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https://doi.org/10.1073/pnas.0409462102

has been cited by the following article:

• TITLE: Survival Analysis for a Breast Cancer Data Set

  AUTHORS: Hong Li

  KEYWORDS: Proportional Hazards Model, Hypertabastic Model, Survival Function, Hazard Function, Log-Likelihood

  JOURNAL NAME: Advances in Breast Cancer Research, Vol.6 No.1, December 28, 2016

  ABSTRACT: A survival analysis on a data set of 295 early breast cancer patients is performed in this study. A new proportional hazards model, hypertabastic model was applied in the survival analysis. We assume a proportional hazards model, and select two sets of risk factors for death and metastasis for breast cancer patients respectively by using standard variable selection methods. To evaluate the performance of the new model and compare it with other popular distributions, Cox, Weibull and log-logistic models were fitted to the data besides the hypertabastic model. Result shows that the hypertabastic proportional hazards model outperformed all the comparison models and provided the best fit for the breast cancer data. In addition, we observed that the gene expression variable, wound response signature, combined with other clinical variables, can provide an effective model to predict the overall survival and hazard rate for breast cancer patients.