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020 |z9783319312439|q(print)
035 (OCoLC)949759423|z(OCoLC)950466140|z(OCoLC)953021490
037 com.springer.onix.9783319312453|bSpringer Nature
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072 7 MAT|x003000|2bisacsh
072 7 MAT|x029000|2bisacsh
082 04 519.5/46|223
090 QA276
100 1 Moore, Dirk Foster,|0https://id.loc.gov/authorities/names/
n88605433|eauthor.
245 10 Applied survival analysis using R /|cDirk F. Moore.
264 1 Switzerland :|bSpringer,|c2016.
300 1 online resource (xiv, 226 pages) :|billustrations (some
color).
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
340 |gpolychrome|2rdacc
347 text file|2rdaft
490 1 Use R!,|x2197-5736
504 Includes bibliographical references and indexes.
505 0 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 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.
588 0 Online resource; title from PDF title page (SpringerLink,
viewed May 19, 2016).
590 eBooks on EBSCOhost|bEBSCO eBook Subscription Academic
Collection - North America
650 0 Survival analysis (Biometry)|0https://id.loc.gov/
authorities/subjects/sh90003967
650 0 Failure time data analysis.|0https://id.loc.gov/
authorities/subjects/sh85046885
650 7 Survival analysis (Biometry)|2fast|0https://
id.worldcat.org/fast/1139649
650 7 Failure time data analysis.|2fast|0https://id.worldcat.org
/fast/919850
650 7 Life sciences: general issues.|2bicssc
650 7 Probability & statistics.|2bicssc
650 7 Epidemiology & medical statistics.|2bicssc
650 7 MATHEMATICS|xApplied.|2bisacsh
650 7 MATHEMATICS|xProbability & Statistics|xGeneral.|2bisacsh
776 08 |iPrint version:|aMoore, Dirk F.|tApplied Survival
Analysis Using R.|dCham : Springer International
Publishing, ©2016|z9783319312439
830 0 Use R!|0https://id.loc.gov/authorities/names/no2006017477
856 40 |uhttps://rider.idm.oclc.org/login?url=https://
search.ebscohost.com/login.aspx?direct=true&scope=site&
db=nlebk&AN=1181927|zOnline ebook via EBSCO. Access
restricted to current Rider University students, faculty,
and staff.
856 42 |3Instructions for reading/downloading the EBSCO version
of this ebook|uhttp://guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 |d20230203|cEBSCO|tEBSCOebooksacademic NEW 6073 Quarterly
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994 92|bRID
