Dynamic Modelling of Time-to-Event Processes

Grab the definitive resource for modern survival science: Dynamic Modelling of Time-to-Event Processes by Gangaram S. Ladde, Emmanuel A. Appiah, and Jay G. Ladde delivers a clear, rigorous pathway from theory to application for statisticians, biostatisticians, engineers, and clinical trial designers.

This compelling guide opens with intuitive explanations of stochastic processes, hazard modeling, censoring, and frailty, then advances to contemporary tools for dynamic prediction and recurrent-event analysis. Rich with practical examples in survival analysis, reliability engineering, and biomedical research, the text balances parametric and nonparametric approaches, estimation strategies, and simulation techniques—making complex concepts accessible without sacrificing mathematical depth.

Readers will appreciate how the authors translate theory into real-world impact: improved modeling of patient survival, better risk forecasting for engineers, and more robust analysis for longitudinal studies. Key strengths include clear derivations, comparative discussions of proportional and nonproportional hazards, treatment of time-varying covariates, and attention to computational considerations relevant to modern software environments.

Whether you are a researcher in North America, Europe, Asia, Africa, or Australia, this book is tailored to a global audience seeking a go-to reference in dynamic time-to-event modeling. Ideal for graduate courses and professional development, it enhances both applied intuition and technical rigor.

Expand your analytical toolkit and elevate your research—secure your copy of Dynamic Modelling of Time-to-Event Processes today and start transforming event-history data into actionable insight.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.