Missing data in longitudinal studies: Comparison of multiple imputation methods in a real clinical setting.

ABSTRACT

RATIONALE, AIMS, AND OBJECTIVES: Missing data represent a challenge in longitudinal studies. The aim of the study is to compare the performance of the multivariate normal imputation and the fully conditional specification methods, using real data set with missing data partially completed 2 years later. METHOD: The data used came from an ongoing randomized controlled trial with 5-year follow-up. At a certain time, we observed a number of patients with missing data and a number of patients whose data were unobserved because they were not yet eligible for a given follow-up. Both unobserved and missing data were imputed. The imputed unobserved data were compared with the corresponding real information obtained 2 years later. RESULTS: Both imputation methods showed similar performance on the accuracy measures and produced minimally biased estimates. CONCLUSION: Despite the large number of repeated measures with intermittent missing data and the non-normal multivariate distribution of data, both methods performed well and was not possible to determine which was better.