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The power of testing a semi-parametric shared gamma frailty parameter in failure time data
Mehdi Rahgozar1, Soghrat Faghihzadeh, Gholamreza Babaee Rouchi
1Department of Statistics and Computer, University of Social Welfare and Rehabilitation Sciences, Koodakyar Ave, Daneshjoo Blvd, Evin, Tehran 1985713831, Iran.
Statistics in Medicine
|June 14, 2008
Summary
This study introduces a power function for testing shared frailty in survival analysis. Simulation results indicate that 8-25 groups and 200-500 individuals ensure high statistical power for frailty models.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Frailty models are crucial in survival analysis for accounting for unobserved heterogeneity.
- Shared unobserved quantities in frailty models induce positive correlations in failure times.
- The gamma frailty model is a common approach for modeling this shared frailty.
Purpose of the Study:
- To derive a power function for testing the shared frailty parameter in gamma frailty models.
- To evaluate the impact of group numbers and individuals per group on test power via simulations.
Main Methods:
- Utilized asymptotic properties of the nonparametric maximum likelihood estimator (NPMLE).
- Developed a power function for hypothesis testing of the shared frailty parameter.
- Conducted simulation studies to assess power based on varying sample sizes and group numbers.
Main Results:
- A derived power function provides a tool for sample size determination in frailty studies.
- Simulation results demonstrate that sample sizes between 200 and 500 individuals are sufficient.
- Optimal power is achieved with a group count ranging from 8 to 25.
Conclusions:
- The study provides practical guidelines for designing survival studies employing gamma frailty models.
- Achieving high statistical power is feasible with moderate sample sizes and group numbers.
- The findings aid researchers in optimizing resource allocation for robust frailty analysis.
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