Next-Gen Lifetime Data Analysis
Emerging Innovations and Applications
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Beschreibung
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.
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.
Provides readers with a quick and comprehensive understanding of key concepts in lifetime data analysis topics Equips readers with skills for the AI era lifetime data analysis, cure models, competing risks & deep learning Includes new advancements in statistical and biostatistical methods, algorithms, and applications to real-world datasets
Autor*in
Mingyue Du
Themen in »Next-Gen Lifetime Data Analysis«
Additive hazards model Cox model semiparametric transformation model competing risks cure model variable selection randomized trials lasso missing data multiple imputation interval-censored data current status data survival analysis machine learning big data