Literature DB >> 27278096

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

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Abstract

Year:  2016        PMID： 27278096     DOI： 10.1136/bmj.i3235

Source DB:  PubMed          Journal:  BMJ        ISSN： 0959-8138


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  4 in total

1.  Development and Validation of a Risk Nomogram Model for Predicting Community-Acquired Pressure Injury Among the Older Adults in China: A Case-Control Study.

Authors:  Zhi Li Zhang; Xiao Xue Hu; Hong Li Yang; Du Wang
Journal:  Clin Interv Aging       Date:  2022-10-01       Impact factor: 3.829

2.  No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

Authors:  Maarten van Smeden; Joris A H de Groot; Karel G M Moons; Gary S Collins; Douglas G Altman; Marinus J C Eijkemans; Johannes B Reitsma
Journal:  BMC Med Res Methodol       Date:  2016-11-24       Impact factor: 4.615

3.  Prognostic Models for Predicting Overall Survival in Patients with Primary Gastric Cancer: A Systematic Review.

Authors:  Qi Feng; Margaret T May; Suzanne Ingle; Ming Lu; Zuyao Yang; Jinling Tang
Journal:  Biomed Res Int       Date:  2019-09-18       Impact factor: 3.411

4.  Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer.

Authors:  Changhee Lee; Alexander Light; Evgeny S Saveliev; Mihaela van der Schaar; Vincent J Gnanapragasam
Journal:  NPJ Digit Med       Date:  2022-08-06
  4 in total

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