The power of testing a semi-parametric shared gamma frailty parameter in failure time data.
Frailty is usually modeled as an unobserved random variable acting multiplicatively on the baseline hazard function, where a shared unobserved quantity in the intensity induces a positive correlation among the observed failure times. Using the asymptotic properties of the nonparametric maximum likelihood estimator for the gamma frailty model, we derive a power function for testing the shared frailty parameter and evaluate the effect of number of groups and number of individuals per groups on the power of the test by simulation studies. The results show that choosing between 8 and 25 groups with sample sizes between 200 and 500 individuals will be enough to get a high power.
Department of Statistics and Computer, University of Social Welfare and Rehabilitation Sciences, Koodakyar Ave, Daneshjoo Blvd, Evin, Tehran 1985713831, Iran.
Mehdi
M
Rahgozar
Soghrat
S
Faghihzadeh
Gholamreza Babaee
GB
Rouchi
Yingwei
Y
Peng
D003198:Computer Simulation; D016039:Corneal Transplantation; D005260:Female; D006084:Graft Rejection; D006801:Humans; D008297:Male; D015233:Models, Statistical; D016016:Proportional Hazards Models; D018401:Sample Size