No abstract

The appropriate development of a model begins with understanding the problem that is being represented. The aim of this article was to provide a series of consensus-based best practices regarding the process of model conceptualization. For the purpose of this series of articles, we consider the development of models whose purpose is to inform medical decisions and health-related resource allocation questions. We specifically divide the conceptualization process into two distinct components: the conceptualization of the problem, which converts knowledge of the health care process or decision into a representation of the problem, followed by the conceptualization of the model itself, which matches the attributes and characteristics of a particular modeling type with the needs of the problem being represented. Recommendations are made regarding the structure of the modeling team, agreement on the statement of the problem, the structure, perspective, and target population of the model, and the interventions and outcomes represented. Best practices relating to the specific characteristics of model structure and which characteristics of the problem might be most easily represented in a specific modeling method are presented. Each section contains a number of recommendations that were iterated among the authors, as well as among the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

No abstract

No abstract

This study examined relative hazards for mortality and functional limitations according to poor self-ratings of health using prospective data from the NHANES I Epidemiologic Follow-up Study, a representative sample of US adults aged 25-74 years that has been followed since the First National Health and Nutrition Examination Survey (NHANES I) was conducted in 1971-1975. Follow-up data were taken from death records and from the 1982 and 1992 reinterviews. Respondents (n = 6,913) provided extensive baseline data through physician examinations, laboratory testing, and self-reports of conditions, symptoms, and risk behaviors. Functional limitations were assessed among survivors in 1982 and 1992. Cox regression models accounting for sample design indicated that baseline self-rated health was associated with a significantly reduced hazard of mortality for males but not for females through 1992; adjusted hazards ratios for excellent health as compared with poor health were 0.52 for males (95% confidence interval: 0.36, 0.73) and 0.80 for females (95% confidence interval: 0.51, 1.23). Self-rated health also predicted 1982 and 1992 functional limitation for both men and women and 1992 function net of 1982 function for men only. Self-rated health contributes unique information to epidemiologic studies that is not captured by standard clinical assessments or self-reported histories, but evidence suggests that the effect may be stronger for men than for women.

No abstract

No abstract

Objectives. In 2016, the Second Panel on Cost-effectiveness in Health and Medicine updated the seminal work of the original panel from 2 decades earlier. The Second Panel had an opportunity to reflect on the evolution of cost-effectiveness analysis (CEA) and to provide guidance for the next generation of practitioners and consumers. In this article, we present key topics for future research and policy. Methods. During the course of its deliberations, the Second Panel discussed numerous topics for advancing methods and for improving the use of CEA in decision making. We identify and consider 7 areas for which the panel believes that future research would be particularly fruitful. In each of these areas, we highlight outstanding research needs. The list is not intended as an exhaustive inventory but rather a set of key items that surfaced repeatedly in the panel’s discussions. In the online Appendix , we also list and expound briefly on 8 other important topics. Results. We highlight 7 key areas: CEA and perspectives (determining, valuing, and summarizing elements for the analysis), modeling (comparative modeling and model transparency), health outcomes (valuing temporary health and path states, as well as health effects on caregivers), costing (a cost catalogue, valuing household production, and productivity effects), evidence synthesis (developing theory on learning across studies and combining data from clinical trials and observational studies), estimating and using cost-effectiveness thresholds (empirically representing 2 broad concepts: opportunity costs and public willingness to pay), and reporting and communicating CEAs (written protocols and a quality scoring system). Conclusions. Cost-effectiveness analysis remains a flourishing and evolving field with many opportunities for research. More work is needed on many fronts to understand how best to incorporate CEA into policy and practice.