Multiple imputation for continuous variables using a Bayesian principal component analysis
Résumé
We propose a multiple imputation method to deal with incomplete continuous data based on principal component analysis (PCA). To reflect the uncertainty of the parameters from one imputation to the next, we use a Bayesian treatment of the PCA model. Using a simulation study, the method is compared to two classical approaches: multiple imputation based on joint modeling and on fully conditional modeling. Contrary to the others, the proposed method can be easily used on data sets where the number of individuals is less than the number of variables. In addition, it provides a good point estimate of the parameter of interest, an estimate of the variability of the estimator reliable while reducing the width of the confidence intervals.