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A new parametric family for modelling cumulative incidence functions: application to breast cancer data

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  • Jong‐Hyeon Jeong

Abstract

Summary. Competing risks situations can be encountered in many research areas such as medicine, social science and engineering. The main stream of analyses of those competing risks data has been nonparametric or semiparametric in the statistical literature. We propose a new parametric family to parameterize the cumulative incidence function completely. The new distribution is sufficiently flexible to fit various shapes of hazard patterns in survival data and increases the efficiency of the cumulative incidence estimates over the distribution‐free approaches. A simple two‐sample parametric test statistic is also proposed to compare the cumulative incidence functions between two groups at a given time point. The new parametric approach is illustrated by using breast cancer data sets from the National Surgical Adjuvant Breast and Bowel Project.

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  • Jong‐Hyeon Jeong, 2006. "A new parametric family for modelling cumulative incidence functions: application to breast cancer data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 289-303, March.
    • Handle: RePEc:bla:jorssa:v:169:y:2006:i:2:p:289-303
    DOI: 10.1111/j.1467-985X.2006.00409.x
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    References listed on IDEAS

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    1. John Bryant & James J. Dignam, 2004. "Semiparametric Models for Cumulative Incidence Functions," Biometrics, The International Biometric Society, vol. 60(1), pages 182-190, March.
    2. Paul Meier & Theodore Karrison & Rick Chappell & Hui Xie, 2004. "The Price of Kaplan-Meier," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 890-896, January.
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    Cited by:

    1. Steve Su, 2016. "Flexible modelling of survival curves for censored data," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-20, December.

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