Titlebook: Bayesian Inference and Computation in Reliability and Survival Analysis; Yuhlong Lio,Ding-Geng Chen,Tzong-Ru Tsai Book 2022 The Editor(s)

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Katrin Winkler,Nelly Heim,Tabea Heinzuch as the log-rank test and the Cox proportional hazards model assume non-informative censoring for time-to-event data, and mixed model analysis assumes missing-at-random (MAR) in longitudinal trials. Although such assumptions play a critical role in influencing the outcome of the analysis, there a

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Jasmine Grabher,Madeleine Grawehrdata are very common in medical and epidemiological studies. In this chapter, we discuss a Bayesian approach for correlated interval-censored data under a dynamic Cox regression model. Some methods that incorporate right censoring have been developed for time-to-event data with temporal covariate ef

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Alexander Kuteynikov,Anatoly Boyashove considered a randomized clinical trial in which both longitudinal data and survival data were collected to compare the efficacy and the safety of two antiretroviral drugs in treating patients who had failed or were intolerant of zidovudine (AZT) therapy. Using these data, we demonstrated the advan

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Bayesian Computation in a Birnbaum–Saunders Reliability Model with Applications to Fatigue Data effect of two treatments and evaluate reliability. Bayesian computation is considered for inferring on the parameters of the Birnbaum–Saunders reliability model analyzed in this work. The methodology is applied to real fatigue data with the aid of the R software.

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https://doi.org/10.1007/978-3-030-88658-5Bayes; Bayesian inference; statistical computing; reliability analysis; survival analysis

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