FRAX and its applications in health economics--cost-effectiveness and intervention thresholds using bazedoxifene in a...

An important aspect of cost-effectiveness analysis of osteoporosis is to accurately model the fracture risk and mortality related to the patient groups in the analysis. The estimation of fracture risk is based on a number of factors, such as the level of general risk of the normal population, the effect of treatment and the prevalence of clinical risk factors (CRFs) for fracture. Fracture risk has traditionally been calculated with risk adjustments based on age, bone mineral density and prior vertebral fracture. The treatment effect has been derived from clinical trials and, in the absence of subgroup analyses, the same efficacy has been assumed irrespective of the fracture risk of the population. The FRAX tool enables the estimation of risk based on a wider range of risk factors, and treatment efficacy that is dependent on the level of risk in the analyzed population. The objective was to describe the implementation of the FRAX algorithms into health economic osteoporosis models and to highlight how it differs from traditional risk assessment.

The selective estrogen receptor modulator, bazedoxifene, was evaluated in a Swedish setting with traditional and FRAX-based risk assessment in a previously developed Markov model that included fractures and thromboembolic events, and also was adapted to accommodate risk-dependent efficacy, which is available for bazedoxifene.

The traditional approach gave lower ICERs at ages up to 60 years compared to the FRAX method when only considering age, BMD and prior fracture. At 70 years and older and when adding more CRFs with the FRAX approach, the FRAX ICER decreased and fell below the traditional approach. The risk dependant efficacy was the main reason for lower ICERs with FRAX in women at higher risk of fracture.

FRAX applied in cost-effectiveness analyses is a more granular method for the estimation of fracture risk, mortality and efficacy compared to previous approaches that can also improve case finding. Furthermore, it facilitates the estimation of cost-effectiveness for various types of patients with different combinations of CRFs, which more closely matches patients in clinical practice.