With matching, 75%-99% balance improvement was achieved regardless of cohort and PS, but with smaller sample size. Hazard ratios within each CVD subgroup differed minimally among PS and cohorts.
Both overall PSs and CVD subgroup-specific PSs achieved good balance on measured covariates when assessing the relative association of diabetes monotherapy with nonfatal myocardial infarction. PS matching generally led to better balance than stratification, but with smaller sample size. Our study is limited insofar as crude differences were minimal, suggesting that the new user, active comparator design identified patients with some equipoise between treatments.
研究人员通常有兴趣在基于整个研究人群中估计的倾向评分（PS）控制混杂因素的情况下，估计亚组的治疗效果。
通过 1:1 匹配和分层比较整体 PS 与亚组特定 PS，评估磺脲类药物与二甲双胍启动者在心血管疾病（CVD）史定义的亚组中的协变量平衡和混杂控制。
我们分析了来自美国保险索赔数据库的年轻患者和来自 2 个医疗保险（Humana Medicare Advantage、医疗保险部分 A、B 和 D）数据集的老年患者。将急性心肌梗死的混杂因素和危险因素纳入整体 PS 和亚组 PS，包括 CVD 和不包括 CVD。使用平均标准化绝对平均差异（ASAMD）评估协变量平衡。
与粗估计相比，分层时 Medicare 队列的所有协变量的 ASAMD 提高了 70%-94%，年轻队列提高了 44%-99%，整体 PS 和亚组特定 PS 之间差异最小。无论队列和 PS 如何，匹配都能实现 75%-99%的平衡改善，但样本量较小。在每个 CVD 亚组中，PS 和队列之间的风险比差异最小。
在评估糖尿病单药治疗与非致死性心肌梗死的相对关联时，整体 PS 和 CVD 亚组特定 PS 均在测量协变量上实现了良好的平衡。PS 匹配通常比分层能更好地平衡，但样本量较小。我们的研究存在局限性，因为粗差异很小，这表明新使用者、主动对照设计确定了治疗之间存在一些均衡的患者。
