9781439856789 - modelling survival data in medical research di collett, david (1 risultati)
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Da: killarneybooks, Inagh, CLARE, Irlandakillarneybooks
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Molto buono
EUR 49,50
EUR 34,84 spedizioneSpedito da Irlanda a U.S.A.Quantità: 1 disponibili
Hardcover. Condizione: Very Good. 3rd Edition. Hardcover, third edition, xvi + 532 pages, shipping weight over 1kg, NOT ex-library. Dusty handling marks on the outer page edges. Interior is crisp, clean and bright throughout with unmarked text, free of inscriptions and stamps, firmly bound. Issued without a dust jacket. -- This…cornerstone text in the Chapman & Hall/CRC Texts in Statistical Science series provides a highly practical, non-mathematical introduction to the analysis of "time-to-event" data, specifically tailored for clinicians and medical statisticians. Core Focus and Content: The book is renowned for its clarity, focusing on the application of statistical models rather than dense mathematical proofs. - Survival Functions: Detailed guidance on estimating survival using the Kaplan-Meier and Nelson-Aalen methods. - Proportional Hazards: Extensive coverage of the Cox Regression Model, including checking the proportional hazards assumption and handling time-dependent variables. - Parametric Models: Comparison of Weibull, exponential, and log-logistic models for survival data. - Advanced Topics: Introduction to interval-censored data, frailty models (for correlated data), and competing risks. Key Features of the 3rd Edition: - Software Integration: Includes updated examples using R and SAS, reflecting modern computational standards in medical research. - New Content: Features expanded sections on sample size calculation and the analysis of recurrent event data. - Case Studies: Uses real-world medical datasets (e.g., remission times for leukemia patients or survival after heart transplants) to illustrate diagnostic techniques and model selection. - Model Diagnostics: Strong emphasis on residual analysis and identifying influential observations to ensure model validity. -- David Collett is a former Associate Director of Statistics and Clinical Audit at NHS Blood and Transplant and was previously a Professor of Medical Statistics at the University of Reading. His background in both academia and practical healthcare application makes this text a staple for "bench-to-bedside" research.
