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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Dunn, Michelle C
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Michigan Ann Arbor
Biostatistics & Other Math Sci
Organized Research Units
Ann Arbor
United States
Zip Code
Xia, Xi; Elliott, Michael R (2016) Weight Smoothing for Generalized Linear Models Using a Laplace Prior. J Off Stat 32:507-539
Zhou, Hanzhi; Elliott, Michael R; Raghunathan, Trivellore E (2016) Synthetic Multiple-Imputation Procedure for Multistage Complex Samples. J Off Stat 32:231-256
Zhou, Hanzhi; Elliott, Michael R; Raghunathan, Trviellore E (2016) A two-step semiparametric method to accommodate sampling weights in multiple imputation. Biometrics 72:242-52
Zhou, Hanzhi; Elliott, Michael R; Raghunathan, Trivellore E (2016) Multiple Imputation in Two-Stage Cluster Samples Using The Weighted Finite Population Bayesian Bootstrap. J Surv Stat Methodol 4:139-170
Dong, Qi; Elliott, Michael R; Raghunathan, Trivellore E (2014) Combining information from multiple complex surveys. Surv Methodol 40:347-354
Dong, Qi; Elliott, Michael R; Raghunathan, Trivellore E (2014) A nonparametric method to generate synthetic populations to adjust for complex sampling design features. Surv Methodol 40:29-46
Ha, Neung Soo; Lahiri, Partha; Parsons, Van (2014) Methods and results for small area estimation using smoking data from the 2008 National Health Interview Survey. Stat Med 33:3932-45
Chen, Shijie; Lahiri, P (2012) Inferences on Small Area Proportions. J Indian Soc Agric Stat 66:121-124
Chen, Qixuan; Elliott, Michael R; Little, Roderick J A (2012) Bayesian inference for finite population quantiles from unequal probability samples. Surv Methodol 38:203-214
Lahiri, P; Pramanik, Santanu (2011) Discussion of ""Estimating Random Effects via Adjustment for Density Maximization"" by C. Morris and R. Tang. Stat Sci 26:291-295

Showing the most recent 10 out of 11 publications