Titre : 
Survival analysis using the SASÂ® : a practical guide 
Type de document : 
texte imprimÃ© 
Auteurs : 
D. Allison Paul 
Mention d'Ã©dition : 
2nd ed. 
Editeur : 
Cary (N.C.) : SAS Publishing 
AnnÃ©e de publication : 
2010 
Importance : 
324 p. 
ISBN/ISSN/EAN : 
9781599946405 
Prix : 
45,55 EUR 
Langues : 
Anglais (eng) 
CatÃ©gories : 
ThÃ©saurus CEREQ ANALYSE DES DONNEES ; INFORMATIQUE ; METHODOLOGIE ; MANUEL INFORMATIQUE

RÃ©sumÃ© : 
Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, databased 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, KaplanMeier estimation, accelerated failure time models, Cox regression models, and discretetime analysis. Also included are topics not usually covered in survival analysis books, such as timedependent 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 timedependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate randomeffects models for discretetime data. (4Ã¨me de couv.) 
Document Céreq : 
Non 
Permalink : 
http://pmb.cereq.fr/index.php?lvl=notice_display&id=55499 
Survival analysis using the SASÂ® : a practical guide [texte imprimÃ©] / D. Allison Paul .  2nd ed. .  Cary (N.C.)Â : SAS Publishing, 2010 .  324 p. ISBN : 9781599946405 : 45,55 EUR Langues : Anglais ( eng)
CatÃ©gories : 
ThÃ©saurus CEREQ ANALYSE DES DONNEES ; INFORMATIQUE ; METHODOLOGIE ; MANUEL INFORMATIQUE

RÃ©sumÃ© : 
Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, databased 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, KaplanMeier estimation, accelerated failure time models, Cox regression models, and discretetime analysis. Also included are topics not usually covered in survival analysis books, such as timedependent 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 timedependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate randomeffects models for discretetime data. (4Ã¨me de couv.) 
Document Céreq : 
Non 
Permalink : 
http://pmb.cereq.fr/index.php?lvl=notice_display&id=55499 
 