Improving clinical reality in chronic obstructive pulmonary disease economic modelling: Development and validation of a micro-simulation approach

Background: Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible disease responsible for the deaths of 3 million people worldwide in 2005, and predicted to be the third leading cause of death worldwide by 2030. Many COPD models developed to date have followed a Markov structure, in which patients or populations can move between defined health states over successive time periods or cycles. In COPD, health states are typically based on disease severity defined solely by lung function, as described by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. These current modelling methods may restrict the ability to reflect the disease progression/clinical pathway or clinical practice. Objectives: Given these limitations in previous COPD models, the authors aimed to develop a more flexible model that could improve on the description of the clinical disease pathway. The overall objective of this model was to inform the development of policies, guidelines or cost-effectiveness analyses. A second objective was to validate the model in relation to existing epidemiology studies of COPD. Methods: A patient simulation model was developed in Microsoft Excel™. The predictability of the model was tested by populating it with data from natural history of disease studies as well as with clinical trial data. Each patient moves through the model with demographic characteristics randomly generated from a set distribution. These characteristics determine the risk of clinical events occurring in the model. Results: The validation with these studies found the model to have generally good predictive ability, yielding in this way a good degree of external validity. Conclusions: The micro-simulation model is a flexible approach for modelling COPD that allows consideration of complex COPD treatment pathways. The model was found to be generally robust in terms of predicting clinical outcomes of published studies when tested against other studies. It has significant potential as a tool for supporting future COPD treatment positioning decisions as well as to inform the development of policies, guidelines or cost-effectiveness analyses.