To overcome these drawbacks and obtain more accurate evaluation of treatment effect on COPD exacerbations, alternative analytical methods based, for example, on predictive mathematical models such as hidden Markov chains or Bayesian forecasting should be tried. Such models can characterise and predict rare events without undertaking a full-scale, long-term longitudinal study. This approach to predicting rare events has been used previously in studies of migraine, epilepsy and various cardiovascular diseases where the size of treatment effect is measured in terms of a reduction in the frequency of the repetition of an event within a given probability or within a given time period [54, 55] . One example of a mathematical model development includes the use of a Markov model to predict COPD exacerbation rates in a clinical trial of the inhaled anticholinergic bronchodilator tiotropium [56] . In this example, the model was developed on the basis of prior knowledge of the exacerbation rate as estimated from meta-analyses of randomised controlled trial data. This gave the probabilities for COPD exacerbations for different stages of COPD. In another study, a proportional hazards model was used to identify risk factors for COPD patients hospitalised due to an exacerbation [44] . The current ATS/ERS guidelines for exacerbations do not consider the implications of using probabilistic models as a means of assessing the severity of COPD exacerbations or the effect of treatment [1] . A modelling approach may offer new insights into which variables related to COPD exacerbations should be investigated.