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当事件数量较少时，如何开发一个更准确的风险预测模型。

How to develop a more accurate risk prediction model when there are few events.

作者信息

Pavlou Menelaos, Ambler Gareth, Seaman Shaun R, Guttmann Oliver, Elliott Perry, King Michael, Omar Rumana Z

机构信息

Department of Statistical Science, University College London, WC1E 6BT London, UK

Department of Statistical Science, University College London, WC1E 6BT London, UK.

出版信息

BMJ. 2015 Aug 11;351:h3868. doi: 10.1136/bmj.h3868.

DOI:10.1136/bmj.h3868
PMID:26264962
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4531311/
Abstract

When the number of events is low relative to the number of predictors, standard regression could produce overfitted risk models that make inaccurate predictions. Use of penalised regression may improve the accuracy of risk prediction

摘要

当事件数量相对于预测变量数量较少时，标准回归可能会产生过度拟合的风险模型，从而做出不准确的预测。使用惩罚回归可能会提高风险预测的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72b7/4784839/8609109df015/pavm022798.f2_default.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72b7/4784839/61f3b7163279/pavm022798.f1_default.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72b7/4784839/8609109df015/pavm022798.f2_default.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72b7/4784839/61f3b7163279/pavm022798.f1_default.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72b7/4784839/8609109df015/pavm022798.f2_default.jpg

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本文引用的文献

[1]
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

BMJ. 2015-1-7

[2]
Meta-analysis and aggregation of multiple published prediction models.

Stat Med. 2014-6-30

[3]
A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM risk-SCD).

Eur Heart J. 2013-10-14

[4]
An evaluation of penalised survival methods for developing prognostic models with rare events.

Stat Med. 2011-10-14

[5]
Assessing the performance of prediction models: a framework for traditional and novel measures.

Epidemiology. 2010-1

[6]
Prognosis and prognostic research: application and impact of prognostic models in clinical practice.

BMJ. 2009-6-4

[7]
Prognosis and prognostic research: what, why, and how?

BMJ. 2009-2-23

[8]
General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

Circulation. 2008-2-12

[9]
Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study.

BMJ. 2007-7-21

[10]
Generic, simple risk stratification model for heart valve surgery.

Circulation. 2005-7-12

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