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||Two criteria for evaluating risk prediction models.
||Pfeiffer RM, Gail MH
||We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followedâPCFâ(q), is the proportion of individuals who will develop disease who are included in the proportionâqâof individuals in the population at highest risk. The second criterion is the proportion needed to follow-up,âPNFâ(p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportionâpâof those destined to become cases will be followed.âPCFâ(q)âassesses the effectiveness of a program that followsâ100q% of the population at highest risk.âPNFâ(p)âassess the feasibility of coveringâ100p% of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates ofâPCFâandâPNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing theâPCFs andâPNFs of two risk models, both of which were evaluated in the same validation data.