… Race and origin, and year of diagnosis were independent … plots (shape and scale), and
considered the influence of time [… multivariable Cox proportional hazard model; the performance …

… analysis of the proposed Bayesian model provides information on which variables can strongly
influence … Figure 8 presents the diagnostic model for determining the environmental risk …

… traditional tests and statistical methods with their Bayesian … Thus, the influence of the prior is
negligible here, so that only … For example, JASP offers no options for survival analysis, which …

… Bayesian models are those which apply Bayesian theorem [108], a statistical inference …
for Bayesian theorem application. For predictive maintenance purposes, Bayesian models can …

… ANOVA or Kruskall-Wallis tests for the … analysis, logistic regression was used to analyze
the relationship between IAB and the presence of CI at baseline. The censored survival analysis …

… We first used survival analysis to test whether the treatments and the species identity had
an … cover treatments (post hoc Tukey tests, p value < 0.05); indeed it presented the highest …

… modelling is suitable for rapid and effective design simulations supporting flood hazard
modelling … relationships that could have appeared to influence the work reported in this paper. …

… diagnostics reached less than 1.05 for all the four outcome parameters. The model diagnostics
of the developed model … the tumor nodules did not influence the treatment effect. However…

… The most influential of such models is Cox’s proportional … We concentrate on the use of
frailty models in survival analysis. … Bayes’ theorem and the definition of the conditional survival …

… dynamics, case identification and tracing, infection diagnosis and … hazard regression model.
Chen et al. [57] adapted the Cox … statistical models in both frequentist and Bayesian families …