Semiparametric estimation of the cure fraction in population‐based cancer survival analysis

With rapid development in medical research, the treatment of diseases including cancer has progressed dramatically and those survivors may die from causes other than the one under study, especially among elderly patients. Motivated by the Surveillance, Epidemiology, and End Results (SEER) female bre...

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Bibliographic Details
Published in:Statistics in medicine 2020-11, Vol.39 (26), p.3787-3805
Main Authors: Gu, Ennan, Zhang, Jiajia, Lu, Wenbin, Wang, Lianming, Felizzi, Federico
Format: Article
Language:English
Subjects:
Breast cancer
Breslow estimator
Cancer
EM algorithm
mixture cure model
Mortality
perturbation
Population
Population-based studies
population‐based study
semiparametric regression
Survival analysis
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Summary:With rapid development in medical research, the treatment of diseases including cancer has progressed dramatically and those survivors may die from causes other than the one under study, especially among elderly patients. Motivated by the Surveillance, Epidemiology, and End Results (SEER) female breast cancer study, background mortality is incorporated into the mixture cure proportional hazards (MCPH) model to improve the cure fraction estimation in population‐based cancer studies. Here, that patients are “cured” is defined as when the mortality rate of the individuals in diseased group returns to the same level as that expected in the general population, where the population level mortality is presented by the mortality table of the United States. The semiparametric estimation method based on the EM algorithm for the MCPH model with background mortality (MCPH+BM) is further developed and validated via comprehensive simulation studies. Real data analysis shows that the proposed semiparametric MCPH+BM model may provide more accurate estimation in population‐level cancer study.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.8693