A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis.

ABSTRACT:

Background

Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of the longitudinal regression tree algorithm as a non-parametric method after imputing missing data using SI and MI was investigated using simulated and real data.

Method

Using different simulation scenarios derived from a real data set, we compared the performance of cross, trajectory mean, interpolation, copy-mean, and MI methods (27 approaches) to impute missing longitudinal data using parametric and non-parametric longitudinal models and the performance of the methods was assessed in real data. The real data included 3,645 participants

SUBMITTER: Jahangiri M 

PROVIDER: S-EPMC10327316 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature