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Mixed-effects beta regression for modeling continuous bounded outcome scores using NONMEM when data are not on the
Xu Steven Xu1, Mahesh N Samtani, Adrian Dunne
1Model-Based Drug Development, Janssen Research & Development, 920 Route 202, Raritan, NJ, USA. sxu26@its.jnj.com
Journal of Pharmacokinetics and Pharmacodynamics
|May 7, 2013
Summary
NONMEM can now implement mixed-effects beta regression models for bounded clinical data using Nemes
Area of Science:
- Pharmacometrics and Clinical Pharmacology
- Biostatistics and Computational Biology
Background:
- Beta regression models are suitable for bounded continuous outcome scores common in clinical research.
- Implementing these models in NONMEM is challenging due to the absence of built-in gamma functions required for the beta distribution.
- Existing methods often rely on approximations or external software, necessitating an evaluation of NONMEM's capabilities.
Purpose of the Study:
- To implement mixed-effects beta regression models in NONMEM utilizing Nemes' approximation for the gamma function.
- To assess the performance of this NONMEM implementation by comparing it with the established SAS approach.
- To evaluate the accuracy, bias, and computational efficiency of the NONMEM implementation for clinical trial data analysis.
Main Methods:
- Mixed-effects beta regression models were implemented in NONMEM using Nemes' approximation for the gamma function.
- Monte Carlo simulations generated continuous outcomes (0-70) based on a beta regression model in an Alzheimer's disease context.
- Simulations involved 250 subjects with 6 samples per subject over 3 years, repeated for 1000 trials, and compared with SAS using adaptive Gaussian quadrature.
Main Results:
- The NONMEM implementation with Nemes' approximation showed only slightly higher bias and relative Root Mean Square Error (RMSE) compared to SAS.
- Differences in bias and RRMSE for fixed effects, random intercept, and precision parameters were <1-3%; for random slope, <3-7%.
- NONMEM demonstrated significantly shorter run times (1-2 seconds) compared to SAS (20-40 seconds) for the analyzed model and data.
Conclusions:
- NONMEM, with Nemes' approximation of the gamma function, provides comparable parameter estimates to SAS for mixed-effects beta regression models.
- The NONMEM implementation offers a computationally efficient alternative for analyzing bounded continuous outcomes in clinical studies.
- This approach is suitable for modeling disease progression, as demonstrated in the Alzheimer's disease cognitive assessment scale analysis.
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