Dynamic Modelling of Time-to-Event Processes

by Gangaram S. Ladde, Emmanuel A. Appiah, Jay G. Ladde

Overview

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

Product Details

ISBN-13:	9780443223426
Publisher:	Elsevier Science
Publication date:	10/20/2025
Series:	Advances in Reliability Science
Sold by:	Barnes & Noble
Format:	eBook
Pages:	350
File size:	22 MB
Note:	This product may take a few minutes to download.

About the Author

Gangaram S. Ladde is a Professor of Mathematics and Statistics at the University of South Florida (since 2007). Prior to that he was Professor of Mathematics at the University of Texas at Arlington (1980-2007). He received his Ph.D. in Mathematics from the University of Rhode Island in 1972. He has published more than 190 peer-reviewed articles, co-authored four monographs, and co-edited six proceedings of international conferences, including 'Introduction to Differential Equations: Stochastic Modeling, Methods and Analysis' (World Scientific Publishing Company, Singapore, 2013); 'Stochastic versus Deterministic Systems of Differential Equations' (Inc, New York, 2004) and 'Random Differential Inequalities' (Academic Press, New York, 1980). Professor Ladde is the Founder and joint Editor-in-Chief (1983-present) of the Journal of Stochastic Analysis and Applications. He is also an Editorial Board member of several Mathematical Science journals and the recipient of research awards and grants. Recently, Dr. Ladde research team's innovative research work is technologically transferred as: United States Patent in 2021 (another work is pending.)Emmanuel A. Appiah is an Assistant Professor of Mathematics at Prairie View A&M University. His research focuses on integrating mathematics, statistics, and computer science to address challenges in healthcare, epidemiology, and the social sciences.Dr. Jay Ladde is an emergency medicine physician in Orlando, Florida. He is the Senior Associate Program Director of Emergency Medicine, Orlando Health, Florida. Prior to this, he was the Associate Program Director of Emergency Medicine at the Orlando Regional Medical Center, Florida. He received his medical degree (MD) from Baylor College of Medicine, Texas, and has been in practice for more than 20 years. Dr. Ladde has held faculty appointments at various universities. He is currently a Clinical Professor at the University of Central Florida, Florida. He is the chair of the Florida College of Emergency Physicians Council of Residencies Committee and co-chair of the Florida College of Emergency Physicians Education and Academic Affairs Committee. Dr. Ladde has published several peer-reviewed articles in reputable medical journals.

Table of Contents

1. Some Latent Dynamic Structural Elements in Time-to-Event Processes2. Linear Deterministic Hybrid Dynamic Modeling of Time-to-event Processes (LDHDM)3. Conceptual Computational and Simulation Algorithms - LDHDM4. Nonlinear Deterministic Interconnected Hybrid Dynamic Modeling for Time-to-Event Processes - INHDMTTEP5. Conceptual Computational and Simulation Algorithms for INHDMTTEP6. Stochastic Hybrid Dynamic Modeling for Time-to-event Processes - SIHDMTTEP7. Conceptual Computational and Simulation Algorithms for SIHDMTTEP8. Application to Time-to-Event Datasets9. Statistical Comparative Analysis with Existing Methods10. Case Studies

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From the Publisher

An innovative, dynamic approach for state and parameter estimation problems that is combined with data analysis of time to event processes