Biostatistics Seminar: Zhenke Wu, PhD, Johns Hopkins University

​Zhenke Wu

 Postdoctoral Fellow

Department of Biostatistics

Johns Hopkins University

“Bayesian Nested Partially-Latent Class Models for Multivariate Binary Data;

Estimating Disease Etiology”

  ​Abstract: This talk presents Bayesian hierarchical models developed to estimate from multiple sources of data the probability distribution of unknown disease status and trajectory for a population and for its individuals. The motivating application is the Pneumonia Etiology Research for Child Health (PERCH) study whose goal is to infer the distribution of pneumonia-causing pathogens from multiple peripheral measurements with variable precision. Our models are related to the latent class model but with partially known class membership. In a first model, we assume each child’s data is a draw from a mixture model for which each component represents one pathogen. Conditioned upon the unknown etiology, measurements are assumed independent of one another. In a second model, conditional dependence is induced by nesting latent subclasses within each etiology class. We use stick-breaking priors on the subclass weights to estimate the population and individual etiologic distributions. Finally, I will mention multiple recent extensions to allow for regression of the etiology distribution on covariates and to accommodate the possibility of etiologies involving multiple pathogens. We demonstrate the approach and specific models with simulated and PERCH data analysis. Light refreshments will be served at 3:00 pm.