A Bayesian approach for the analysis of tumorigenicity data from sacrificial experiments under Weibull lifetimes

Man Ho Alpha LING, Hon Yiu SO, Narayanaswamy BALAKRISHNAN

Research output: Chapter in Book/Report/Conference proceedingChapters

Abstract

This chapter details a Bayesian approach for inference on onset time of tumors based on tumorigenicity data from sacrificial experiments under Weibull lifetimes. We assume that both shape and scale parameters are related to various covariates in log-linear forms. Metropolis–Hastings sampling method is then used for the estimation of posterior means of quantities of interest. A simulation study and a sensitivity analysis are carried out to assess the performance of the developed Bayesian approach with different priors. A comparison is also made with the likelihood estimates determined from an EM algorithm. Finally, a known mice tumor toxicology dataset is analyzed to illustrate the developed Bayesian approach. Copyright © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationBayesian inference and computation in reliability and survival analysis
EditorsYuhlong LIO, Ding-Geng CHEN, Hon Keung Tony NG, Tzong-Ru TSAI
Place of PublicationCham
PublisherSpringer
Pages215–237
ISBN (Electronic)9783030886585
ISBN (Print)9783030886578
DOIs
Publication statusPublished - 2022

Citation

Ling, M. H., So, H. Y., & Balakrishnan, N. (2022). A Bayesian approach for the analysis of tumorigenicity data from sacrificial experiments under Weibull lifetimes. In Y. Lio, D.-G. Chen, H. K. T. Ng, & T.-R. Tsai (Eds.), Bayesian inference and computation in reliability and survival analysis (pp. 215–237). Cham: Springer.

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