### Abstract

Lung cancer is the most frequently occuring fatal cancer in the United States. By assuming a form for the hazard function for a group of lung cancer patients for survival study, the covariates in the hazard function are estimated by the maximum likelihood estimation following the proportional hazards regression analysis. Although the proportional hazards model does not give an explicit baseline hazard function, the function can be estimated by fitting the data with 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.

Original language | English |
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Title of host publication | Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques |

Publisher | World Scientific Publishing Co. |

Pages | 201-219 |

Number of pages | 19 |

ISBN (Electronic) | 9789812772121 |

ISBN (Print) | 9812704612, 9789812704610 |

DOIs | |

Publication status | Published - 2007 Jan 1 |

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### All Science Journal Classification (ASJC) codes

- Medicine(all)

### Cite this

*Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques*(pp. 201-219). World Scientific Publishing Co.. https://doi.org/10.1142/9789812772121_0010

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*Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques.*World Scientific Publishing Co., pp. 201-219. https://doi.org/10.1142/9789812772121_0010

**Survival model and estimation for lung cancer patients.** / Yuan, Xingchen; Hong, Don; Shyr, Yu.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Survival model and estimation for lung cancer patients

AU - Yuan, Xingchen

AU - Hong, Don

AU - Shyr, Yu

PY - 2007/1/1

Y1 - 2007/1/1

N2 - Lung cancer is the most frequently occuring fatal cancer in the United States. By assuming a form for the hazard function for a group of lung cancer patients for survival study, the covariates in the hazard function are estimated by the maximum likelihood estimation following the proportional hazards regression analysis. Although the proportional hazards model does not give an explicit baseline hazard function, the function can be estimated by fitting the data with 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.

AB - Lung cancer is the most frequently occuring fatal cancer in the United States. By assuming a form for the hazard function for a group of lung cancer patients for survival study, the covariates in the hazard function are estimated by the maximum likelihood estimation following the proportional hazards regression analysis. Although the proportional hazards model does not give an explicit baseline hazard function, the function can be estimated by fitting the data with 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.

UR - http://www.scopus.com/inward/record.url?scp=84967604801&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84967604801&partnerID=8YFLogxK

U2 - 10.1142/9789812772121_0010

DO - 10.1142/9789812772121_0010

M3 - Chapter

AN - SCOPUS:84967604801

SN - 9812704612

SN - 9789812704610

SP - 201

EP - 219

BT - Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques

PB - World Scientific Publishing Co.

ER -