Department of Statistics Seminar
North Carolina State University

presents

Dr. Jason P Fine

University of Wisconsin-Madison

"Comparing Non-nested Cox Models"

ABSTRACT

This talk will focus on choosing between two, possibly non-nested proportional hazards models using the partial likelihood ratio test. I will present the limiting distribution of the test under general conditions. The multiplicative hazards models being fitted may be misspecified and the true model is not assumed to be contained by either of the fitted models. The null hypothesis is that the models are equidistant in Kullback-Leibler distance applied to the rank likelihood. The ratio statistic is consistent for the model which is closer to the truth. However, its distribution takes one of two forms and depends on the unknown data generating mechanism, which complicates inference. A two-step testing procedure is proposed for which is valid regardless of the true model. The first step involves a novel test for the equality of the fitted models which is separate from the partial likelihood. The methodology has important applications in model assessment. A reanalysis of the well-known PBC data will be used to demonstrate its utility in selecting the functional forms of covariates and relative risks.

Friday, February, 20, 2004

3:35 - 4:35 pm

206 Cox Hall

Refreshments will be served on the second floor of Dabney Hall (left of Room 222) at 3:00 pm.