Continuity Correction and Standard Error Calculation for Testing in Proportional Hazards Models

Standard asymptotic inference for proportional hazards models is conventionally performed by calculating a standard error for the estimate and comparing the estimate divided by the standard error to a standard normal distribution. In this paper, we compare various standard error estimates, including based on the inverse observed information, the inverse expected inverse information, and the jackknife. Furthermore, correction for continuity is compared to omitting this correction. We find that correction for continuity represents an important improvement in the quality of approximation, and furthermore note that the usual naive standard error yields a distribution closer to normality, as measured by skewness and kurtosis, than any of the other standard errors investigated.