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

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dc.contributor.author Fei, Mingwei
dc.date.accessioned 2012-03-15T13:10:16Z
dc.date.available 2012-03-15T13:10:16Z
dc.date.issued 2012-03-15
dc.identifier.uri http://hdl.handle.net/2097/13528
dc.description.abstract 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. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Survival analysis en_US
dc.subject Proportional hazards(PH) model en_US
dc.subject Accelerated failure time(AFT) model en_US
dc.subject Covariate effect test en_US
dc.title A study of the robustness of Cox's proportional hazards model used in testing for covariate effects en_US
dc.type Report en_US
dc.description.degree Master of Arts en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Paul Nelson en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2012 en_US
dc.date.graduationmonth May en_US


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