Chronic Disease Population Research Issues and Strategies

Fred Hutchinson Cancer Research Center, Seattle WA

Investigators

Linked publications & trials

Abstract

This project will address methodology development needs that arise in disease prevention trials and[unreadable] epidemiologic cohort studies. Our continuing work on failure time data methods will include sub-aims on[unreadable] multivariate survivor function estimation, on cohort and case-control estimation under a semiparametric[unreadable] normal transformation model, on attributable risk estimation for a preventive intervention, and on case-only[unreadable] estimation methods in a randomized controlled trial context. Our continuing work, motivated by dietary and[unreadable] physical activity epidemiology, on covariate measurement error methods will develop and compare[unreadable] estimation procedures based on biomarker data on subsets of a cohort, and self-report data on the entire[unreadable] cohort. Both recovery-type biomarkers, corresponding to the expenditure of a nutrient, and concentrationtype[unreadable] biomarkers, reflecting the concentration of a nutrient blood or another body compartment, will be[unreadable] considered. Our work on population science research issues and strategies will continue to contrast[unreadable] randomized controlled trial and observational study data, toward identifying sources of bias, with emphasis[unreadable] on both postmenopausal hormone therapy and dietary intervention, and with motivation and data derived[unreadable] from the Women's Health Initiative (WHI) clinical trial and cohort study. Efforts to elucidate postmenopausal[unreadable] hormone therapy effects in the WHI have led to a number of case-control studies using the WHI specimen[unreadable] repository, including genome-wide single nucleotide polymorphism (SNP) association studies of diseases[unreadable] that were adversely affected by estrogen plus progestin use. Aspects of the design and analysis of highdimensional[unreadable] SNP association studies is an additional Project 1 research aim. These aims will be addressed[unreadable] by using statistical models for disease risk, non-standard exposure measurement models, standard genetic[unreadable] models, asymptotic distribution theory development, computer simulations, and applications to important[unreadable] chronic disease data sets.