Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 14-24 Tagen versandfertig
Veröffentlichung:	Juli 2018
Genre:	EDV / Informatik
ISBN:	9781635269086
EAN-Code:	9781635269086
Verlag:	SAS Institute
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 286 mm / B 221 mm / D 23 mm
Gewicht:	1109 gr
Seiten:	336
Zus. Info:	HC gerader Rücken kaschiert
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul Allison, is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. Although the book assumes only a minimal knowledge of SAS, more experienced users will learn new techniques of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, ensuring that even the uninitiated become sophisticated users of survival analysis. The main topics presented include censoring, survival curves, Kaplan-Meier estimation, accelerated failure time models, Cox regression models, and discrete-time analysis. Also included are topics not usually covered in survival analysis books, such as time-dependent covariates, competing risks, and repeated events. Survival Analysis Using SAS: A Practical Guide, Second Edition, has been thoroughly updated for SAS 9, and all figures are presented using ODS Graphics. This new edition also documents major enhancements to the STRATA statement in the LIFETEST procedure; includes a section on the PROBPLOT command, which offers graphical methods to evaluate the fit of each parametric regression model; introduces the new BAYES statement for both parametric and Cox models, which allows the user to do a Bayesian analysis using MCMC methods; demonstrates the use of the counting process syntax as an alternative method for handling time-dependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate random-effects models for discrete-time data. This book is part of the SAS Press program.