Département des maladies infectieuses, Institut de Veille Sanitaire, 12 rue du Val d'Osne, 94415 St Maurice, France.
BMC Med Res Methodol. 2012 Jun 8;12:73. doi: 10.1186/1471-2288-12-73.
Multiple Imputation as usually implemented assumes that data are Missing At Random (MAR), meaning that the underlying missing data mechanism, given the observed data, is independent of the unobserved data. To explore the sensitivity of the inferences to departures from the MAR assumption, we applied the method proposed by Carpenter et al. (2007).This approach aims to approximate inferences under a Missing Not At random (MNAR) mechanism by reweighting estimates obtained after multiple imputation where the weights depend on the assumed degree of departure from the MAR assumption.
The method is illustrated with epidemiological data from a surveillance system of hepatitis C virus (HCV) infection in France during the 2001-2007 period. The subpopulation studied included 4343 HCV infected patients who reported drug use. Risk factors for severe liver disease were assessed. After performing complete-case and multiple imputation analyses, we applied the sensitivity analysis to 3 risk factors of severe liver disease: past excessive alcohol consumption, HIV co-infection and infection with HCV genotype 3.
In these data, the association between severe liver disease and HIV was underestimated, if given the observed data the chance of observing HIV status is high when this is positive. Inference for two other risk factors were robust to plausible local departures from the MAR assumption.
We have demonstrated the practical utility of, and advocate, a pragmatic widely applicable approach to exploring plausible departures from the MAR assumption post multiple imputation. We have developed guidelines for applying this approach to epidemiological studies.
通常实施的多重插补法假设数据是随机缺失的（MAR），这意味着在给定观测数据的情况下，潜在的缺失数据机制与未观测数据是独立的。为了探究推断对违背 MAR 假设的敏感性，我们应用了 Carpenter 等人（2007 年）提出的方法。这种方法旨在通过重新加权多重插补后获得的估计值来逼近在非随机缺失（MNAR）机制下的推断，其中权重取决于对偏离 MAR 假设程度的假设。
该方法通过应用于法国 2001-2007 年期间丙型肝炎病毒（HCV）感染监测系统的流行病学数据进行说明。研究的子人群包括 4343 名报告药物使用的 HCV 感染患者。评估了严重肝病的危险因素。在进行完全案例和多重插补分析后，我们对 3 个严重肝病危险因素进行了敏感性分析：过去过量饮酒、HIV 合并感染和 HCV 基因型 3 感染。
在这些数据中，如果根据观测数据观察到 HIV 状态的可能性很高，那么 HIV 与严重肝病之间的关联就会被低估。另外两个危险因素的推断对 MAR 假设的合理局部偏离具有稳健性。
我们已经证明了，在多重插补后探索 MAR 假设合理偏离的实用且普遍适用的务实方法具有实际效用，并提倡使用这种方法。我们已经制定了将该方法应用于流行病学研究的指南。
