Seminar Details

Seminar Details


May 13

1:00 pm

Bayesian Modeling of Survey Data in Space and Time

Cici Chen

General Exam

University of Washington - Department of Statistics

Advisor: Jon Wakefield

Public health data are frequently obtained from surveys, which often have complex design sampling frames. It is crucial that analyses account for the latter to give appropriate inference. We describe two scenarios, with both having important spatial components.

The first example is motivated by Behavioral Risk Factor Surveillance System (BRFSS) data. Empirical Bayes and Bayes hierarchical models for small area estimation have been used extensively for surveys like BRFSS. However, the sampling weights that are required to reflect complex surveys are rarely considered in these models. We describe a method to incorporate the sampling weights for binary data when estimating, for example, small area proportions or predicting small area counts. We consider models that include spatial random effects, with computation carried out using the integrated nested Laplace approximation, which is fast. A simulation study is presented, to demonstrate the performance of the proposed approaches, and to compare results from models with and without the sampling weights. The results show that estimation mean squared error can be greatly reduced using the proposed models, when compared with more standard approaches. Bias reduction occurs through the incorporation of sampling weights, with variance reduction being achieved through hierarchical smoothing.

The second example is motivated by data on Hand-foot-mouth disease (HFMD) collected in China between 2008 and 2010. HFMD is an acute contagious viral infection that has caused large-scale outbreaks in Asia during the past decade. Very little is known about the etiology of the viral strains causing HFMD, the factors associated with its spread, or effective means of public health intervention. With the aid of lab test data, we propose a Bayesian hierarchical model to investigate the strain-specific probabilities of contracting HFMD, and the strain-specific probabilities of being a clinically severe case. We are also interested in how these probabilities evolve over time and space.