大葉大學圖書館 |

Applied survival analysis using R[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.546
書名/作者:	Applied survival analysis using R/ by Dirk F. Moore.
作者:	Moore, Dirk F.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xiv, 226 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Survival analysis (Biometry)
標題:	Failure time data analysis.
標題:	Statistics.
標題:	Statistics for Life Sciences, Medicine, Health Sciences.
標題:	Biostatistics.
標題:	Statistical Theory and Methods.
標題:	Epidemiology.
ISBN:	9783319312453
ISBN:	9783319312439
內容註:	Introduction -- Basic Principles of Survival Analysis -- Nonparametric Survival Curve Estimation -- Nonparametric Comparison of Survival Distributions -- Regression Analysis Using the Proportional Hazards Model -- Model Selection and Interpretation -- Model Diagnostics -- Time Dependent Covariates -- Multiple Survival Outcomes and Competing Risks -- Parametric Models -- Sample Size Determination for Survival Studies -- Additional Topics -- References -- Appendix A -- Index -- R Package Index.
摘要、提要註:	Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics. Clearly illustrates concepts of survival analysis principles and analyzes actual survival data using R, in addition to including an appendix with a basic introduction to R Organized via basic concepts and most frequently used procedures, with advanced topics toward the end of the book and in appendices Includes multiple original data sets that have not appeared in other textbooks Dirk F. Moore is Associate Professor of Biostatistics at the Rutgers School of Public Health and the Rutgers Cancer Institute of New Jersey. He received a Ph.D. in biostatistics from the University of Washington in Seattle and, prior to joining Rutgers, was a faculty member in the Statistics Department at Temple University. He has published numerous papers on the theory and application of survival analysis and other biostatistics methods to clinical trials and epidemiology studies.
電子資源:	http://dx.doi.org/10.1007/978-3-319-31245-3

Applied survival analysis using R[electronic resource] /
Moore, Dirk F.

Applied survival analysis using R[electronic resource] /by Dirk F. Moore. - Cham :Springer International Publishing :2016. - xiv, 226 p. :ill. (some col.), digital ;24 cm. - Use R!,2197-5736. - Use R!.

Introduction -- Basic Principles of Survival Analysis -- Nonparametric Survival Curve Estimation -- Nonparametric Comparison of Survival Distributions -- Regression Analysis Using the Proportional Hazards Model -- Model Selection and Interpretation -- Model Diagnostics -- Time Dependent Covariates -- Multiple Survival Outcomes and Competing Risks -- Parametric Models -- Sample Size Determination for Survival Studies -- Additional Topics -- References -- Appendix A -- Index -- R Package Index.

Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics. Clearly illustrates concepts of survival analysis principles and analyzes actual survival data using R, in addition to including an appendix with a basic introduction to R Organized via basic concepts and most frequently used procedures, with advanced topics toward the end of the book and in appendices Includes multiple original data sets that have not appeared in other textbooks Dirk F. Moore is Associate Professor of Biostatistics at the Rutgers School of Public Health and the Rutgers Cancer Institute of New Jersey. He received a Ph.D. in biostatistics from the University of Washington in Seattle and, prior to joining Rutgers, was a faculty member in the Statistics Department at Temple University. He has published numerous papers on the theory and application of survival analysis and other biostatistics methods to clinical trials and epidemiology studies.

ISBN: 9783319312453

Standard No.: 10.1007/978-3-319-31245-3doiSubjects--Topical Terms:

431338
Survival analysis (Biometry)

LC Class. No.: QA276

Dewey Class. No.: 519.546

Applied survival analysis using R[electronic resource] /
LDR:03839nam a2200337 a 4500 001 450776
003 DE-He213
005 20161102095651.0
006 m d
007 cr nn 008maaau
008 161210s2016 gw s 0 eng d
020 $a 9783319312453 $q (electronic bk.)
020 $a 9783319312439 $q (paper)
024 7 $a 10.1007/978-3-319-31245-3 $2 doi
035 $a 978-3-319-31245-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MBNS $2 bicssc
072 7 $a MED090000 $2 bisacsh
082 0 4 $a 519.546 $2 23
090 $a QA276 $b .M821 2016
100 1 $a Moore, Dirk F. $3 647261
245 1 0 $a Applied survival analysis using R $h [electronic resource] / $c by Dirk F. Moore.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xiv, 226 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Use R!, $x 2197-5736
505 0 $a Introduction -- Basic Principles of Survival Analysis -- Nonparametric Survival Curve Estimation -- Nonparametric Comparison of Survival Distributions -- Regression Analysis Using the Proportional Hazards Model -- Model Selection and Interpretation -- Model Diagnostics -- Time Dependent Covariates -- Multiple Survival Outcomes and Competing Risks -- Parametric Models -- Sample Size Determination for Survival Studies -- Additional Topics -- References -- Appendix A -- Index -- R Package Index.
520 $a Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics. Clearly illustrates concepts of survival analysis principles and analyzes actual survival data using R, in addition to including an appendix with a basic introduction to R Organized via basic concepts and most frequently used procedures, with advanced topics toward the end of the book and in appendices Includes multiple original data sets that have not appeared in other textbooks Dirk F. Moore is Associate Professor of Biostatistics at the Rutgers School of Public Health and the Rutgers Cancer Institute of New Jersey. He received a Ph.D. in biostatistics from the University of Washington in Seattle and, prior to joining Rutgers, was a faculty member in the Statistics Department at Temple University. He has published numerous papers on the theory and application of survival analysis and other biostatistics methods to clinical trials and epidemiology studies.
650 0 $a Survival analysis (Biometry) $3 431338
650 0 $a Failure time data analysis. $3 605142
650 1 4 $a Statistics. $3 145349
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 464109
650 2 4 $a Biostatistics. $3 464330
650 2 4 $a Statistical Theory and Methods. $3 464135
650 2 4 $a Epidemiology. $3 342217
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Use R! $3 465790
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-31245-3
950 $a Mathematics and Statistics (Springer-11649)