Constructing Statistical Intervals for Small Area Estimates Based on Generalized Linear Mixed Model in Health Surveys
Constructing Statistical Intervals for Small Area Estimates Based on Generalized Linear Mixed Model in Health Surveys作者机构：Division of Population Health National Center for Chronic Disease Prevention and Health Promotion Centers for Disease Control and Prevention Atlanta GA USA Office of Compensation and Working Conditions U.S. Bureau of Labor Statistics Washington DC USA
出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)
年 卷 期：2022年第12卷第1期
页 面：70-81页
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：Bayesian Estimation Behavioral Risk Factor Surveillance System Bootstrapping Monte Carlo Simulation Small Area Estimation
摘 要：Generalized Linear Mixed Model (GLMM) has been widely used in small area estimation for health indicators. Bayesian estimation is usually used to construct statistical intervals, however, its computational intensity is a big challenge for large complex surveys. Frequentist approaches, such as bootstrapping, and Monte Carlo (MC) simulation, are also applied but not evaluated in terms of the interval magnitude, width, and the computational time consumed. The 2013 Florida Behavioral Risk Factor Surveillance System data was used as a case study. County-level estimated prevalence of three health-related outcomes was obtained through a GLMM;and their 95% confidence intervals (CIs) were generated from bootstrapping and MC simulation. The intervals were compared to 95% credential intervals through a hierarchial Bayesian model. The results showed that 95% CIs for county-level estimates of each outcome by using MC simulation were similar to the 95% credible intervals generated by Bayesian estimation and were the most computationally efficient. It could be a viable option for constructing statistical intervals for small area estimation in public health practice.
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