Application of Decision-Analytic Modelling in Health Economic Evaluations

Background: Western Societies are facing increasing challenges in funding their health care systems. Therefore, health care decision-makers have started increasingly to demand evidence that a health technology is cost-effective before it is financed or recommended to be adopted for wide use. In medicine, scientific evidence produced by randomised controlled trials (RCTs) is typically ranked highly in the hierarchy of evidence. Unfortunately, RCTs are restricted in their ability to produce evidence in the form that is needed for health care decisions. The applications of decision-analytic models have been suggested to offer a potential vehicle to produce valid evidence, which is relevant to the health care decision-makers. Aim of the study: To develop the applications of decision-analytic models and to explore the applicability of a set of Bayesian methods for evidence synthesis and decision-analytic modelling in health economic evaluations. Material and methods: The study is based on four separate case studies, where the decision-analytic models were developed and evidence synthesis methods were applied to aid in real world decisionmaking processes. Probabilistic modelling techniques were applied in all four case studies. Furthermore, a set of Bayesian methods was applied to synthesise the available evidence and to handle uncertainties in the decision-analytic models. Results: The conducted case studies showed that the decision-analytic models can be applied, when there is a need to assess all relevant evidence, to link intermediate outcomes to final outcomes, to make results applicable to the decision-making context due to a gap between clinical trial evidence and the requirements for a decision, or to estimate cost-effectiveness for specific subgroups. The case studies also showed that the identification, selection, and critical appraisal of evidence are the most time consuming parts of the model development process. Evidence synthesis proved to be challenging due to the timing of cost-effectiveness evaluations, since they tend to focus on a time period at or around the implementation of health technology, when experience and evidence about its clinical and economic consequences may still be relatively limited. Furthermore, the case studies proved that the probabilistic modelling approach does offer an efficient way to reflect decision uncertainty but data used in the probabilistic models requires very often some preparation, which in turn, increases the number of the additional sources of methodological and process uncertainties. Conclusions and suggestions for further research: The study proved that the applications of decision-analytic models offer a clear and coherent mathematical structure to combine all relevant evidence and to assess in advance the consequences, such as expected costs and health outcomes, of different decisions. However, further developments to standardise the modelling processes and to reflect the quality of evidence used in the decision-analytic models are needed. In addition, further developments to improve the handling of model uncertainty and to increase transparency are to be welcomed. National Library of Medicine Classification: W 74 Medical Subject Headings: Decision Making; Economics; Models, Economic; Cost and Cost Analysis; Bayes Theorem Martikainen, Janne. Paatosanalyyttisten mallien soveltaminen terveystaloudellisessa arviointitutkimuksessa. Kuopion yliopiston julkaisuja E. Yhteiskuntatieteet 152. 2008. 147 s. ISBN 978-951-27-0811-6 ISBN 978-951-27-1062-1 (PDF) ISSN 1235-0494