Crowe Brenda J, Lipkovich Ilya A, Wang Ouhong
Eli Lilly and Company, Indianapolis, IN, USA.
Pharm Stat. 2010 Oct-Dec;9(4):269-79. doi: 10.1002/pst.389.
We performed a simulation study comparing the statistical properties of the estimated log odds ratio from propensity scores analyses of a binary response variable, in which missing baseline data had been imputed using a simple imputation scheme (Treatment Mean Imputation), compared with three ways of performing multiple imputation (MI) and with a Complete Case analysis. MI that included treatment (treated/untreated) and outcome (for our analyses, outcome was adverse event [yes/no]) in the imputer's model had the best statistical properties of the imputation schemes we studied. MI is feasible to use in situations where one has just a few outcomes to analyze. We also found that Treatment Mean Imputation performed quite well and is a reasonable alternative to MI in situations where it is not feasible to use MI. Treatment Mean Imputation performed better than MI methods that did not include both the treatment and outcome in the imputer's model.
我们进行了一项模拟研究，比较了对二元反应变量进行倾向得分分析时估计的对数比值比的统计特性。在该分析中，使用简单插补方案（治疗均值插补）对缺失的基线数据进行了插补，并与三种多重插补（MI）方法以及完全病例分析进行了比较。在插补模型中纳入治疗（治疗/未治疗）和结局（对于我们的分析，结局为不良事件[是/否]）的MI具有我们所研究的插补方案中最佳的统计特性。MI在仅有少数结局需要分析的情况下是可行的。我们还发现，治疗均值插补表现相当不错，在无法使用MI的情况下是MI的合理替代方法。治疗均值插补比在插补模型中未同时纳入治疗和结局的MI方法表现更好。
