Sensitivity analysis of state-transition models: how to deal with a large number of inputs

State-transition models are employed to project future prevalence rates of risk factors and diseases within populations. Sensitivity analysis should be performed to assess the reliability of the results but often the number of inputs of the model is so huge, and running the model is so time-consuming, that not all methods of sensitivity analysis are practically available. Screening methods detect which inputs have a major influence on the outputs. We briefly review the available screening methods, and discuss one in particular, Morris' OAT Design. We applied the method under different assumptions to a module of the RIVM Chronic Diseases Model, where we projected the rates of never smokers, former smokers and current smokers in time up to the year 2050, based on smoking rates, start, stop and quit rates from 2003 and information on selective mortality in smokers from the literature. Different assumptions with regard to the interval of the inputs used for screeing led to different conclusions, especially with regard to the importance of quit and relapse rates versus initial prevalence rates. This should not to be read as a lack of validity of the method, but it shows that any sensitivity method cannot be automated in a form that runs without expert guidance on the ranges.