Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
Am J Epidemiol. 2018 Nov 1;187(11):2439-2448. doi: 10.1093/aje/kwy130.
Use of disease risk score (DRS)-based confounding adjustment when estimating treatment effects on multiple outcomes is not well studied. We designed an empirical cohort study to compare dabigatran initiators and warfarin initiators with respect to risks of ischemic stroke and major bleeding in 12 sequential monitoring periods (90 days each), using data from the Truven Marketscan database (Truven Health Analytics, Ann Arbor, Michigan). We implemented 2 approaches to combine DRS for multiple outcomes: 1) 1:1 matching on prognostic propensity scores (PPS), created using DRS for bleeding and stroke as independent variables in a propensity score (PS) model; and 2) simultaneous 1:1 matching on DRS for bleeding and stroke using Mahalanobis distance (M-distance), and compared their performance with that of traditional PS matching. M-distance matching appeared to produce more stable results in the early marketing period than both PPS and traditional PS matching; hazard ratios from unadjusted analysis, traditional PS matching, PPS matching, and M-distance matching after 4 periods were 0.72 (95% confidence interval (CI): 0.51, 1.03), 0.61 (95% CI: 0.31, 1.09), 0.55 (95% CI: 0.33, 0.91), and 0.78 (95% CI: 0.45, 1.34), respectively, for stroke and 0.65 (95% CI: 0.53, 0.80), 0.78 (95% CI: 0.60, 1.01), 0.75 (95% CI: 0.59, 0.96), and 0.78 (95% CI: 0.64, 0.95), respectively, for bleeding. In later periods, estimates were similar for traditional PS matching and M-distance matching but suggested potential residual confounding with PPS matching. These results suggest that M-distance matching may be a valid approach for extension of DRS-based confounding adjustments for multiple outcomes of interest.
使用基于疾病风险评分(DRS)的混杂调整来估计多种结局的治疗效果在研究中尚不完善。我们设计了一项实证队列研究,使用 Truven Marketscan 数据库(密歇根州安阿伯市 Truven Health Analytics)的数据,比较了达比加群酯和华法林的起始治疗者在 12 个连续监测期(每个 90 天)内缺血性中风和大出血的风险。我们实施了两种方法来合并多个结局的 DRS:1)基于预后倾向评分(PPS)的 1:1 匹配,使用 DRS 对出血和中风作为倾向评分(PS)模型中的独立变量来创建;2)同时使用 Mahalanobis 距离(M-距离)对出血和中风的 DRS 进行 1:1 匹配,并比较其与传统 PS 匹配的性能。在早期市场推广阶段,M-距离匹配似乎比 PPS 和传统 PS 匹配产生更稳定的结果;未经调整的分析、传统 PS 匹配、PPS 匹配和 M-距离匹配 4 个周期后的风险比分别为 0.72(95%置信区间:0.51,1.03)、0.61(95%置信区间:0.31,1.09)、0.55(95%置信区间:0.33,0.91)和 0.78(95%置信区间:0.45,1.34),用于中风;0.65(95%置信区间:0.53,0.80)、0.78(95%置信区间:0.60,1.01)、0.75(95%置信区间:0.59,0.96)和 0.78(95%置信区间:0.64,0.95),用于大出血。在后期,传统 PS 匹配和 M-距离匹配的估计值相似,但提示 PPS 匹配可能存在潜在的残余混杂。这些结果表明,M-距离匹配可能是一种有效的方法,可以扩展基于 DRS 的混杂调整,以用于感兴趣的多个结局。