Degree Name

MS (Master of Science)

Program

Mathematical Sciences

Date of Award

5-2005

Committee Chair or Co-Chairs

Don Hong

Committee Members

Tiejian Wu, Robert B. Gardner, Jeff R. Knisley

Abstract

Lung cancer is the most frequent fatal cancer in the United States. Following the notion in actuarial math analysis, we assume an exponential form for the baseline hazard function and combine Cox proportional hazard regression for the survival study of a group of lung cancer patients. The covariates in the hazard function are estimated by maximum likelihood estimation following the proportional hazards regression analysis. Although the proportional hazards model does not give an explicit baseline hazard function, the baseline hazard function can be estimated by fitting the data with a non-linear least square technique. The survival model is then examined by a neural network simulation. The neural network learns the survival pattern from available hospital data and gives survival prediction for random covariate combinations. The simulation results support the covariate estimation in the survival model.

Document Type

Thesis - Open Access

Copyright

Copyright by the authors.

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