The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Importance of events per independent variable in proportional hazards analysis I. Background, goals, and general strategy
Open accessAbstract
Multivariable methods of analysis can yield problematic results if methodological guidelines and mathematical assumptions are ignored. A problem arising from a too-small ratio of events per variable (EPV) can affect the accuracy and precision of regression coefficients and their tests of statistical significance. The problem occurs when a proportional hazards analysis contains too few "failure" events (e.g., deaths) in relation to the number of included independent variables. In the current research, the impact of EPV was assessed for results of proportional hazards analysis done with Monte Carlo simulations in an empirical data set of 673 subjects enrolled in a multicenter trial of coronary artery bypass surgery. The research is presented in two parts: Part I describes the data set and strategy used for the analyses, including the Monte Carlo simulation studies done to determine and compare the impact of various values of EPV in proportional hazards analytical results. Part II compares the output of regression models obtained from the simulations, and discusses the implication of the findings.
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Citations
Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Cited by
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655
Self-citationRelaxing the Rule of Ten Events per Variable in Logistic and Cox Regression
Vittinghoff, E., & McCulloch, C. E. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwk052 · Cited by 4,359
Calculating the sample size required for developing a clinical prediction model
Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & van Smeden, M. (2020) · BMJ · doi:10.1136/bmj.m441 · Cited by 2,917
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
“The number of events-pervariable (EPV) is commonly used to calculate the sample size, where attaining a sample size with an EPV of ten or more is frequently recommended to avoid overfitting [97–101].”
MethodPrognosis and prognostic research: what, why, and how?
Moons, K. G. M., Royston, P., Vergouwe, Y., Grobbee, D. E., & Altman, D. G. (2009) · BMJ · doi:10.1136/bmj.b375 · Cited by 1,337
Metadata
- Authors
- Concato, J., Peduzzi, P., Holford, T. R., & Feinstein, A. R.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 1995-12
- Volume
- 48
- Issue
- 12
- Pages
- 1495-1501
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W1982245010
- Cited by
- 797
- References
- 1
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
