A comparison of statistical methods in interrupted time series analysis to estimate an intervention effect
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 flawed for various reasons. In a more general context, researchers want to estimate whether and how a policy intervention changed an outcome of interest. Quasi-experimental interrupted time series (ITS) is the most appropriate design to evaluate the longitudinal effects of policy interventions and segmented regression analysis is often used as a powerful statistical method for ITS. Recent research has employed a log-linear regression model for the hospital admission counts of head and limb injuries from New South Wales, Australia, from a 36 month period centred at the time of legislation. Estimation of the model was done using a frequentist approach. In this paper, we re-analyse this data using empirical Bayes and full Bayesian methods, since the use of these methods has become popular in road safety studies. In particular, we show how a full Bayesian method can be readily implemented in WinBUGS software. We discuss the advantages and disadvantages of each method and describe and compare the different estimation methods in terms of parameter estimates. The results show that all three estimation methods give consistent conclusions regarding the positive effect of compulsory helmet wearing on cyclist head injuries in New South Wales.