Survival Analysis of Breast Cancer Using Parametric Methods
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Document Type
Event
Description
Survival analysis involves the study of time until an event of interest occurs, or lifetimes. This analysis is used to analyze how long after patients are diagnosed with breast cancer they can live while studying the variables or covariates involved. Hypotheses tests are performed on the different attributes and conclusions are made based on their significance in predicting survival. In this research, I shall perform hypothesis testing to determine differences in survival functions for different groups.
Parametric models are used to represent the relationship between the survival time in months of patients diagnosed with breast cancer and the covariates or the explanatory variables. The parametric models we focus on in this study include Weibull distribution, Exponential distribution, Log-normal, and log- Logistic. These distribution models are thoroughly explained and analyzed for proportional hazards in this study. Accelerated failure time (AFT) models which are parametric regression analysis for log- linear models is discussed and a comparison is made to the parametric proportional hazard function.
Keywords
survival, breast cancer, ethnicity, parametric
Degree
Master of Science (MS)
Department
Mathematics and Statistics
College
Science, Engineering and Technology
First Faculty Advisor's Name
Mezbahur Rahman
First Faculty Advisor's Department
Mathematics and Statistics
First Faculty Advisor's College
Science, Engineering and Technology
Recommended Citation
Jepng'etich, L.R. (2017, April 17). Survival Analysis of Breast Cancer Using Parametric Methods. Presented at the 2017 Graduate Online Symposium, Mankato, MN. http://cornerstone.lib.mnsu.edu/gos/2017/presentation/5
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Presentation Handout
Survival Analysis of Breast Cancer Using Parametric Methods
Survival analysis involves the study of time until an event of interest occurs, or lifetimes. This analysis is used to analyze how long after patients are diagnosed with breast cancer they can live while studying the variables or covariates involved. Hypotheses tests are performed on the different attributes and conclusions are made based on their significance in predicting survival. In this research, I shall perform hypothesis testing to determine differences in survival functions for different groups.
Parametric models are used to represent the relationship between the survival time in months of patients diagnosed with breast cancer and the covariates or the explanatory variables. The parametric models we focus on in this study include Weibull distribution, Exponential distribution, Log-normal, and log- Logistic. These distribution models are thoroughly explained and analyzed for proportional hazards in this study. Accelerated failure time (AFT) models which are parametric regression analysis for log- linear models is discussed and a comparison is made to the parametric proportional hazard function.
