Recent developments in decision-analytic modelling for economic evaluation

The past few years have seen rapid changes in the methods of decision-analytic modelling of healthcare programmes for the purposes of economic evaluation. This paper focuses on four developments in modelling that have emerged over the past few years or have become more widely used. First, no one optimal method for extrapolating outcomes from clinical trials has yet been established. Modellers may draw from a set of varied assumptions about survival extrapolation that encompass a range of possibilities from highly optimistic to extremely cautious. Secondly, the practicality and appeal of microsimulation as a method for analysing healthcare decision problems has increased dramatically with the speed of computing technology. Individual instantiations of a system are generated by using a random process to draw from probability distributions a large number of times (also known as Monte Carlo or probabilistic simulation). Microsimulation is moving in new directions, such as discrete-event simulations that simulate sequences of events by drawing directly from probability distributions of event times; this approach is now being broadly applied to model situations where populations of patients interact with healthcare delivery systems. Microsimulation modelling of transmission systems at the population level is also rapidly developing. Thirdly, model calibration is emerging as a new tool that may offer health scientists a means of generating important fundamental knowledge about disease processes. Model calibration allows evidence synthesis in which observations on observable quantities are used to draw inferences about unobservable quantities. The methodology of model calibration has advanced considerably, drawing on theories of numerical analysis and mathematical programming such as gradient methods, intelligent grid search algorithms, and many more. As a fourth issue, an area of extraordinary activity is in the use of transmission models to analyse interventions for infectious diseases, including population-wide effects of vaccination. Transmission models use differential equations to simulate, deterministically for the most part, transitions among infection-related health states. Only recently have modelling methodologies been combined so that cost-effectiveness analyses can consider explicitly not only the patient-level benefits of interventions but also the secondary benefits through transmission dynamics. Advances in technology allow more realistic and complex healthcare models to be simulated more rapidly. However, decision makers will not readily accept results from models unless they can understand them intuitively and explain them to others in relatively simple terms. The challenge for the next generation of modellers is not only to harness the power available from these newly accessible methods, but also to extract from the new generation of models the insights that will have the power to influence decision makers.