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.
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Acknowledgements
This work was made possible by the facilities of the Shared Hierarchical Academic Research Computing Network (SHARCNET: www.sharcnet.ca) and Compute/Calcul Canada. This work was supported by grants from the Natural Sciences and Engineering Research Council of Canada, the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. [T32-101/15-R]), and the Education University of Hong Kong (Ref. IDS-2 2019, MIT/SGA05/19-20).
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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: Lio, Y., Chen, DG., Ng, H.K.T., Tsai, TR. (eds) Bayesian Inference and Computation in Reliability and Survival Analysis. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-88658-5_10
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