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Next-Gen Lifetime Data Analysis de Mingyue Du

Next-Gen Lifetime Data Analysis

Editat de Mingyue Du, Ding-Geng Chen, Zhezhen Jin, Jianguo Sun
en Limba Engleză Hardback – 24 feb 2027

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Specificații

ISBN-13: 9783032307729
ISBN-10: 3032307724
Pagini: 369
Editura: Springer Nature Switzerland AG

Notă biografică

Mingyue Du is an Associate Professor of Statistics at Jilin University in Changchun, Jilin, China. Her current research interest focuses on lifetime data analysis, high-dimensional data analysis, variable selection and screening, and case cohort studies. She has published 28 research articles on statistics and biostatistics in top statistical journals. Also, she has been invited to deliver more than 20 research talks nationally and internationally and is currently serving as Associate Editors for Journal of Applied Statistics and Statistics in Biosciences. Dr. Du is an Elected Member of the International Statistical Institute.
Ding-Geng Chen is a fellow of the American Statistical Association and is currently the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. He is also an extraordinary professor and the SARChI in biostatistics at the University of Pretoria, an honorary professor at the University of KwaZulu-Natal, South Africa. He is a senior biostatistics consultant for biopharmaceuticals and government agencies with extensive expertise in biostatistics, clinical trials, and public health statistics. Dr. Chen has more than 200 referred professional publications, co-authored 11 books and co-edited 24 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health research.
Zhezhen Jin is a Professor of Biostatistics in the Department of Biostatistics in Mailman School of Public Health at Columbia University. He has been conducting statistical and biostatistical methodological research on resampling methods, survival analysis, nonparametric and semiparametric methods, smoothing methods, and statistical computing. He has also been collaborating with clinical investigators to address statistical issues in neurology, cardiology, oncology, transplantation, psychiatry, pathology and alternative medicine. He was a co-founding Editor-in-Chief of the Contemporary Clinical Trials Communication. He is Statistical Editor for the Journal of American Cardiology College—Cardiovascular Imaging and Journal of American Cardiology College—Cardiovascular Intervention, and is on the editorial board for Kidney International, the Journal of the International Society for Nephrology. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an elected member of International Statistical Institute. He served as the President of the International Chinese Statistical Association (ICSA) in 2022 and as the Chair of the Lifetime Data Science Section of ASA in 2025.
Jianguo Sun is Chair Professor at the Department of Statistics and Data Science of the Southern University of Science and Technology. He is ASA fellow, IMS fellow and Elected Member of ISI. Among his academic achievements, he is the world’s leading experts and authors in the fields of interval-censored failure time data analysis and panel count data analysis. He published the first books on each of these two fields and edited two other books on interval-censored failure time data analysis. In addition, he has done some important work on AIDS and cancer research, the analysis of doubly censored data, the analysis of longitudinal data, and chemometrics. Dr. Sun has published over 300 publications in top statistics journals and also given over 200 invited short lectures and talks. Currently, he is serving as an Editor-in-Chief for Statistics in Biosciences and Associate Editors for several other top journals, including Journal of Nonparametric Statistics, Lifetime Data Analysis, and Statistics in Medicine. In addition, he has served as Chair of the Lifetime Data Science Section of ASA and was the President of the International Chinese Statistical Association.

Cuprins

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.