alexa Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates.


Journal of Community & Public Health Nursing

Author(s): Peduzzi P, Concato J, Feinstein AR, Holford TR, Peduzzi P, Concato J, Feinstein AR, Holford TR, Peduzzi P, Concato J, Feinstein AR, Holford TR, Peduzzi P, Concato J, Feinstein AR, Holford TR

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Abstract The analytical effect of the number of events per variable (EPV) in a proportional hazards regression analysis was evaluated using Monte Carlo simulation techniques for data from a randomized trial containing 673 patients and 252 deaths, in which seven predictor variables had an original significance level of p < 0.10. The 252 deaths and 7 variables correspond to 36 events per variable analyzed in the full data set. Five hundred simulated analyses were conducted for these seven variables at EPVs of 2, 5, 10, 15, 20, and 25. For each simulation, a random exponential survival time was generated for each of the 673 patients, and the simulated results were compared with their original counterparts. As EPV decreased, the regression coefficients became more biased relative to the true value; the 90\% confidence limits about the simulated values did not have a coverage of 90\% for the original value; large sample properties did not hold for variance estimates from the proportional hazards model, and the Z statistics used to test the significance of the regression coefficients lost validity under the null hypothesis. Although a single boundary level for avoiding problems is not easy to choose, the value of EPV = 10 seems most prudent. Below this value for EPV, the results of proportional hazards regression analyses should be interpreted with caution because the statistical model may not be valid.
This article was published in J Clin Epidemiol and referenced in Journal of Community & Public Health Nursing

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