Decision analytic models, sensitivity analysis and value of information in economic evaluations in health care

Economic evaluations of health care technologies are now commonly carried out to assess the economic value of new pharmaceuticals, medical devices and procedures. The aim of health economic evaluations is to measure, value and compare the costs and benefits of different health care interventions. To date, cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) are the two types of economic evaluations that are applied in the vast majority of economic evaluation studies. Cost-effectiveness estimates in CEAs and CUAs can either be derived from data collected alongside a randomized controlled trial or by means of decision analytic modelling. In situations where evidence from a trial is (e.g. short time horizon of the trial; small sample size), decision analytic modelling provides a structure within which evidence from a range of sources can be directed at a specific decision problem for a defined population and context. Decision analytic models use mathematical relationships to define a series of possible consequences. Based on the inputs of the model, the likelihood of each consequence is expressed in terms of probabilities. It is thus possible to calculate the expected costs and expected outcome for different interventions analyzed in the model. In the first study the cost-effectiveness of extended prophylaxis with fondaparinux of one month versus one week in patients undergoing hip fracture surgery and total hip replacement was analysed. The analysis was based on a decision tree, using a time horizon of 30 days and 5 years. In this CEA the health effect was measured in life-years gained. Depending on the patient population and the time horizon, the extended prophylaxis with fondaparinux was found to be cost-effective or cost-saving. In the second study the cost-effectiveness of risedronate was examined for Swiss osteoporotic women. In this study we developed a time-dependent Markov model to examine the cost-effectiveness of risedronate for women who start a 5 year risedronate therapy between 60 and 90 years of age. This CUA was carried out from a Swiss health care perspective. For osteoporotic women or women with severe osteoporosis we found that risedronate treatment is cost-effective. As expected, the cost-effectiveness estimate is influenced by the patients’ age and disease severity. Two chapters of this thesis are based on a cost-utility analysis of 2 drug-eluting stents (the sirolimus- and the paclitaxel-eluting stent; DES) compared to bare metal stents (BMS). Although DES are now used for several years, concerns remain about their long term safety. Given the threefold higher acquisition costs, it was unclear whether DES are cost-effective when compared to BMS. Based on clinical data with 3-year follow-up we developed a Markov CUA model to shed light on this question. Both DES under analysis were found to not be cost-effective from a US Medicare payer’s perspective. In a further study the decision uncertainty was examined in full depth. With expected value of perfect information (EVPI) analysis total decision uncertainty was assessed. EVPI provides the value a rational decision maker should be willing to spend in order to acquire perfect information. Through expected value of partial perfect information analysis the contribution of groups of parameters towards total decision uncertainty was examined. The uncertainty in the cost-effectiveness estimate is largely driven by the uncertainty in the clinical model input parameters. More precise clinical parameter estimates could be derived from a future clinical trial. To assess the value of such a trial, analysis of expected value of sample information was performed. Although the value of a future trial would be enormous, we show diminishing marginal returns and a linear increase in the costs of the future trial per additional patient enrolled into the trial for sample sizes larger than 2000 patients. The optimal sample size was estimated to be 4700 patients for a 3 year time horizon. To conclude, decision analytic models have a range of uses and are thus an important and powerful tool for economic evaluations in health care. Decision analytic models that incorporate probabilistic sensitivity analysis and closely related expected value of perfect information analysis are best suited to provide decision makers not only with a point estimate for the cost-effectiveness estimate but to quantify in addition decision uncertainty and the value of future research