Néphrologie & Thérapeutique


Comment gérer les données manquantes ? Imputation multiple par équations chaînées : recommandations et explications pour la pratique clinique Volume 19, issue 3, June 2023


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1 Centre hospitalier universitaire de Caen, service de néphrologie, dialyse et transplantation, avenue de la Délivrande, 14000 Caen, France
2 Inserm U1086 ANTICIPE, Caen, France
3 Centre hospitalier universitaire de Caen, Unité de santé publique, Caen, France
Correspondance : B. Legendre

The presence of missing data, a constant problem in medical research, has several consequences: systematic loss of power, associated or not with a reduction in the representativeness of the sample analyzed. There are three types of missing data: 1) missing completely at random (MCAR); 2) missing at random (MAR); 3) missing not at random (MNAR).

Multiple imputation by chained equations allows for the correct handling of missing data under the MCAR and MAR assumptions. It allows to simulate for each missing data j, a number m of simulated values which seem plausible with regard to the other variables. A random effect is included in this simulation to express the uncertainty. Several data sets are thus created and analyzed individually, in an identical way. Then the estimators of each data set are combined to obtain a global estimator. Multiple imputation increases power, corrects for some biases and has the advantage of being applicable to many types of variables. Complete case analysis should no longer be the norm.

The objective of this guide is to help the reader in conducting an analysis with multiple imputed data. We cover the following points: the different types of missing data, the different historical approaches to handling them, and then we detail the multiple imputation method using chained equations. We provide a code example for the mice package of R®.