Since the introduction of mandatory helmet legislation (MHL) in Australia, debate on the effect of MHL on cyclist head injuries has been ongoing. The debate sometimes revolves around the statistical methodology used to assess intervention effectiveness. Supporters of rescinding the MHL thereby encouraging cyclists to ride without helmets, regularly dismiss statistical evaluations as being flawe...

In this paper, we investigate the use of Bayesian modeling and Markov chain Monte Carlo (MCMC) simulation, via the software WinBUGS, to project future mortality for populations with limited data. In particular, we adapt some extensions of the Lee-Carter method under the Bayesian framework to allow for situations in which mortality data are scarce. Our approach would be useful for certain develo...

This study demonstrated the equivalence between the Rasch testlet model and the three-level one-parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE) with the expectation-maximization algorithm in Con...

Bayesian inference of model parameters for the time dependent carbonation depth of concrete is presented. The statistical model is assumed to have a form of where implies the carbonation coefficient and stands for the modeling error which is assumed to have a normal distribution with zero mean and unknown variance. By the use of existing data for the natural outdoor exposed concrete, the poster...

Random effects models are used in many applications in medical statistics, including meta-analysis, cluster randomized trials and comparisons of health care providers. This paper provides a tutorial on the practical implementation of a flexible random effects model based on methodology developed in Bayesian non-parametrics literature, and implemented in freely available software. The approach i...

An approach called generalizability in item response modeling (GIRM) is introduced in this article. The GIRM approach essentially incorporates the sampling model of generalizability theory (GT) into the scaling model of item response theory (IRT) by making distributional assumptions about the relevant measurement facets. By specifying a random effects measurement model, and taking advantage of ...

In this report the authors evaluate epidemiological evidence of thyroid cancer cases in a highly radiated territory in Poland--i.e. in the province of Opole after 1986. The analysis - based on logistic regression modelling of registered thyroid cancer and an ecological covariate (137Cs concentration)--shows differences in incidence in six distinguished radiation zones and two equal periods. The...

During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constrai...

The aim of the study is to determine a measurement invariance cut-off point based on item parameter differences in Bayesian Item Response Theory Models. Within this scope, Bayes factor estimated for testing invariance. For purpose, simulation conducted. data were generated R software each condition under one-parameter logistic model 10 binary (1-0 scored) items. test was performed various group...