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Missing data in longitudinal studies: Comparison of multiple imputation methods in a real clinical setting
Author(s) -
Rosato Rosalba,
Pagano Eva,
Testa Silvia,
Zola Paolo,
di Cuonzo Daniela
Publication year - 2021
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/jep.13376
Subject(s) - missing data , imputation (statistics) , multivariate statistics , longitudinal data , statistics , data set , multivariate normal distribution , computer science , data mining , econometrics , mathematics
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.