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.