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Bayesian Accelerated Failure Time and its Application in Chemotherapy Drug Treatment Trial

Author

Listed:
  • Prabhash Kumar

    (Department of Medical Oncology, Tata Memorial Hospital, Bangalore, ; India)

  • Patil Vijay M

    (Department of Medical Oncology, Tata Memorial Hospital, Bangalore, ; India)

  • Noronha Vanita

    (Department of Medical Oncology, Tata Memorial Hospital, Bangalore, ; India)

  • Joshi Amit

    (Department of Medical Oncology, Tata Memorial Hospital, Bangalore, ; India)

  • Bhattacharjee Atanu

    (Department of Biometrics, Chiltern Clinical Research Ltd, Bangalore, Bangalore, ; India)

Abstract

The Cox proportional hazards model (CPH) is normally applied in clinical trial data analysis, but it can generate severe problems with breaking the proportion hazard assumption. An accelerated failure time (AFT) is considered as an alternative to the proportional hazard model. The model can be used through consideration of different covariates of interest and random effects in each section. The model is simple to fit by using OpenBugs software and is revealed to be a good fit to the Chemotherapy data.

Suggested Citation

  • Prabhash Kumar & Patil Vijay M & Noronha Vanita & Joshi Amit & Bhattacharjee Atanu, 2016. "Bayesian Accelerated Failure Time and its Application in Chemotherapy Drug Treatment Trial," Statistics in Transition New Series, Polish Statistical Association, vol. 17(4), pages 671-690, December.
    • Handle: RePEc:vrs:stintr:v:17:y:2016:i:4:p:671-690:n:14
    DOI: 10.21307/stattrans-2016-046
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    References listed on IDEAS

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    1. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    2. Stephen G. Walker & Bani K. Mallick, 1997. "Hierarchical Generalized Linear Models and Frailty Models with Bayesian Nonparametric Mixing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 845-860.
    3. Joel L. Horowitz, 1999. "Semiparametric Estimation of a Proportional Hazard Model with Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 67(5), pages 1001-1028, September.
    4. Komarek, Arnost & Lesaffre, Emmanuel, 2008. "Bayesian Accelerated Failure Time Model With Multivariate Doubly Interval-Censored Data and Flexible Distributional Assumptions," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 523-533, June.
    5. Stephen Walker & Bani K. Mallick, 1999. "A Bayesian Semiparametric Accelerated Failure Time Model," Biometrics, The International Biometric Society, vol. 55(2), pages 477-483, June.
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