Uncertainty and validation of health economic decision models.

Health economic decision models are based on specific assumptions relating to model structure and parameter estimation. Validation of these models is recommended as an indicator of reliability, but is not commonly reported. Furthermore, models derived from different data and employing different assumptions may produce a variety of results.A Markov model for evaluating the long-term cost-effectiveness of screening for abdominal aortic aneurysm is described. Internal, prospective and external validations are carried out using individual participant data from two randomised trials. Validation is assessed in terms of total numbers and timings of key events, and total costs and life-years. Since the initial model validates well only internally, two further models are developed that better fit the prospective and external validation data. All three models are then extrapolated to a life-time horizon, producing cost-effectiveness estimates ranging from pound1600 to pound4200 per life-year gained.Parameter uncertainty is now commonly addressed in health economic decision modelling. However, the derivation of models from different data sources adds another level of uncertainty. This extra uncertainty should be recognised in practical decision-making and, where possible, specifically investigated through independent model validation

Estimation of life‐years gained and cost effectiveness based on cause‐specific mortality

Cost-effectiveness analysis is usually based on life‐years gained estimated from all‐cause mortality. When an intervention affects only a few causes of death accounting for a small fraction of all deaths, this approach may lack precision. We develop a novel technique for cost‐effectiveness analysis when life‐years gained are estimated from cause‐specific mortality, allowing for competing causes of death. In the context of randomised trial data, we adjust for other‐cause mortality combined across randomised groups. This method yields a greater precision than analysis based on total mortality, and we show application to life‐years gained, quality‐adjusted life‐years gained, incremental costs, and cost effectiveness. In multi‐state health economic models, however, mortality from competing causes is commonly derived from national statistics and is assumed to be known and equal across intervention groups. In such models, our method based on cause‐specific mortality and standard methods using total mortality give essentially identical estimates and precision. The methods are applied to a randomised trial and a health economic model, both of screening for abdominal aortic aneurysm. A gain in precision for cost‐effectiveness estimates is clearly helpful for decision making, but it is important to ensure that ‘cause‐specific mortality’ is defined to include all causes of death potentially affected by the intervention. Copyright (C) 2010 John Wiley & Sons, Ltd.cost‐effectiveness analysis , life‐years gained , competing risks , cause‐specific mortality ,