220885 Semiparametric Regression Inference for Tumor Progression in Cross-sectional Cancer Studies

Monday, November 8, 2010 : 12:41 PM - 12:52 PM

Chen Hu , Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI
Alex Tsodikov , Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI
In cancer studies, to develop better treatment and screening programs, it is of great interest to understand the natural history of the disease and what factors affect its progression. Methods for assessing covariate effects on the joint response of age and stage of cancer at diagnosis are our main focus. We address this question through a semiparametric regression model for stage-specific cancer incidence. Such data structure requires a joint model for correlated survival and binary data. Constructed through a series of semiparametric regression models with time-dependent covariates, our model can be represented as a transformation model induced by a complex non-proportional frailty. We develop maximum likelihood estimation and inference procedures. The methodology is illustrated by simulation studies and real prostate cancer data from the Surveillance, Epidemiology and End Results (SEER) program.

Learning Areas:
Biostatistics, economics
Chronic disease management and prevention

Learning Objectives:
Assess covariate effects on tumor progression in cross-sectional cancer studies

Keywords: Cancer Prevention, Biostatistics

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a Graduate Student Research Assistant working on this.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.