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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