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Comparison performance of the Bayesian Approach with the Weibull and Birnbaum-Saunders distributions in imputation of time-to-event censors

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  • Parviz Shahmirzalou
  • Aliakbar Rasekhi
  • Majid Jafari Khaledi
  • Maryam Khayamzadeh

Abstract

Almost all survival data is censored, and censor imputation is necessary. This study aimed to investigate the performance of the Bayesian Approach (BA) in the imputation of censored records in simulated and Breast Cancer (BC) data. Due to the difference in the distribution of time to event in survival analysis, two well-known the Weibull and Birnbaum-Saunders (BS) distributions have been used to test the performance of the BA. For each of the censored, 10,000 times were simulated using the BA in R and BUGS software, and their median or mean was imputed instead of each censor. The eligibility of both imputation methods was investigated using different curves, different censoring percentages, and sample sizes, as well as the Deviance Information Criteria (DIC), Effective Sample Size, and the Geweke diagnostic in simulated and especially real BC data. The BC data, which contains 220 patients who were identified and followed up between 2015 and 2023, was made accessible on February 1, 2023. The Kaplan-Meier, the BA, and other survival curves were drawn for the observed times. Findings indicated that the performance of the BA under the Weibull and BS distributions in simulated data is similar. The DIC index in the BC data under the BS distribution (1510) is less than the Weibull distribution (1698). Therefore, the BS distribution is preferred over the Weibull for imputation of censoring times in real BC data.

Suggested Citation

  • Parviz Shahmirzalou & Aliakbar Rasekhi & Majid Jafari Khaledi & Maryam Khayamzadeh, 2024. "Comparison performance of the Bayesian Approach with the Weibull and Birnbaum-Saunders distributions in imputation of time-to-event censors," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-29, January.
    • Handle: RePEc:plo:pone00:0295977
    DOI: 10.1371/journal.pone.0295977
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    References listed on IDEAS

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    1. Shirin Moghaddam & John Newell & John Hinde, 2022. "A Bayesian Approach for Imputation of Censored Survival Data," Stats, MDPI, vol. 5(1), pages 1-19, January.
    2. Marco Geraci & Alexander McLain, 2018. "Multiple Imputation for Bounded Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 919-940, December.
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