预测建模
检查表
指南
多元微积分
计算机科学
医学
数据挖掘
机器学习
病理
心理学
工程类
控制工程
认知心理学
作者
Karel G.M. Moons,Douglas G. Altman,Johannes B. Reitsma,Gary S. Collins
标识
DOI:10.1097/pap.0000000000000072
摘要
Prediction models are developed to aid health care providers in estimating the probability that a specific outcome or disease is present (diagnostic prediction models) or will occur in the future (prognostic prediction models), to inform their decision making. Prognostic models here also include models to predict treatment outcomes or responses; in the cancer literature often referred to as predictive models. Clinical prediction models have become abundant. Pathology measurement or results are frequently included as predictors in such prediction models, certainly in the cancer domain. Only when full information on all aspects of a prediction modeling study are clearly reported, risk of bias and potential usefulness of the prediction model can be adequately assessed. Many reviews have illustrated that the quality of reports on the development, validation, and/or adjusting (updating) of prediction models, is very poor. Hence, the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative has developed a comprehensive and user-friendly checklist for the reporting of studies on, both diagnostic and prognostic, prediction models. The TRIPOD Statement intends to improve the transparency and completeness of reporting of studies that report solely on development, both development and validation, and solely on the validation (with or without updating) of diagnostic or prognostic, including predictive, models.
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