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Bayesian analysis of survival data with missing censoring indicators

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  • Naomi C. Brownstein
  • Veronica Bunn
  • Luis M. Castro
  • Debajyoti Sinha

Abstract

In some large clinical studies, it may be impractical to perform the physical examination to every subject at his/her last monitoring time in order to diagnose the occurrence of the event of interest. This gives rise to survival data with missing censoring indicators where the probability of missing may depend on time of last monitoring and some covariates. We present a fully Bayesian semi‐parametric method for such survival data to estimate regression parameters of the proportional hazards model of Cox. Theoretical investigation and simulation studies show that our method performs better than competing methods. We apply the proposed method to analyze the survival data with missing censoring indicators from the Orofacial Pain: Prospective Evaluation and Risk Assessment study.

Suggested Citation

  • Naomi C. Brownstein & Veronica Bunn & Luis M. Castro & Debajyoti Sinha, 2021. "Bayesian analysis of survival data with missing censoring indicators," Biometrics, The International Biometric Society, vol. 77(1), pages 305-315, March.
    • Handle: RePEc:bla:biomet:v:77:y:2021:i:1:p:305-315
    DOI: 10.1111/biom.13280
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    References listed on IDEAS

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    1. Thomas D. Cook & Michael R. Kosorok, 2004. "Analysis of Time-to-Event Data With Incomplete Event Adjudication," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1140-1152, December.
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