The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The number of subjects per variable required in linear regression analyses
Open accessAbstract
OBJECTIVES: To determine the number of independent variables that can be included in a linear regression model. STUDY DESIGN AND SETTING: We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R(2) of the fitted model. RESULTS: A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R(2), although adjusted R(2) estimates behaved well. The bias in estimating the model R(2) statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. CONCLUSION: Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals.
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
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
Relaxing 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
How many subjects does it take to do a regression analysis
Green, S. B. (1991) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr2603_7 · Cited by 3,916 · cited 3×
“Green [5] used statistical power analysis to compare the performance of different rules-of-thumb for how many subjects were required for linear regression analysis.”
BackgroundMethodInfluentialPeduzzi, P., Concato, J., Feinstein, A. R., & Holford, T. R. (1995) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(95)00048-8 · Cited by 2,154 · cited 5×
“[8,9] conducted a series of simulation studies to examine the effective sample size that was required to allow logistic regression models and Cox proportional hazards models to be reliably estimated.”
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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
A calibration hierarchy for risk models was defined: from utopia to empirical data
Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina, M. J., & Steyerberg, E. W. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.12.005 · Cited by 910
Self-citationCappa, F., Oriani, R., Peruffo, E., & McCarthy, I. (2021) · Journal of Product Innovation Management · doi:10.1111/jpim.12545 · Cited by 377
Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes
Riley, R. D., Snell, K. I. E., Ensor, J., Burke, D. L., Harrell, F. E., Moons, K. G. M., & Collins, G. S. (2019) · Statistics in Medicine · doi:10.1002/sim.7993 · Cited by 351
Westbrook, J. I., Raban, M. Z., Walter, S. R., & Douglas, H. (2018) · BMJ Quality & Safety · doi:10.1136/bmjqs-2017-007333 · Cited by 317
Metadata
- Authors
- Austin, P. C., & Steyerberg, E. W.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 68
- Issue
- 6
- Pages
- 627-636
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2135760566
- Cited by
- 1,059
- References
- 6
- FWCI
- 59.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
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