Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data
1 Département des maladies infectieuses, Institut de Veille Sanitaire, 12 rue du Val d’Osne, 94415 St Maurice, France
2 Département des Maladies Infectieuses, Institut de Veille Sanitaire, St Maurice, France
3 Medical Statistics Unit, London School of Hygiene and Tropical Medicine, London, France
4 Direction Scientifique, Institut de Veille Sanitaire, St Maurice, France
BMC Medical Research Methodology 2012, 12:73 doi:10.1186/1471-2288-12-73Published: 8 June 2012
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.