Sample size considerations and predictive performance of multinomial logistic prediction models-Reference-Cited by-同舟云学术

Sample size considerations and predictive performance of multinomial logistic prediction models

Published:2019-01-06 Issue:9 Volume:38 Page:1601-1619
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statistics in Medicine

Author:

Jong Valentijn M. T.¹^ORCID,Eijkemans Marinus J. C.¹^ORCID,Calster Ben²³^ORCID,Timmerman Dirk²⁴^ORCID,Moons Karel G. M.¹,Steyerberg Ewout W.³^ORCID,Smeden Maarten³⁵^ORCID

Affiliation:

1. Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht, Utrecht University Utrecht The Netherlands

2. Department of Development and RegenerationKU Leuven Leuven Belgium

3. Department of Biomedical Data SciencesLeiden University Medical Center Leiden The Netherlands

4. Department of Obstetrics and GynecologyUniversity Hospitals Leuven Leuven Belgium

5. Department of Clinical EpidemiologyLeiden University Medical Center Leiden The Netherlands

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Fonds Wetenschappelijk Onderzoek

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8063

Reference50 articles.

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

2. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

3. Polytomous logistic regression analysis could be applied more often in diagnostic research

4. Diagnostic studies as multivariable, prediction research

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