Two Criteria for Evaluating Risk Prediction Models
Two Criteria for Evaluating Risk Prediction Models
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DOI：
10.1111/j.1541-0420.2010.01523.x
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发表时间：
2011-09-01
期刊：
影响因子：
1.9
通讯作者：
Gail, M. H.
中科院分区：
文献类型：
--
作者：
Pfeiffer, R. M.;Gail, M. H.
我们提出并研究了两个标准来评估预测疾病发病率风险的模型对筛查和预防的有用性，或预测模型对疾病诊断后管理的有用性。第一个标准是遵循PCF的病例比例(Q)，即将发生疾病的个人的比例包括在高危人群中的个人比例Q中。第二个标准是随访所需的比例PnF(P)，即需要遵循的最高风险普通人群的比例，以使注定成为病例的人的比例p得到遵循。PCF(Q)评估一项跟踪100q%高危人群的计划的有效性。PNF(P)通过指出必须跟踪多少高危人群来评估覆盖100p%病例的可行性。我们证明了这两个准则与Lorenz曲线及其逆的关系，并给出了估计PCF和PNF的分布理论。我们开发了基于影响函数的新方法，用于对单个风险模型进行推断，并比较两个风险模型的PCF和PNF，这两个模型都是在相同的验证数据中进行评估的。
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 followed PCF(q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF(p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF(q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF(p) assess the feasibility of covering 100p% 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 of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data.