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Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches.预测多种二元结局风险的临床预测模型：方法比较
Stat Med. 2021 Jan 30;40(2):498-517. doi: 10.1002/sim.8787. Epub 2020 Oct 26.
2
Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: an observational cohort study.系统评价和外部验证 22 种住院成人 COVID-19 预后模型：一项观察性队列研究。
Eur Respir J. 2020 Dec 24;56(6). doi: 10.1183/13993003.03498-2020. Print 2020 Dec.
3
Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.利用 ISARIC WHO 临床特征协议对因 COVID-19 住院的患者进行风险分层：4C 死亡率评分的制定和验证。
BMJ. 2020 Sep 9;370:m3339. doi: 10.1136/bmj.m3339.
4
Prediction meets causal inference: the role of treatment in clinical prediction models.预测与因果推断：治疗在临床预测模型中的作用。
Eur J Epidemiol. 2020 Jul;35(7):619-630. doi: 10.1007/s10654-020-00636-1. Epub 2020 May 22.
5
Prediction models for diagnosis and prognosis in Covid-19.新型冠状病毒肺炎诊断与预后的预测模型
BMJ. 2020 Apr 14;369:m1464. doi: 10.1136/bmj.m1464.
6
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal.COVID-19 诊断和预后预测模型：系统评价和批判性评估。
BMJ. 2020 Apr 7;369:m1328. doi: 10.1136/bmj.m1328.
7
Calculating the sample size required for developing a clinical prediction model.计算开发临床预测模型所需的样本量。
BMJ. 2020 Mar 18;368:m441. doi: 10.1136/bmj.m441.
8
Dynamic models to predict health outcomes: current status and methodological challenges.预测健康结果的动态模型：现状与方法学挑战
Diagn Progn Res. 2018 Dec 18;2:23. doi: 10.1186/s41512-018-0045-2. eCollection 2018.
9
Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes.建立多变量预测模型的最小样本量：第二部分 - 二分类和生存数据。
Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24.
10
Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes.建立多变量预测模型的最小样本量：第一部分-连续结局。
Stat Med. 2019 Mar 30;38(7):1262-1275. doi: 10.1002/sim.7993. Epub 2018 Oct 22.

预测 COVID-19 模型的性能：外部验证的卡度内分岔。

Performance of prediction models for COVID-19: the Caudine Forks of the external validation.

机构信息

Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

Clinical Epidemiology and Medical Statistics Unit, Dept of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy.

出版信息

Eur Respir J. 2020 Dec 24;56(6). doi: 10.1183/13993003.03728-2020. Print 2020 Dec.

DOI:10.1183/13993003.03728-2020
PMID:33060155
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7562696/
Abstract

https://bit.ly/2SMtoLV

摘要

链接