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Joint modeling of zero-inflated panel count and severity outcomes.
E Juarez-Colunga1, G L Silva2, C B Dean3
1Department of Biostatistics and Informatics, University of Colorado Denver, Aurora, Colorado 80045, U.S.A.
Biometrics
|March 18, 2017
Summary
This study introduces a joint modeling approach for analyzing event counts and their severity in longitudinal studies. The joint model demonstrates higher power in detecting treatment effects compared to traditional scoring methods.
Area of Science:
- Biostatistics
- Longitudinal Data Analysis
- Clinical Trials
Background:
- Longitudinal studies often record event counts over time.
- Methods are needed to analyze both event counts and their associated severity marks.
- Existing methods may face challenges in isolating treatment effects.
Purpose of the Study:
- To develop and evaluate a joint modeling approach for panel counts and their severity.
- To compare the power of joint modeling against a scoring approach for detecting treatment effects.
- To investigate the association between event counts and their severities in clinical applications.
Main Methods:
- Joint modeling of event counts and severity marks using shared random effects.
- Simulation studies to compare statistical power.
- Markov chain Monte Carlo (MCMC) methods for inference.
- Application to a study on hormone therapy for vasomotor symptoms.
Main Results:
- The joint modeling approach shows higher statistical power for detecting treatment effects than the scoring approach.
- The study quantifies challenges associated with the scoring approach in isolating treatment effects.
- Demonstrates the utility of shared random effects in modeling count and severity data.
Conclusions:
- Joint modeling provides a more powerful method for analyzing longitudinal count and severity data.
- The proposed methods are effective for studies involving associated event counts and severities.
- This approach offers advantages over traditional scoring methods in clinical trial analysis.
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