Master of Arts / Department of Statistics / Paul Nelson / 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.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/13528 |
| Date | January 1900 |
| Creators | Fei, Mingwei |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Report |
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