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A class of hazard rate mixtures for combining survival data from different experiments

Author

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  • Antonio Lijoi

    () (Department of Economics and Management, University of Pavia and Collegio Carlo Alberto)

  • Bernardo Nipoti

    () (University of Turin and Collegio Carlo Alberto)

Abstract

Mixture models for hazard rate functions are widely used tools for addressing the statistical analysis of survival data subject to a censoring mechanism. The present paper introduces a new class of vectors of random hazard rate functions that are expressed as kernel mixtures of dependent completely random measures. This leads to define dependent nonparametric prior processes that are suitably tailored to draw inferences in the presence of heterogeneous observations. Besides its exibility, an important appealing feature of our proposal is analytical tractability: we are, indeed, able to determine some relevant distributional properties and a posterior characterization that is also the key for devising an efficient MCMC sampler. For illustrative purposes, we specialize our general results to a class of dependent extended gamma processes. We finally display a few numerical examples, including both simulated and real two-sample datasets: these allow us to identify the effect of a borrowing strength phenomenon and provide evidence of the effectiveness of the prior to deal with datasets for which the proportional hazards assumption does not hold true.

Suggested Citation

  • Antonio Lijoi & Bernardo Nipoti, 2013. "A class of hazard rate mixtures for combining survival data from different experiments," DEM Working Papers Series 059, University of Pavia, Department of Economics and Management.
  • Handle: RePEc:pav:demwpp:demwp0059
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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0059.pdf
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    Keywords

    Bayesian nonparametrics; Completely random measures; Dependent processes; Extended gamma processes; Partial exchangeability.;
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