
Next-Gen Lifetime Data Analysis
Description
This book showcases innovative statistical and biostatistical approaches for complex lifetime data analysis in the era of artificial intelligence. It covers a wide range of topics in the rapidly evolving fields of lifetime data analysis in the AI era, including:
Statistical procedures and data analysis in lifetime data analysis
Bayesian survival analysis
Analysis of informatively censoring lifetime data
Current status lifetime data analysis
Interval-censored lifetime data analysis
Clinical trials
Competing risk analysis
Survival analysis and joint modeling
Residual analysis of lifetime data
Cure models
This book provides essential insights into cutting-edge methodologies and practical applications, making it an invaluable resource for professionals, researchers, and graduate students in biostatistics, bioinformatics, and data science. The contributors are distinguished researchers and applied scientists that discusses statistical methodologies, innovative approaches in lifetime data analysis, and their applications in biostatistics, bioinformatics, public health, and neuroscience.
As a timely and authoritative resource, it serves as a reference for professionals, researchers, and graduate students, helping to identify new directions in lifetime data analysis-related research. The latest advancements presented in this volume are invaluable for both practitioners and academics seeking to navigate the evolving landscape of statistical science and data analytics.
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Chapter 1. Semiparametric Additive Hazards Models for Missing Covariates, with Application to a Preventive HIV Vaccine Efficacy Trial.- Chapter 2. Residual Diagnostics for Generalized Linear Models with Interval-Censored Covariates.- Chapter 3. Estimation of Stratified Semiparametric Transformation Models with Partly Interval Censored Failure Time Data Under Two-Phase Sampling.- Chapter 4. Gamma frailty proportional hazards model for Regression analysis of current status data subject to informative censoring.- Chapter 5. Bayesian Discrete-time Survival Analysis for Arbitrarily Censored Data with Time-varying Effects.- Chapter 6. A Unified Cure Model with Competing Risks and Inevitable Mortality.- Chapter 7. Spatiotemporal Neural Networks for Time-to-Event Prediction Using Longitudinal Neuroimaging in Alzheimer's Disease.- Chapter 8. Multi-Layer Backward Joint Model for Dynamic Prediction of Clinical Events with Multivariate Longitudinal Predictors of Mixed Types.- Chapter 9. Combining Survival Data of Multiple Types - Methodologies and Examples.- Chapter 10. Semiparametric Analysis of Multivariate Recurrent Events with Informative Censoring.- Chapter 11. Semiparametric Bayesian Inference of Multitype Recurrent Events and a Terminal Event.- Chapter 12. Scalable Conway-Maxwell-Poisson Regression via Subsampling.- Chapter 13. Survival Function Precision under Censoring: A Comparative Study of Cox and Weibull AFT Models.- Chapter 14. Regression Model for Right-Censored Time-to-Event Data with Unknown Event Times in the Control Group currently serving as Associate Editors for Journal of Applied Statistics and Statistics in Biosciences.