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An approximate joint model for multiple paired longitudinal outcomes and time-to-event data
Angelo F Elmi1, Katherine L Grantz2, Paul S Albert3
1Department of Epidemiology and Biostatistics, The Milken Institute School of Public Health at The George Washington University, Washington, D.C. 20052, U.S.A.
Biometrics
|March 2, 2018
Summary
This study introduces a computationally simpler method for analyzing complex paired longitudinal and time-to-event data. The approach effectively handles missing data, offering a practical alternative for joint modeling in biomedical research.
Area of Science:
- Biostatistics
- Longitudinal Data Analysis
- Survival Analysis
Background:
- Joint modeling of longitudinal and time-to-event data is computationally intensive.
- High-dimensional random effects are needed to capture complex correlations.
- Existing methods face challenges with computational complexity.
Purpose of the Study:
- To propose a computationally simpler estimation approach for complex shared parameter models.
- To address challenges in joint modeling of multivariate paired longitudinal and time-to-event data.
- To impute missing data using a Conditional Linear Model (CLM) approximation.
Main Methods:
- Utilizing Posterior Predictive Distribution for missing data imputation.
- Employing a Conditional Linear Model (CLM) approximation.
- Applying existing methods for complete data to estimate event time model parameters.
Main Results:
- The proposed method offers a computationally feasible alternative to full likelihood estimation.
- Demonstrated application in analyzing discordant fetal growth and birth timing in twins.
- Simulations indicate good performance with moderate measurement errors under CLM approximations.
Conclusions:
- The novel imputation method simplifies joint modeling of complex correlated data.
- This approach is valuable for analyzing paired longitudinal and survival data, particularly in obstetrics.
- The method shows promise for handling measurement error in complex statistical models.
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