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|Title:||Survival analysis approaches for prostate cancer.|
|Keywords:||Survival analyis;Prostate cancer;Kaplan-Meier method;Treatment of radical prostatectomy;Primary tumor;Metastic tumor;Log-rank test;Cox proportional hazard (PH) model;Parametric model;Gleason score;Survival rate|
|Abstract:||Survival time has become an essential outcome of clinical trial, which began to emerge among the latter half of the 20th century. A present study was carried out on the survival analysis for patients with prostate cancer. The data was obtained from Memorial Sloan Kettering where each sample was collected from the recipients of the treatment of radical prostatectomy. The Kaplan-Meier method was used to obtain and estimate the survival function and median time among the primary and metastatic tumor of prostate cancer. Results showed that the metastatic tumor has a poor survival rate compared to the primary tumor, which give us a hint that primary tumor has a higher probability of surviving. The log-rank test was used to test the differences in the survival curves. The results showed that the difference in survival rate between the patients of the two groups of tumor was significant with a p-value of 4.44e-15. The second approach was based on the efficiency of cox proportional hazards model and parametric model. Some criteria of residuals were used for judging the goodness of fit among the candidate models. The cox proportional hazard (PH) model provided an effective covariate on the hazard function. As a result of cox PH model, the influence of standard clinical prognostic factors is based on the hazard rate of prostate cancer patients. These prognostic factors are: prostate specific antigen (PSA) level at diagnosis, tumor size, Secondary Gleason grade, and Gleason score which is helpful to determine the treatment. The Gleason score [HR 4.835, 95% CI 2.7847- 8.3937, p=2.20E-08] has the most significant progression-associated prognosticators and reveal to be an effective criteria leading to death in prostate cancer. The Accelerated Failure Time (AFT) was applied to the data with four distortions. AFT with Weibull distortions was chosen to be the best model for our data by testing the AIC.|
|Appears in Collections:||Computational Sciences - Master's theses|
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