An Application of Penalized Quasi-likelihood in Variable Selection on Parametric Accelerated Failure Time Models With Frailty
Document Type
Presentation
Presentation Date
3-2019
Abstract or Description
Variable selection has been administered in many studies of Biostatistics especially regarding large multi-center clinical trials that have high dimensional data points. The penalized likelihood is one of the most prominent methods for variable selection as it has shown a consistent and successful range of essential and necessary variables. Therefore, we aim to comparatively test these different penalty functions to conduct variable selection among parametric accelerated failure time (AFT) models with mixed effects (i.e., with a shared frailty parameter). The determination of the best estimates was done via mean sums of squares and the computational costs. We propose to use the penalized quasi-likelihood (PQL) approach with an induced penalty to our selection process. We used this method to compare with other penalty functions in mixed models and evaluated their performance under censoring.
Sponsorship/Conference/Institution
Eastern North American Region International Biometric Society Conference
Location
Philadelphia, PA
Recommended Citation
Pandeya, Sarbesh R., Lili Yu, Hani Samawi, Xinyan Zhang.
2019.
"An Application of Penalized Quasi-likelihood in Variable Selection on Parametric Accelerated Failure Time Models With Frailty."
Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Presentations.
Presentation 312.
https://digitalcommons.georgiasouthern.edu/bee-facpres/312
Additional Information
Georgia Southern University faculty members, Lili Yu, Hani Samawi, and Xinyan Zhang co-presented An Application of Penalized Quasi-likelihood in Variable Selection on Parametric Accelerated Failure Time Models With Frailty in the Eastern North American Region International Biometric Society Conference, March 2019.
Program Abstracts