期刊
JOURNAL OF CLINICAL EPIDEMIOLOGY
卷 76, 期 -, 页码 175-182出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jclinepi.2016.02.031
关键词
Events per variable; Cox model; External validation; Predictive modeling; Sample size; Resampling study
资金
- MRC Partnership Grant for the PROGnosis RESearch Strategy (PROGRESS) group [G0902393]
- Medical Research Council [G1100513]
- Cancer Research UK [16895] Funding Source: researchfish
- Medical Research Council [G1100513, G0902393] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0513-10131] Funding Source: researchfish
- MRC [G1100513, G0902393] Funding Source: UKRI
Objectives: The choice of an adequate sample size for a Cox regression analysis is generally based on the rule of thumb derived from simulation studies of a minimum of 10 events per variable (EPV). One simulation study suggested scenarios in which the 10 EPV rule can be relaxed. The effect of a range of binary predictors with varying prevalence, reflecting clinical practice, has not yet been fully investigated. Study Design and Setting: We conducted an extended resampling study using a large general-practice data set, comprising over 2 million anonymized patient records, to examine the EPV requirements for prediction models with low-prevalence binary predictors developed using Cox regression. The performance of the models was then evaluated using an independent external validation data set. We investigated both fully specified models and models derived using variable selection. Results: Our results indicated that an EPV rule of thumb should be data driven and that EPV >= 20 generally eliminates bias in regression coefficients when many low-prevalence predictors are included in a Cox model. Conclusion: Higher EPV is needed when low-prevalence predictors are present in a model to eliminate bias in regression coefficients and improve predictive-accuracy. (C) 2016 The Authors. Published by Elsevier Inc.
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