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Activity Number: 187 - Contributed Poster Presentations: Section on Nonparametric Statistics
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #304355
Title: Improvement of the Accuracy in Testing the Effect in the Cox Proportional Hazards Model Using Higher Order Approximations
Author(s): Silvie Belaskova* and Eva Fiserova and Jay Mandrekar
Companies: Fakultni Nemocnice U Sv. Anny V Brne and St. Anne’s University Hospital Brno, Czech Republic and Mayo Clinic, Rochester MN, USA
Keywords: Survival analysis; Likelihood ratio test; Wald test; Score test; Higher order approximations; Cox proportional hazards model

The Cox proportional hazards model is one of the most popular models in survival analysis. The significance of the effect of covariates on time to an event is usually verified by means of the likelihood ratio test, the Wald test, or the score test. These are large sample tests that are only the first order approximations and they do not necessarily maintain the significance level. We focus on the accuracy of these tests for small datasets for the Cox model with right-censored and left-truncated observations. Higher order approximations of the likelihood function based on the Barndorff-Nielsen formula and the Lugannani-Rice formula will be applied to improve the accuracy in testing the effects. The accuracy of these tests together with proposed approximations will be compared by means of simulations under conditions of decreasing the sample size, and increasing proportion of right-censored and left-truncated data in the Cox model with the exponential and the Weibull distribution of the baseline hazard function. We will show that higher order approximations in the combination with the Wald statistic improve the accuracy.

Authors who are presenting talks have a * after their name.

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