With the advances in cancer research, more and more cancer patients are cured, and they will not relapse or die due to the cancer. It becomes increasingly important to know a patient's chance of being cured given a set of risk factors of the patient, or the survival rate at a certain time if the patient is not cured. The primary aim of this project is to develop a new and 0exible mixture cure model for estimating the proportion of cured patients, and the survival probability of uncured patients. Current methodologies in this area assume that the treatment is e(R)ective at the early stage of trials, while the new model can allow the treatment to have no e(R)ect at the initial time and a gradual e(R)ect later on for uncured patients. This distinctive feature makes the proposed model an important alternative model for practitioners or researchers involved in cancer epidemiology studies in modeling censored survival data with long term survivors. We will develop a semiparametric estimation method, compare it with existing models and methods, evaluate all models by the simulation study, and apply the model to breast cancer data sets. The study will provide practitioners an important tool in analyzing the cancer data with cured fraction and evaluating the risk e(R)ects. The development of the software in the R environment will enable the possible use of the mixture cure model in the epidemiology cancer study easily.

Public Health Relevance

Narrative With the advances in cancer research, more and more cancer patients are cured. This project will investigate a new model for analyzing the cancer survival data with cured patients. The software development in R environment will enable the proposed method used easily in epidemiology cancer study.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Research Grants (R03)
Project #
5R03CA137790-02
Application #
7901018
Study Section
Special Emphasis Panel (ZCA1-SRRB-D (M1))
Program Officer
Feuer, Eric J
Project Start
2009-08-01
Project End
2012-07-31
Budget Start
2010-08-01
Budget End
2012-07-31
Support Year
2
Fiscal Year
2010
Total Cost
$72,576
Indirect Cost
Name
University of South Carolina at Columbia
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
041387846
City
Columbia
State
SC
Country
United States
Zip Code
29208
Cai, Chao; Wang, Songfeng; Lu, Wenbin et al. (2014) NPHMC: an R-package for estimating sample size of proportional hazards mixture cure model. Comput Methods Programs Biomed 113:290-300
Zhang, Jiajia; Peng, Yingwei; Li, Haifen (2013) A New Semiparametric Estimation Method for Accelerated Hazards Mixture Cure Model. Comput Stat Data Anal 59:95-102
Zhang, Jiajia; Peng, Yingwei (2012) Semiparametric Estimation Methods for the Accelerated Failure Time Mixture Cure Model. J Korean Stat Soc 41:415-422
Cai, Chao; Zou, Yubo; Peng, Yingwei et al. (2012) smcure: an R-package for estimating semiparametric mixture cure models. Comput Methods Programs Biomed 108:1255-60
Li, Haifen; Zhang, Jiajia; Tang, Yincai (2012) Induced Smoothing for the Semiparametric Accelerated Hazards Model. Comput Stat Data Anal 56:4312-4319
Zhang, Jiajia; Peng, Yingwei; Zhao, Ou (2011) A new semiparametric estimation method for accelerated hazard model. Biometrics 67:1352-60
Zhang, Jiajia; Peng, Yingwei (2009) Crossing Hazard Functions in Common Survival Models. Stat Probab Lett 79:2124-2130
Zhang, Jiajia; Peng, Yingwei (2009) Accelerated hazards mixture cure model. Lifetime Data Anal 15:455-67