Martens Edwin P, de Boer Anthonius, Pestman Wiebe R, Belitser Svetlana V, Stricker Bruno H Ch, Klungel Olaf H
Department of Pharmacoepidemiology & Pharmacotherapy, Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.
Pharmacoepidemiol Drug Saf. 2008 Jan;17(1):1-8. doi: 10.1002/pds.1520.
To compare adjusted effects of drug treatment for hypertension on the risk of stroke from propensity score (PS) methods with a multivariable Cox proportional hazards (Cox PH) regression in an observational study with censored data.
From two prospective population-based cohort studies in The Netherlands a selection of subjects was used who either received drug treatment for hypertension (n = 1293) or were untreated 'candidates' for treatment (n = 954). A multivariable Cox PH was performed on the risk of stroke using eight covariates along with three PS methods.
In multivariable Cox PH regression the adjusted hazard ratio (HR) for treatment was 0.64 (CI(95%): 0.42, 0.98). After stratification on the PS the HR was 0.58 (CI(95%): 0.38, 0.89). Matching on the PS yielded a HR of 0.49 (CI(95%): 0.27, 0.88), whereas adjustment with a continuous PS gave similar results as Cox regression. When more covariates were added (not possible in multivariable Cox model) a similar reduction in HR was reached by all PS methods. The inclusion of a simulated balanced covariate gave largest changes in HR using the multivariable Cox model and matching on the PS.
In PS methods in general a larger number of confounders can be used. In this data set matching on the PS is sensitive to small changes in the model, probably because of the small number of events. Stratification, and covariate adjustment, were less sensitive to the inclusion of a non-confounder than multivariable Cox PH regression. Attention should be paid to PS model building and balance checking.
在一项带有删失数据的观察性研究中，比较倾向评分（PS）方法和多变量Cox比例风险（Cox PH）回归对高血压药物治疗降低中风风险的校正效果。
从荷兰两项基于人群的前瞻性队列研究中选取了一些受试者，其中一部分接受了高血压药物治疗（n = 1293），另一部分是未接受治疗的“候选”患者（n = 954）。使用八个协变量以及三种PS方法对中风风险进行多变量Cox PH分析。
在多变量Cox PH回归中，治疗的校正风险比（HR）为0.64（95%置信区间：0.42，0.98）。按PS分层后，HR为0.58（95%置信区间：0.38，0.89）。根据PS进行匹配得到的HR为0.49（95%置信区间：0.27，0.88），而使用连续PS进行校正得到的结果与Cox回归相似。当添加更多协变量时（多变量Cox模型中无法实现），所有PS方法的HR都有类似程度的降低。在多变量Cox模型和按PS匹配中，纳入模拟平衡协变量导致HR变化最大。
一般来说，PS方法可以使用更多数量的混杂因素。在该数据集中，按PS匹配对模型中的微小变化敏感，可能是因为事件数量较少。与多变量Cox PH回归相比，分层和协变量校正对纳入非混杂因素的敏感性较低。应注意PS模型构建和平衡检验。
