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Survival model and estimation for lu...
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Yuan, Xingchen.
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Survival model and estimation for lung cancer patients.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Survival model and estimation for lung cancer patients./
作者:
Yuan, Xingchen.
面頁冊數:
51 p.
附註:
Source: Masters Abstracts International, Volume: 43-05, page: 1738.
Contained By:
Masters Abstracts International43-05.
標題:
Mathematics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1425699
ISBN:
9780496998364
Survival model and estimation for lung cancer patients.
Yuan, Xingchen.
Survival model and estimation for lung cancer patients.
- 51 p.
Source: Masters Abstracts International, Volume: 43-05, page: 1738.
Thesis (M.S.)--East Tennessee State University, 2005.
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 nonlinear 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.
ISBN: 9780496998364Subjects--Topical Terms:
515831
Mathematics.
Survival model and estimation for lung cancer patients.
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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 nonlinear 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.
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