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

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 n...

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Bibliographic Details
Main Authors: Mina Jahangiri, Anoshirvan Kazemnejad, Keith S. Goldfeld, Maryam S. Daneshpour, Shayan Mostafaei, Davood Khalili, Mohammad Reza Moghadas, Mahdi Akbarzadeh
Format: Article
Language:English
Published: BMC 2023-07-01
Series:BMC Medical Research Methodology
Subjects:
Single imputation
Multiple imputations
Missing longitudinal data
Longitudinal regression tree
Online Access:https://doi.org/10.1186/s12874-023-01968-8

Internet

https://doi.org/10.1186/s12874-023-01968-8

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