Which statistical tests for estimating osteoporotic fracture risk?

The article presents an analysis of statistical methods used for estimating fracture risk in patients with osteoporosis. Mathematical relations of different methods are explained (risk--R, risk ratio--RR, RD--risk difference, odds--O, odds ratio--OR, Yule's Q, Yule's Y, logistic model). What is important to keep in mind is that: 1) relative risk and odds ratio are statistics that only describe an association, not causation; 2) relative risk and odds ratio refer to a population, not to an individual patient; 3) the studies of small groups are more likely to find an association that might actually just be due to chance, the larger the groups, the less likely the association between a risk factor and an outcome (fracture); 4) when the incidence of an outcome of interest in the study population is low (<10 %), the OR is close to the RR, the more frequent the outcome becomes, the more the OR will overestimate the RR when it is more than 1 or underestimate the RR when it is less than 1. Sophisticated statistical packages are available which can calculate many of the tests of association but the problem is that the investigator must know which the desirable is. The incorrect option of statistical analysis, the incorrect interpretation of risk ratio or odds ratio and overestimation of the importance of a risk factor may lead to unintentional errors in the economic analysis of potential programs or treatments in osteoporosis. This article could be a contribution for investigators, who are concerned with assessment of fracture risk.