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Open Access Research article

Clinical and multiple gene expression variables in survival analysis of breast cancer: Analysis with the hypertabastic survival model

Mohammad A Tabatabai1, Wayne M Eby1*, Nadim Nimeh2, Hong Li1 and Karan P Singh3

Author Affiliations

1 Department of Mathematical Sciences, Cameron University, Lawton, OK, 73505, USA

2 Cancer Centers of Southwest Oklahoma, Lawton, OK, 73505, USA

3 Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35295, USA

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BMC Medical Genomics 2012, 5:63  doi:10.1186/1755-8794-5-63

Published: 14 December 2012



We explore the benefits of applying a new proportional hazard model to analyze survival of breast cancer patients. As a parametric model, the hypertabastic survival model offers a closer fit to experimental data than Cox regression, and furthermore provides explicit survival and hazard functions which can be used as additional tools in the survival analysis. In addition, one of our main concerns is utilization of multiple gene expression variables. Our analysis treats the important issue of interaction of different gene signatures in the survival analysis.


The hypertabastic proportional hazards model was applied in survival analysis of breast cancer patients. This model was compared, using statistical measures of goodness of fit, with models based on the semi-parametric Cox proportional hazards model and the parametric log-logistic and Weibull models. The explicit functions for hazard and survival were then used to analyze the dynamic behavior of hazard and survival functions.


The hypertabastic model provided the best fit among all the models considered. Use of multiple gene expression variables also provided a considerable improvement in the goodness of fit of the model, as compared to use of only one. By utilizing the explicit survival and hazard functions provided by the model, we were able to determine the magnitude of the maximum rate of increase in hazard, and the maximum rate of decrease in survival, as well as the times when these occurred. We explore the influence of each gene expression variable on these extrema. Furthermore, in the cases of continuous gene expression variables, represented by a measure of correlation, we were able to investigate the dynamics with respect to changes in gene expression.


We observed that use of three different gene signatures in the model provided a greater combined effect and allowed us to assess the relative importance of each in determination of outcome in this data set. These results point to the potential to combine gene signatures to a greater effect in cases where each gene signature represents some distinct aspect of the cancer biology. Furthermore we conclude that the hypertabastic survival models can be an effective survival analysis tool for breast cancer patients.

Hypertabastic survival models; Gene expression variables; Breast cancer biomarkers; Seventy gene signature; ErbB2 overexpression; Fibroblast core serum response