EcoSta 2019: Start Registration
View Submission - EcoSta2019
Title: Regression analysis of dependent binary data for estimating disease etiology from case-control studies Authors:  Zhenke Wu - University of Michigan (United States) [presenting]
Abstract: In large-scale disease etiology studies, epidemiologists often need to use multiple imperfect binary measures of unobserved causes of disease to estimate cause-specific case fractions, or ``population etiologic fractions" (PEFs). Despite recent methodological advances, the scientific need of incorporating control data to estimate the effect of explanatory variables upon the PEFs remains unmet. We build on and extend the nested partially-latent class model npLCM to a general framework for etiology regression analysis in case-control studies. Data from controls provide requisite information about measurement specificities and covariations to correctly assign cause-specific probabilities for each case given her measurements. We estimate the distribution of the controls' diagnostic measures given the covariates via a separate regression model and a priori encourage simpler dependence structures. We use Markov chain Monte Carlo for posterior inference of the PEF functions, cases' latent classes and the overall PEFs of policy import. We illustrate the regression analysis via simulations and show less biased estimation and more valid inference of the overall PEFs than an npLCM analysis omitting covariates. Regression analysis of data from a childhood pneumonia study site reveals the dependence of pneumonia etiology upon season, age, disease severity and HIV status.