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Multivariate linear mixed models with censored and nonignorable missing outcomes, with application to AIDS studies
Tsung-I Lin1,2, Wan-Lun Wang3
1Institute of Statistics, National Chung Hsing University, Taichung, Taiwan.
Biometrical Journal. Biometrische Zeitschrift
|June 20, 2022
Summary
This study introduces a new statistical model for complex longitudinal data, handling both censored and missing values effectively. The method improves analysis for clinical trials with non-ignorable missing data.
Area of Science:
- Statistics
- Biostatistics
- Longitudinal Data Analysis
Background:
- Multivariate longitudinal data analysis faces challenges from data censoring and nonresponse.
- Existing methods may not adequately address simultaneous censored responses and non-ignorable missing outcomes.
Purpose of the Study:
- To generalize the multivariate linear mixed model for handling censored responses and non-ignorable missing data concurrently.
- To develop a robust statistical framework for analyzing complex longitudinal datasets.
Main Methods:
- A selection approach was used to model non-ignorable missingness by decomposing joint distributions.
- A computationally feasible Monte Carlo Expectation Conditional Maximization algorithm was developed for maximum likelihood (ML) parameter estimation.
- An information-based approach was presented for assessing the variability of ML estimators.
Main Results:
- The proposed generalized multivariate linear mixed model effectively accommodates both censored responses and non-ignorable missing outcomes.
- The developed Monte Carlo EM algorithm provides a feasible method for parameter estimation.
- The study demonstrated the utility of the methodology with HIV-AIDS clinical trial data.
Conclusions:
- The new statistical approach offers a powerful tool for analyzing complex longitudinal data in clinical research.
- The methodology provides reliable methods for prediction of censored responses and imputation of missing outcomes.
- Simulation studies confirmed the superior performance of the proposed method over traditional approaches.
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