Development of an economic evaluation of diagnostic strategies: the case of monogenic diabetes

OBJECTIVES: To describe the development process for defining an appropriate model structure for the economic evaluation of test–treatment strategies for patients with monogenic diabetes (caused by mutations in the GCK, HNF1A or HNF4A genes). DESIGN: Experts were consulted to identify and define realistic test–treatment strategies and care pathways. A systematic assessment of published diabetes models was undertaken to inform the model structure. SETTING: National Health Service in England and Wales. PARTICIPANTS: Experts in monogenic diabetes whose collective expertise spans the length of the patient care pathway. PRIMARY AND SECONDARY OUTCOMES: A defined model structure, including the test–treatment strategies, and the selection of a published diabetes model appropriate for the economic evaluation of strategies to identify patients with monogenic diabetes. RESULTS: Five monogenic diabetes test–treatment strategies were defined: no testing of any kind, referral for genetic testing based on clinical features as noted by clinicians, referral for genetic testing based on the results of a clinical prediction model, referral for genetic testing based on the results of biochemical and immunological tests, referral for genetic testing for all patients with a diagnosis of diabetes under the age of 30 years. The systematic assessment of diabetes models identified the IMS CORE Diabetes Model (IMS CDM) as a good candidate for modelling the long-term outcomes and costs of the test–treatment strategies for monogenic diabetes. The short-term test–treatment events will be modelled using a decision tree which will feed into the IMS CDM. CONCLUSIONS: Defining a model structure for any economic evaluation requires decisions to be made. Expert consultation and the explicit use of critical appraisal can inform these decisions. Although arbitrary choices have still been made, decision modelling allows investigation into such choices and the impact of assumptions that have to be made due to a lack of data.