Advanced survival models [electronic resource] / Catherine Legrand.
Material type: TextSeries: Publication details: FL : CRC Press, c2021.Edition: First editionDescription: 1 online resource (xxviii, 332 pages) : illustrations (black and white)ISBN:- 9780429054167
- 0429054165
- 9780429620409
- 0429620403
- 610.727 LEG
Item type | Current library | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|
General Books | CUTN Central Library Medicine, Technology & Management | Non-fiction | 610.727 LEG (Browse shelf(Opens below)) | Available | 47485 |
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610.724 CHO Adaptive design methods in clinical trials | 610.724 MUN. Nutrition Assessment: Clinical and Research Applications: Clinical and Research Applications / | 610.724 RAB Biomarker Analysis in Clinical Trials with R / | 610.727 LEG Advanced survival models | 610.734 HOL Community health nursing : | 610.734 LAL Textbook of community medicine | 610.92 INT Work life balance among doctors of corporate hospitals in Chennai / |
"A Chapman & Hall book"
1. Introduction
2. Classical Survival Analysis
3. Frailty Models
4. Cure Models
5. Competing Risks
6. Joint Modeling
Online version restricted to NUS staff and students only through NUSNET.
Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.
Mode of access: World Wide Web.
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