A simulation study of the number of events per variable in logistic regression analysis.
We performed a Monte Carlo study to evaluate the effect of the number of events per variable (EPV) analyzed in logistic regression analysis. The simulations were based on data from a cardiac trial of 673 patients in which 252 deaths occurred and seven variables were cogent predictors of mortality; the number of events per predictive variable was (252/7 =) 36 for the full sample. For the simulations, at values of EPV = 2, 5, 10, 15, 20, and 25, we randomly generated 500 samples of the 673 patients, chosen with replacement, according to a logistic model derived from the full sample. Simulation results for the regression coefficients for each variable in each group of 500 samples were compared for bias, precision, and significance testing against the results of the model fitted to the original sample. For EPV values of 10 or greater, no major problems occurred. For EPV values less than 10, however, the regression coefficients were biased in both positive and negative directions; the large sample variance estimates from the logistic model both overestimated and underestimated the sample variance of the regression coefficients; the 90% confidence limits about the estimated values did not have proper coverage; the Wald statistic was conservative under the null hypothesis; and paradoxical associations (significance in the wrong direction) were increased. Although other factors (such as the total number of events, or sample size) may influence the validity of the logistic model, our findings indicate that low EPV can lead to major problems.
Peduzzi
P
P
Cooperative Studies Program Coordinating Center, Veterans Affairs Medical Center, West Haven Connecticut 06516, USA.
Concato
J
J
Kemper
E
E
Holford
T R
TR
Feinstein
A R
AR
D015982:Bias; D003198:Computer Simulation; D001026:Coronary Artery Bypass; D003327:Coronary Disease / Q000401:mortality* / Q000601:surgery; D006801:Humans; D016015:Logistic Models; D009010:Monte Carlo Method; D012044:Regression Analysis; D015203:Reproducibility of Results