Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals
Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals
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DOI：
10.1097/ccm.0000000000004246
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发表时间：
2020-05-01
影响因子：
8.8
通讯作者：
Maslove, David M.
中科院分区：
文献类型：
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作者：
Leisman, Daniel E.;Harhay, Michael O.;Maslove, David M.
预测模型旨在使用可用的数据来预测尚未观察到的健康状态或结果。预测主要与临床实践相关，但也用于研究和管理。虽然预测建模涉及估计患者因素和结果之间的关系，但它不同于随意推断。因此，预测建模需要对开发、验证和更新进行独特的考虑。这份文件代表了31家呼吸、睡眠和重症护理医学杂志的编辑为整合当代最佳实践和与预测研究设计、进行和报告相关的建议所做的努力。在这里，我们解决了在提交给我们各种期刊的文章中经常遇到的问题。主要主题包括选择预测变量的考虑因素、变量的可操作性、处理丢失的数据、适当验证的重要性、模型业绩衡量及其解释以及良好的报告做法。补充讨论涵盖了新出现的话题，如模型公平性、竞争风险、“可修改风险因素”的陷阱、测量误差和偏差风险。这一指导并不意味着过度规范；我们承认每一项研究都是不同的，没有一套规则可以适用于所有情况。更多的最佳实践可以在个人预后或诊断(Tripod)指南的多变量预测模型的透明报告中找到，我们请读者参考该指南以了解进一步的细节。
Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of "modifiable risk factors", measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details.