Machekano R N, Dorsey G, Hubbard A
Center for Health Care Research and Policy, Case Western Reserve University, Cleveland, Ohio 44109, USA.
Stat Methods Med Res. 2008 Apr;17(2):191-206. doi: 10.1177/0962280207078202. Epub 2007 Aug 14.
The effect of missing data in causal inference problems is widely recognized. In malaria drug efficacy studies, it is often difficult to distinguish between new and old infections after treatment, resulting in indeterminate outcomes. Methods that adjust for possible bias from missing data include a variety of imputation procedures (extreme case analysis, hot-deck, single and multiple imputation), weighting methods, and likelihood based methods (data augmentation, EM procedures and their extensions). In this article, we focus our discussion on multiple imputation and two weighting procedures (the inverse probability weighted and the doubly robust (DR) extension), comparing the methods' applicability to the efficient estimation of malaria treatment effects. Simulation studies indicate that DR estimators are generally preferable because they offer protection to misspecification of either the outcome model or the missingness model. We apply the methods to analyze malaria efficacy studies from Uganda.
缺失数据在因果推断问题中的影响已得到广泛认可。在疟疾药物疗效研究中，治疗后往往难以区分新感染和旧感染，从而导致结果不确定。针对缺失数据可能产生的偏差进行调整的方法包括多种插补程序（极端情况分析、热卡填充、单重和多重插补）、加权方法以及基于似然的方法（数据扩充、期望最大化（EM）程序及其扩展）。在本文中，我们将讨论重点放在多重插补和两种加权程序（逆概率加权和双重稳健（DR）扩展）上，比较这些方法在有效估计疟疾治疗效果方面的适用性。模拟研究表明，DR估计量通常更可取，因为它们能防范结果模型或缺失模型的错误设定。我们应用这些方法来分析来自乌干达的疟疾疗效研究。
