Date of Award

1-1-2015

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Epidemiology and Biostatistics

Program

Biostatistics

Content Description

1 online resource (x, 77 pages) : illustrations.

Dissertation/Thesis Chair

Albert G. DiRienzo

Committee Members

Recai M. Yucel, Margaret A. Gates

Keywords

Variables (Mathematics), Failure time data analysis, Survival analysis (Biometry), Censored observations (Statistics)

Subject Categories

Biostatistics

Abstract

Variable selection is fundamental in any kind of statistical modeling. There has been ex- tensive research by different authors on methods of variable selection from linear regression models to more complex non-linear applications. Modeling survival data especially poses challenges because of a more complicated data structure as the time variable T is usually subject to censoring. This thesis presents a two step objective approach to choose between several candidate models based on the the ability of the model to predict survival times using loss functions. Once potentially important variables are selected using a screening method called Iterative Sure Independence Screening(ISIS) the method attempts to select a parsimonious model by using multiple hypothesis testing using generalized family wise error rate to compare models based on estimates of average prediction error. Inverse probability weighted complete case estimator (IPWCC) is used for the calculation of average prediction error. Several simulation studies and a case analysis of the Mayo Clinic Primary Biliary Cirrhosis (PBC) data is also provided .

Download

Included in

Biostatistics Commons

Share

COinS