Purpose: Our study aims to combine the versatility of the Analytic Hierarchy Process (AHP) with the decision-analytic sophistication of Markov modelling in a new methodology for early technology assessment. As an illustration, we apply this methodology to a new technology to diagnose breast cancer.
Method: Markov modelling is a commonly used approach to support decision making about the application of health care technology. We use a basic Markov model to compare the incremental cost-effectiveness of alternative technologies in terms of their costs and clinical effectiveness. The AHP is a technique for multi-criteria analysis, relatively new in the field of technology assessment. It can integrate both quantitative and qualitative criteria in the assessment of alternative technologies. We applied the AHP to prioritize a more versatile set of outcome measures than Markov models do. These outcome measures include the clinical effectiveness and its determinants, as well as costs, patient comfort and safety. Furthermore, the AHP is applied to predict the performance of the new technology with regard to these outcome measures.
Result: We systematically estimated priors on the clinical effectiveness of the new technology. In our illustration, estimations on the sensitivity and specificity of the new diagnostic technology were used as an input in the Markov model. Moreover, prioritized outcome measures including the clinical effectiveness (w = 0.61), patient comfort (w = 0.09) and safety (w = 0.30) could be integrated into one combined outcome measure in the Markov model.
Conclusion: Combining AHP and Markov modelling is particularly valuable in early technology assessment when evidence about the effectiveness of health care technology is still missing. Moreover, this combination can be valuable in case decision makers are interested in other patient relevant outcomes measures besides the technology’s clinical effectiveness, and which are not (adequately) captured in the mainstream utility measures. These outcome measures can have a strong impact on the successful application of health care technology.