"Analysis of Cohort Studies with Multivariate, Partially Observed, Disease Classification Data"
October 6, 2009 @ 3:30 pm - 4:30 pm
Nilanjan Chatterjee, Ph.D., NCI/NIH
Location: BRB - 251
TITLE: Analysis of Cohort Studies with Multivariate, Partially Observed, Disease Classification Data
Complex diseases, like cancer, can often be classified into subtypes using various pathological and molecular traits of the disease. In this article, we develop methods for analysis of disease incidence in cohort studies incorporating data on multiple disease traits using a two-stage semi-parametric Cox proportional hazard regression model that allows one to examine the heterogeneity in the effect of the covariates by the levels of the different disease traits. For inference in the presence of missing disease traits, we propose a generalization of an estimating-equation approach for handling missing cause of failure in competing-risk data. We prove asymptotic unbiasedness of the estimating-equation method under general missing-at-random assumption and propose a novel influence-function based sandwich variance estimator. The methods are illustrated using simulation study and a real data application involving the Cancer Prevention Study (CPS-II) nutrition cohort.
This is a joint work with Professor Samiran Sinha, Texas A&M University, College Station.