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Models for survival data from cancer prevention studies

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  • Debajyoti Sinha
  • Joseph G. Ibrahim
  • Ming‐Hui Chen

Abstract

Summary. We propose a new parametric survival model for cancer prevention studies. The formulation of the model is in the spirit of stochastic modelling of the occurrences of tumours through two stages: initiation of an undetected tumour and promotion of the tumour to a detectable cancer. Several novel properties of the model proposed are derived. In addition, we examine the relationship of our model with the existing lagged regression model of Zucker and Lakatos. Also, we bridge the difference between two distinct stochastic modelling methods for cancer data, one used primarily for cancer therapeutic trials and the other used for cancer prevention trials.

Suggested Citation

  • Debajyoti Sinha & Joseph G. Ibrahim & Ming‐Hui Chen, 2002. "Models for survival data from cancer prevention studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 467-477, August.
    • Handle: RePEc:bla:jorssb:v:64:y:2002:i:3:p:467-477
    DOI: 10.1111/1467-9868.00345
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    Cited by:

    1. Michael L. Pennell & David B. Dunson, 2006. "Bayesian Semiparametric Dynamic Frailty Models for Multiple Event Time Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1044-1052, December.

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