Applied Survival Analysis

From Univariate to Complex Outcomes
Erscheinungsjahr 2027
ISBN: 978-3-032-43699-3
Verlag: Springer

Buch, Englisch, 543 Seiten, Format (B × H): 178 mm x 254 mm

Reihe: Springer Texts in Statistics

This textbook introduces survival analysis from standard methods for a single event to approaches for complex time-to-event outcomes. It connects statistical concepts with practical questions arising in biomedical research, using real data examples and accompanying R code to illustrate how methods are applied and results interpreted.

The book is organized into three parts. Part I develops the foundations of univariate survival analysis, including nonparametric estimation and testing, Cox regression, alternative regression models, study design, and methods for truncated and interval-censored data. Part II covers multivariate and recurrent events, competing and semi-competing risks, joint analysis of longitudinal and survival data, multistate models, and composite endpoints. Part III introduces causal inference and machine learning for survival outcomes, connecting these topics to the concepts developed earlier.

Throughout, the emphasis is on understanding what a method estimates, the assumptions it requires, and how to use it in practice. Exercises range from methodological derivations to data analyses in R.

Designed primarily for advanced undergraduate and graduate students in statistics and biostatistics, the book also serves researchers and quantitatively trained health science professionals who work with time-to-event data.

Zielgruppe

Lower undergraduate

Autoren/Hrsg.

Mao, Lu

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Part I Univariate Events.- 1 Introduction.- 2 Mathematical Foundations.-3 Nonparametric Estimation and Testing.- 4 Cox Proportional Hazards Regression.- 5 Other Non- and Semi-parametric Methods.- 6 Sample Size Determination and Study Design.- 7 Left Truncation and Interval Censoring.- Part II Complex Outcomes.- 8 Multivariate Failure Times.- 9 Recurrent Events.- 10 Competing and Semi-Competing Risks.- 11 Joint Analysis of Longitudinal and Survival Data.- 12 Multistate Processes and Models.- 13 Composite Endpoints.- Part III Special Topics.- 14 Causal Inference in Survival Analysis.- 15 Machine Learning in Survival Analysis.- Index.

Über Autor(innen)

Lu Mao is an Associate Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison, where he teaches graduate courses in survival analysis and related statistical methods. His research focuses on survival analysis, causal inference, semiparametric theory, and clinical trials, with particular emphasis on complex time-to-event outcomes. His methodological research has received support from the National Institutes of Health and the National Science Foundation. He also collaborates with researchers in cardiology, radiology, oncology, and health behavioral interventions, bringing practical questions from these studies into his methodological work and teaching.