Cure Rate Models

  • Joseph G. Ibrahim
  • Ming-Hui Chen
  • Debajyoti Sinha
Part of the Springer Series in Statistics book series (SSS)


Survival models incorporating a cure fraction, often referred to as cure rate models, are becoming increasingly popular in analyzing data from cancer clinical trials. The cure rate model has been used for modeling time-to-event data for various types of cancers, including breast cancer, non-Hodgkins lymphoma, leukemia, prostate cancer, melanoma, and head and neck cancer, where for these diseases, a significant proportion of patients are “cured.” Perhaps the most popular type of cure rate model is the mixture model discussed by Berkson and Gage (1952). In this model, we assume a certain fraction π of the population is “cured,” and the remaining 1 – π are not cured.


Posterior Distribution Failure Time Markov Chain Monte Carlo Algorithm High Posterior Density Posterior Estimate 
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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Joseph G. Ibrahim
    • 1
  • Ming-Hui Chen
    • 2
  • Debajyoti Sinha
    • 3
  1. 1.Department of BiostatisticsHarvard School of Public Health and Dana-Farber Cancer InstituteBostonUSA
  2. 2.Department of Mathematical SciencesWorcester Polytechnic InstituteWorcesterUSA
  3. 3.Department of Biometry and EpidemiologyMedical Universtiy of South CarolinaCharlestonUSA

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