Developing Better Economic Models of Osteoporosis: Considerations for the Calculation of the Relative Risk of Fracture
Article first published online: 18 JAN 2006
Value in Health
Volume 9, Issue 1, pages 54–58, January 2006
How to Cite
Black, D. M., Palermo, L. and Grima, D. T. (2006), Developing Better Economic Models of Osteoporosis: Considerations for the Calculation of the Relative Risk of Fracture. Value in Health, 9: 54–58. doi: 10.1111/j.1524-4733.2006.00081.x
- Issue published online: 18 JAN 2006
- Article first published online: 18 JAN 2006
- cost-effectiveness analysis;
- relative risk;
- risk factors
Objective: Simulation models are often used to assess cost-effectiveness of osteoporosis therapies. Many cost-effectiveness analyses are interested in a subset of the general population, such as high-risk patients. As the analyses are very sensitive to the assumed risk of fracture, it is imperative that the rates accurately reflect the fracture risk in the specified target population. The objective of this study was to describe the methodological difficulties and present some possible solutions for calculating the risk of facture in target populations of interest.
Methods: For binary risk factors, a method for converting from a relative risk (RR) for people with a risk factor relative to those without, to an RR in the target population compared with the general population, is described. For continuous risk factors (i.e., bone mineral density [BMD]), data are often provided as an RR of fracture per SD decrease. A method for converting from an RR per SD decrease to an RR in those below a certain BMD threshold, compared with the general population, is presented.
Results: These results should allow future economic models to more accurately incorporate existing knowledge of risk factors by introducing methods to calculate fracture risk estimates in a target population.
Conclusion: It illustrates the importance of considering the prevalence of risk factors in the general population when calculating RR in a target population.