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

J Clin Epidemiol. 1995 Dec;48(12):1503-10. doi: 10.1016/0895-4356(95)00048-8.


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.

Publication types

  • Clinical Trial
  • Randomized Controlled Trial
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Computer Simulation*
  • Coronary Artery Bypass
  • Coronary Disease / mortality*
  • Coronary Disease / surgery
  • Humans
  • Monte Carlo Method*
  • Proportional Hazards Models*
  • Regression Analysis
  • Reproducibility of Results
  • Risk Factors
  • Survival Rate