Liem Ylian S, Wong John B, Hunink Mg Myriam, de Charro Frank Th, Winkelmayer Wolfgang C
Program for the Assessment of Radiological Technology (ART Program), Department of Epidemiology & Biostatistics and the Department of Radiology, Erasmus University Medical Center Rotterdam, Dr, Molewaterplein 50, 3015 GE Rotterdam, the Netherlands.
Emerg Themes Epidemiol. 2010 May 11;7(1):1. doi: 10.1186/1742-7622-7-1.
To control for confounding bias from non-random treatment assignment in observational data, both traditional multivariable models and more recently propensity score approaches have been applied. Our aim was to compare a propensity score-stratified model with a traditional multivariable-adjusted model, specifically in estimating survival of hemodialysis (HD) versus peritoneal dialysis (PD) patients.
Using the Dutch End-Stage Renal Disease Registry, we constructed a propensity score, predicting PD assignment from age, gender, primary renal disease, center of dialysis, and year of first renal replacement therapy. We developed two Cox proportional hazards regression models to estimate survival on PD relative to HD, a propensity score-stratified model stratifying on the propensity score and a multivariable-adjusted model, and tested several interaction terms in both models.
The propensity score performed well: it showed a reasonable fit, had a good c-statistic, calibrated well and balanced the covariates. The main-effects multivariable-adjusted model and the propensity score-stratified univariable Cox model resulted in similar relative mortality risk estimates of PD compared with HD (0.99 and 0.97, respectively) with fewer significant covariates in the propensity model. After introducing the missing interaction variables for effect modification in both models, the mortality risk estimates for both main effects and interactions remained comparable, but the propensity score model had nearly as many covariates because of the additional interaction variables.
Although the propensity score performed well, it did not alter the treatment effect in the outcome model and lost its advantage of parsimony in the presence of effect modification.
为控制观察性数据中非随机治疗分配所产生的混杂偏倚，传统多变量模型和最近的倾向评分方法均已得到应用。我们的目的是比较倾向评分分层模型和传统多变量调整模型，特别是在估计血液透析（HD）与腹膜透析（PD）患者的生存率方面。
利用荷兰终末期肾病登记处的数据，我们构建了一个倾向评分，根据年龄、性别、原发性肾病、透析中心以及首次肾脏替代治疗年份来预测PD分配情况。我们开发了两个Cox比例风险回归模型来估计PD相对于HD的生存率，一个是基于倾向评分进行分层的倾向评分分层模型，另一个是多变量调整模型，并在两个模型中测试了几个交互项。
倾向评分表现良好：拟合度合理，c统计量良好，校准良好且协变量平衡。多变量调整主效应模型和倾向评分分层单变量Cox模型得出的PD相对于HD的相对死亡风险估计值相似（分别为0.99和0.97），倾向模型中的显著协变量较少。在两个模型中引入用于效应修正的缺失交互变量后，主效应和交互作用的死亡风险估计值仍然相当，但由于额外的交互变量，倾向评分模型的协变量数量几乎相同。
尽管倾向评分表现良好，但它并未改变结局模型中的治疗效果，并且在存在效应修正的情况下失去了其简约性优势。
