Strategies for handling missing data in longitudinal studies with questionnaires

DOI10.1080/00949655.2018.1520854OpenAlexW2890801399MaRDI QIDQ4960771

Nazanin Nooraee, Johan Ormel, Geert Molenberghs, Edwin R. van den Heuvel

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2018.1520854

zbMATH Keywords

maximum likelihoodmultiple imputationlatent variable modelsfully conditional specification

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• SAS
• PROC NLMIXED
• SAS/STAT
• MIANALYZE

Cites Work

• Unnamed Item
• The comparative efficacy of imputation methods for missing data in structural equation model\-ing.
• Asymptotic properties of maximum weighted likelihood estimators.
• Analyzing incomplete discrete longitudinal clinical trial data
• Combining Multiple Imputation and Inverse‐Probability Weighting
• Robust weighted likelihood estimators with an application to bivariate extreme value problems
• Inference and missing data
• Multiple Imputation and its Application
• Informative Drop-Out in Longitudinal Data Analysis
• Robust Estimation: A Weighted Maximum Likelihood Approach
• A Potential for Bias When Rounding in Multiple Imputation
• Multiple imputation of discrete and continuous data by fully conditional specification
• Evaluation of software for multiple imputation of semi-continuous data
• Missing-Data Methods for Generalized Linear Models
• Longitudinal Data Analysis

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