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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.
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Article info
Publication history
Received in revised form:
February 8,
1995
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© 1995 Published by Elsevier Inc.
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