Dynamic Modelling of Time-to-Event Processes covers an alternative dynamic modelling approach for studying time-to-event processes. This innovative approach covers some key elements, including the Development of continuous-time state of dynamic time-to-event processes, an Introduction of an idea of discrete-time dynamic intervention processes, Treating a time-to-event process operating/functioning under multiple time-scales formulation of continuous and discrete-time interconnected dynamic system as hybrid dynamic time-to-event process, Utilizing Euler-type discretized schemes, developing theoretical dynamic algorithms, and more.Additional elements of this process include an Introduction of conceptual and computational state and parameter estimation procedures, Developing multistage a robust mean square suboptimal criterion for state and parameter estimation, and Extending the idea conceptual computational simulation process and applying real datasets. - Presents a dynamic approach which does not require a closed-form survival/reliability distribution - Provides updates that are independent of existing Maximum likelihood, Bayesian, and Nonparametric methods - Applies to nonlinear and non-stationary interconnected large-scale dynamic systems - Includes frailty and other models in survival analysis as case studies
