检查表
计算机科学
聚类分析
多元微积分
语句(逻辑)
数据挖掘
三脚架(摄影)
精化
星团(航天器)
情报检索
数据科学
人工智能
心理学
哲学
控制工程
法学
政治学
人文学科
工程类
认知心理学
程序设计语言
物理
光学
作者
Thomas P. A. Debray,Gary S. Collins,Richard D Riley,Kym I E Snell,Ben Van Calster,Johannes B. Reitsma,Karel G.M. Moons
标识
DOI:10.1136/bmj-2022-071058
摘要
The TRIPOD-Cluster (transparent reporting of multivariable prediction models developed or validated using clustered data) statement comprises a 19 item checklist, which aims to improve the reporting of studies developing or validating a prediction model in clustered data, such as individual participant data meta-analyses (clustering by study) and electronic health records (clustering by practice or hospital). This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD-Cluster statement is explained in detail and accompanied by published examples of good reporting. The document also serves as a reference of factors to consider when designing, conducting, and analysing prediction model development or validation studies in clustered data. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, authors are recommended to include a completed checklist in their submission.
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