With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to a target population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction.
Keywords Epidemiology  Extrapolation  Probability  Risk  Philosophy of Medicine  Evidence-based Medicine  Philosophy of Science
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DOI 10.1016/j.shpsc.2015.06.006
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References found in this work BETA

Studies in the Logic of Explanation.Carl Hempel & Paul Oppenheim - 1948 - Philosophy of Science 15 (2):135-175.
Are Rcts the Gold Standard?Nancy Cartwright - 2007 - Biosocieties 1:11-20.
Mechanisms: What Are They Evidence for in Evidence-Based Medicine?Holly Andersen - 2012 - Journal of Evaluation in Clinical Practice 18 (5):992-999.

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