Sixten Borg (), Ulf-G. Gerdtham (), Tobias Rydén, Pia Munkholm, Selwyn Odes, Bjørn Moum, Reinhold Stockbrügger and Stefan Lindgren
Sixten Borg: Health Economics Unit, Lund University, Postal: Health Economics Unit, Lund University, Medicon Village, SE-22381 Lund, Sweden
Ulf-G. Gerdtham: Department of Economics, Lund University, Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund, Sweden
Tobias Rydén: Department of Information Technology, Uppsala University, Postal: Department of Information Technology, Uppsala University, Sweden
Pia Munkholm: Department of Gastroenterology, North Zealand University Hospital, Postal: Department of Gastroenterology, Medical Section, Capital Region of Copenhagen, North Zealand University Hospital, Denmark
Selwyn Odes: Faculty of Health Sciences, Ben-Gurion University of the Negev, Postal: Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
Bjørn Moum: University of Oslo, Institute of Clinical Medicine, Postal: University of Oslo, Institute of Clinical Medicine, Oslo, Norway
Reinhold Stockbrügger: Department of Gastroenterology and Hepatology, University Hospital Maastricht, Postal: Department of Gastroenterology and Hepatology, University Hospital Maastricht, Maastricht, Netherlands
Stefan Lindgren: Department of Clinical Sciences Malmö, Lund University, Postal: Department of Clinical Sciences Malmö, Lund University
Abstract: Heterogeneity in patient populations is an important issue in health economic evaluations, as the cost-effectiveness of an intervention can vary between patient subgroups, and an intervention which is not cost-effective in the overall population may be cost-effective in particular subgroups. Identifying such subgroups is of interest in the allocation of healthcare resources. Our aim was to develop a method for cost-effectiveness analysis in heterogeneous chronic diseases, by identifying subgroups (phenotypes) directly relevant to the cost-effectiveness of an intervention, and by enabling cost-effectiveness analyses of the intervention in each of these phenotypes. We identified phenotypes based on healthcare resource utilization, using finite mixtures of underlying disease activity models: first, an explicit disease activity model, and secondly, a model of aggregated disease activity. They differed with regards to time-dependence, level of detail, and what interventions they could evaluate. We used them for cost-effectiveness analyses of two hypothetical interventions. Allowing for different phenotypes improved model fit, and was a key step towards dealing with heterogeneity. The cost-effectiveness of the interventions varied substantially between phenotypes. Using underlying disease activity models for identifying phenotypes as well as cost-effectiveness analysis appears both feasible and useful in that they guide the decision to introduce an intervention.
25 pages, February 6, 2015
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