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A Random-Effects Approach to Generalized Linear Mixed Model Analysis of Incomplete Longitudinal Data
Thuan Nguyen1, Jiangshan Zhang2, Jiming Jiang2
1OHSU-PSU School of Public Health, Oregon Health and Science University, Portland, Oregon, USA.
Statistics in Medicine
|December 2, 2025
Summary
This study introduces a novel random-effects method to handle missing data in generalized linear mixed models (GLMMs) for longitudinal analysis. The approach simplifies analysis by converting models with missing covariates into standard GLMMs, improving data handling in healthcare research.
Area of Science:
- Statistics
- Biostatistics
- Longitudinal Data Analysis
Background:
- Missing data is a common challenge in longitudinal studies.
- Generalized linear mixed models (GLMMs) are widely used for analyzing such data.
- Existing methods for handling missing data can be complex or computationally intensive.
Purpose of the Study:
- To propose a novel random-effects approach for addressing missing values in GLMMs.
- To simplify the analysis of longitudinal data with missing covariates.
- To provide a theoretically justified and empirically validated method.
Main Methods:
- A random-effects approach is proposed to convert GLMMs with missing covariates into GLMMs without missing covariates.
- The method is applicable to linear mixed models (LMMs) and logistic regression.
- Performance is evaluated through simulation studies and compared with multiple imputation (MI) using MICE.
Main Results:
- The proposed method effectively handles missing covariates in GLMMs.
- Empirical evaluations demonstrate competitive or superior performance compared to multiple imputation.
- Theoretical justifications align with simulation findings.
Conclusions:
- The random-effects approach offers a viable and efficient alternative for analyzing longitudinal data with missing values.
- This method facilitates the use of standard GLMM analysis tools.
- The approach is demonstrated with real-world healthcare data examples.
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