Grant Number:	4R01CA129101-03 Interpret this number
Primary Investigator:	Elliott, Michael
Organization:	University Of Michigan At Ann Arbor
Project Title:	Hierarchical Bayesian Analysis of Complex Sample Survey Data
Fiscal Year:	2011

DESCRIPTION (provided by applicant): The proposed research will use hierarchical Bayesian modeling to tackle three interrelated problems in the analysis of population-based survey data: accounting for unequal probabilities of inclusion due to sample design or post-sampling non-response; accounting for non-ignorable missingness in item-level data; and combining information from multiple complex survey data sets to obtain more accurate and efficient estimates of the population quantities. We intend to develop robust models that can provide "data- driven" weight trimming procedures for a general class of population statistics under a variety of sample designs; develop selection models that accommodate non-ignorable missingness mechanisms in the context of complex survey designs; and develop methods to combine data from multiple surveys by creating synthetic populations from each survey and then combining these populations to develop estimates. While our methods will be applicable to a wide variety of analytic procedures, we will focus on small area or small domain estimation in particular, since the issues that this proposal intends to address are often most acute in the setting. Domain estimators with highly variable weights can have poor mean square error properties. Associations between nonignorable nonresponse and areas/domains can make between-domain comparisons unreliable. Small samples in a given domain in one survey can be compensated by data from other surveys, if correct procedures are in place to account for complex sample design, as well as the possibility of non-response bias and measurement error. We will consider three major applications: analyses to determine associations between birth weight and cardiovascular risk factors in children using the National Health and Nutrition Examination Survey, to determine the prevalence of cancer behavioral risk factors among adults by combining data from the Behavioral Risk Factor Surveillance Survey and the National Health Interview Survey, and to explore mortality compression among the elderly in the Americans Changing Lives panel survey. Analyses will focus on small domains (race/ethnic minorities, and counties/states, as examples). Though the method is motivated from a Bayesian perspective, the results will be evaluated from the design-based perspective using analytical and simulation techniques. We will also focus on developing user-friendly software to implement the new methods. PUBLIC HEALTH RELEVANCE: In an increasingly diverse nation, the need is increasing to target public health studies and delivery to small areas, be they geographic or demographic (such as ethnic minorities). Health surveys are a rich source of data for such efforts, but methods for extracting information about small areas remain undeveloped. The proposed work will develop new methods for dealing with some of the problems that small area estimation poses, including unstable estimates due to small sample sizes and unequal probabilities of selection, and biased estimates due to differences between people who chose to participate in the surveys and those who refused or could not be contacted. The work can also improve the efficient use of data currently collected by developing new ways of combining data from multiple surveys.





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