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Analysis of cure rate survival data under proportional odds model

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Abstract

Due to significant progress in cancer treatments and management in survival studies involving time to relapse (or death), we often need survival models with cured fraction to account for the subjects enjoying prolonged survival. Our article presents a new proportional odds survival models with a cured fraction using a special hierarchical structure of the latent factors activating cure. This new model has same important differences with classical proportional odds survival models and existing cure-rate survival models. We demonstrate the implementation of Bayesian data analysis using our model with data from the SEER (Surveillance Epidemiology and End Results) database of the National Cancer Institute. Particularly aimed at survival data with cured fraction, we present a novel Bayes method for model comparisons and assessments, and demonstrate our new tool’s superior performance and advantages over competing tools.

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Authors and Affiliations

  1. Department of Statistics, Florida State University, Tallahassee, FL, 32310-5608, USA

    Yu Gu & Debajyoti Sinha

  2. MMC 303, School of Public Health, University of Minnesota, Minneapolis, MN, 55455-0392, USA

    Sudipto Banerjee

Authors
  1. Yu Gu
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  2. Debajyoti Sinha
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  3. Sudipto Banerjee
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Correspondence to Yu Gu.

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Gu, Y., Sinha, D. & Banerjee, S. Analysis of cure rate survival data under proportional odds model. Lifetime Data Anal 17, 123–134 (2011). https://doi.org/10.1007/s10985-010-9171-z

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  • DOI: https://doi.org/10.1007/s10985-010-9171-z

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