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- W2266848231 abstract "When the probability of an outcome depends on exposure to a continuous risk factor, X, the probability at exposure level X=x, is often estimated by multiplying a reference probability p{0} (corresponding to X=x{0}) by a previously published estimate of the relative risk per unit change in that risk factor, raised to the power (x-x{0}). Typically the estimate of relative risk used for such calculations is more precisely an odds ratio (logistic regression), incidence rate ratio (Poisson regression), or hazard ratio (proportional hazards model), and a problem with this use of surrogate measures of relative risk in high-exposure, high-risk situations is that the probability of the outcome is always over-estimated. To illustrate this, a proportional hazards model, a Poisson regression model, and three types of binomial regression with log, logit and complementary log-log links were fitted to data from a study of osteoporotic bone fracture in 1017 elderly women followed for 6 years in Perth, Australia. When surrogate estimates of relative risk were used as multipliers to estimate the probability of fracture at very low values of bone mineral density (BMD) from the probability of fracture at a BMD T-score of zero, they all yielded over-estimates. The dangers of treating an odds ratio as a relative risk are well known - but treating incidence rates, hazard-ratios or the exponential of the Extreme Value model coefficients as approximate relative risks may also lead to over-estimates of risk (both relative and absolute), and for common events the use of the appropriate model-based formulae for estimating risk is mandatory." @default.
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- W2266848231 date "2009-12-01" @default.
- W2266848231 modified "2023-09-23" @default.
- W2266848231 title "A Note on Estimating the Probability of Common Events with Generalised Linear Models; an Example Using Bone Density to Predict Fractures" @default.
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