A study of the robustness of Cox's proportional hazards model used in testing for covariate effects



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Kansas State University


There are two important statistical models for multivariate survival analysis, proportional hazards(PH) models and accelerated failure time(AFT) model. PH analysis is most commonly used multivariate approach for analysing survival time data. For example, in clinical investigations

where several (known) quantities or covariates, potentially affect patient prognosis, it is often

desirable to investigate one factor effect adjust for the impact of others. This report offered a

solution to choose appropriate model in testing covariate effects under different situations. In real life, we are very likely to just have limited sample size and censoring rates(people dropping

off), which cause difficulty in statistical analysis. In this report, each dataset is randomly repeated 1000 times from three different distributions (Weibull, Lognormal and Loglogistc) with

combination of sample sizes and censoring rates. Then both models are evaluated by hypothesis testing of covariate effect using the simulated data using the derived statistics, power, type I

error rate and covergence rate for each situation. We would recommend PH method when sample size is small(n<20) and censoring rate is high(p>0.8). In this case, both PH and AFT

analyses may not be suitable for hypothesis testing, but PH analysis is more robust and consistent than AFT analysis. And when sample size is 20 or above and censoring rate is 0.8 or below, AFT analysis will have slight higher convergence rate and power than PH, but not much

improvement in Type I error rates when sample size is big(n>50) and censoring rate is low(p<0.3).

Considering the privilege of not requiring knowledge of distribution for PH analysis, we concluded

that PH analysis is robust in hypothesis testing for covariate effects using data generated from an AFT model.



Survival analysis, Proportional hazards(PH) model, Accelerated failure time(AFT) model, Covariate effect test

Graduation Month



Master of Arts


Department of Statistics

Major Professor

Paul Nelson