Dynamic Modelling of Time-to-Event Processes (Advances in Reliability Science) - Softcover

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

Softcover

ISBN 10: 0443223432 ISBN 13: 9780443223433

Publisher: Elsevier, 2025

Synopsis

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

About this title

About the Authors

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.

From the Back Cover

Advancements in human mobility, electronic communications, technology, engineering, medical, and social sciences have further diversified and extended the role and scope of time-to-event processes and statistical data analysis.
To accommodate this growth and maximize the scope of existing methods, Dynamic Modelling of Time-to-event Processes covers an alternative dynamic modelling approach for studying time-to-event processes and statistical data analysis.
This innovative approach combines several key ideas: developing continuous-time models for time-to-event data, introducing discrete-time intervention processes, and treating these as part of a hybrid system that operates on multiple time scales. It uses Euler-type methods to create algorithms for estimating states and parameters, and includes strategies for robust, accurate estimation. The approach also extends to simulation and is applied to real data to demonstrate its effectiveness.

Key Features:

• Presents a dynamic approach that does not depend on closed-form survival or reliability distributions
• Offers methods that are independent of traditional techniques such as maximum likelihood, Bayesian, and nonparametric approaches
• Addresses the analysis of nonlinear and nonstationary stochastic and/or deterministic large-scale interconnected dynamic systems
• Includes case studies involving frailty and other survival analysis models to illustrate practical applications

Edition details

• Publisher: Elsevier
• Publication date: 2025
• Language: English
• ISBN 10: 0443223432
• ISBN 13: 9780443223433
• Binding: Paperback
• Edition number: 1
• Number of pages: 368