Upcoming Seminar:
Analysis of Cohort Studies with Multivariate, Partially Observed Disease Classification Data
Speaker:
Nilanjan Chatterjee,
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,
National Institute of Health
Abstract:
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 (EE) approach for handling missing cause of failure in competing-risk data.
We prove asymptotic unbiasedness of such an EE method under general missing-at-random (MAR)
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.
Time: Friday, April 17, 2009, 11:00-12:00 pm
Location: Monroe Hall, Room 113 (2115 G Street, NW, Washington, DC 20052)
Directions
Foggy Bottom-GWU Metro Stop on the Orange and Blue Lines.
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