## Introduction

Over the last decade, our knowledge of risk factors for osteoporotic fractures has greatly increased. Not coincidentally, these years have seen the development of several new therapies that effectively decrease fracture risk. With the development of new drug therapies came the need to assess their economic value to inform health-care budget allocation decisions. Many cost-effectiveness analyses to date have utilized disease simulation models to capture the long-term impact of osteoporosis therapies, which can have economic and clinical benefits that extend beyond the time horizon of most clinical trials [1–6]. These analyses are very sensitive to the assumed fracture risk in the target population, with cost-effectiveness ratios decreasing when fracture risk rises.

In many analyses, the target population is a subgroup of the general population, commonly, high-risk patients who are at greatest need for treatment. For example, one may want to consider for treatment osteoporosis patients with the following risk factors: very low bone mineral density (BMD), a previous vertebral fracture, and a history of falls. Absolute fracture rates in such high-risk groups are typically not known. Instead, the fracture rate must be calculated based on the general population fracture rate and the increased risk of fracture associated with each risk factor. This is normally carried out by multiplying the general population fracture rate by an overall relative risk (RR) that represents the risk of fracture in the target population versus the general population. Nevertheless, the RR estimates available in the literature are commonly for patients with a risk factor compared with those without. Given that cost-effectiveness analyses seek to estimate the value of treating an at-risk population compared with doing nothing, the risk comparison of interest is between the target population and the general population. For this application, the published RRs are an overestimate because some patients within the general population will also have the risk factor.

In many analyses, the explicit calculation of RR for the target population is not conducted, or the risks are overestimated. The purpose of this article is to describe the challenges in determining RR in a target population and present some possible approaches. Specifically, we will address two issues: 1) a method for converting RR estimates for binary risk factors (e.g., history of fracture); and 2) a method for converting RR estimates for continuous risk factors (e.g., BMD).