This checklist is an initial approach to improve transparency and understanding of CoI studies in terms of the extent, structure and development of the socioeconomic burden of diseases. The checklist supports the comparability of different studies and facilitates study conception.

Background and purposeThe German Institute for Quality and Efficiency in Health Care (IQWiG) previously tested two preference elicitation methods in pilot projects and regarded them as generally feasible for prioritizing outcome-specific results of benefit assessment. The present study aimed to investigate the feasibility of completing a discrete choice experiment (DCE) within 3 months and to determine the relative importance of attributes of periodontal disease and its treatment.Patients and methodsThis preference elicitation was conducted alongside the IQWiG benefit assessment of systematic treatments of periodontal diseases. Attributes were defined based on the benefit assessment, literature review, and patients’ and periodontologists’ interviews. The DCE survey was completed by patients with a history of periodontal disease. Preferences were elicited for the attributes “tooth loss within next 10 years”, “own costs for treatment, follow-up visits, re-treatment”, “complaints and symptoms”, and “frequency of follow-up visits”. Patients completed a self-administered questionnaire including 12 choice tasks. Data were analyzed using a random parameters logit model. The relative attribute importance was calculated based on level ranges.ResultsWithin 3 months, survey development, data collection among 267 patients, data analysis, and provision of a study report could be completed. The analysis showed that tooth loss (score 0.73) was the most important attribute in patients’ decisions, followed by complaints and symptoms (0.22), frequency of follow-up visits (0.02), and costs (0.03) (relative importance scores summing up to 1).ConclusionA preference analysis performing a DCE can be generally feasible within 3 months; however, a good research infrastructure and access to patients is required. Outcomes used in benefit assessments might need to be adapted to be used in preference analyses.

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Standardization of international health economic guidelines has been repeatedly requested. In this context, an international reference case was proposed, which constitutes an agreed approach for the key elements of health economic evaluation including study perspective, comparators, source of effectiveness data, role of modeling, main (economic) outcome, source of utilities, characterizing uncertainty. It is, however, questionable whether such a reference scenario can reasonably be applied across all health care systems. Our analysis pursues the question to which degree the Institute for Quality and Efficiency in Health Care's (Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen, IQWiG) "General methods for evaluating the relation between cost and benefit" comply with the key elements of the reference case. In case of divergences, they will be described and discussed in light of the German social legislation and in consideration of current scientific evidence. In conclusion, the analysis revealed that IQWiG complied with the reference case in almost all aspects. Differences were found only with respect to the choice of main (economic) outcome and the source of utilities. These differences seem justified and well explained in the context of the German social legislation as well as in view of the weaknesses of the quality-adjusted life year (QALY) concept.

Background: Estimating input costs for Markov models in health economic evaluations requires health state–specific costing. This is a challenge in mental illnesses such as depression, as interventions are not clearly related to health states. We present a hybrid approach to health state–specific cost estimation for a German health economic evaluation of antidepressants. Methods: Costs were determined from the perspective of the community of persons insured by statutory health insurance (“SHI insuree perspective”) and included costs for outpatient care, inpatient care, drugs, and psychotherapy. In an additional step, costs for rehabilitation and productivity losses were calculated from the societal perspective. We collected resource use data in a stepwise hierarchical approach using SHI claims data, where available, followed by data from clinical guidelines and expert surveys. Bottom-up and top-down costing approaches were combined. Results: Depending on the drug strategy and health state, the average input costs varied per patient per 8-week Markov cycle. The highest costs occurred for agomelatine in the health state first-line treatment (FT) (“FT relapse”) with €506 from the SHI insuree perspective and €724 from the societal perspective. From both perspectives, the lowest costs (excluding placebo) were €55 for selective serotonin reuptake inhibitors in the health state “FT remission.” Conclusion: To estimate costs in health economic evaluations of treatments for depression, it can be necessary to link different data sources and costing approaches systematically to meet the requirements of the decision-analytic model. As this can increase complexity, the corresponding calculations should be presented transparently. The approach presented could provide useful input for future models.