Introduction to Multivariate Failure Time Data. Bivariate Survivor Function Representation and Estimation. Regression Analysis of Bivariate Failure Time Data. Transformation Models, Frailties and Copulas for Bivariate Failure Time Regression. Regression Analysis of Higher Dimensional Failure Time Data. Recurrent Events and Life History Analysis. Missing and Mismeasured Data in Multivariate Failure Time Analysis. Other Failure Time Data Analysis Topics.
Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine.
Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.
"Here, Prentice (Univ. of Washington) and Zhao (National Inst. of
Environmental Health Sciences) provide a systematic introduction to
novel statistical methodology, using a “marginal modeling approach”
relevant to a number of fields where interpretation of survival
outcomes and failure over time data is required.The authors explore
the entirety of each method covered, progressing from background
mathematics to assumptions and caveats, and finally to
interpretation. Intended for biostatistical researchers engaged in
analysis of complex population data sets as encountered, for
example, in randomized clinical trials, this volume may also serve
as a reference for quantitative epidemiologists. Readers will need
a solid understanding of statistical estimation methods and a
reasonable command of calculus and probability theory. Appropriate
exercises accompany each chapter, and links to software and sample
data are provided (appendix B)."
~K. J. Whitehair, independent scholar, CHOICE, January 2020 Vol. 57
No. 5Summing Up: Recommended. Graduate students, faculty and
practitioners."This book gives thorough coverage and rigorous
discussion of statistical methods for the analysis of multivariate
failure time data. The structure of the book has been thoughtfully
planned and it is carefully and clearly written - it does a nice
job of clearly introducing concepts and models, as well as
describing nonparametric methods of estimation. For the core theme
on the analysis of multiple failure times, it explores different
approaches to estimation and inference, and critiques competing
methods in terms of robustness and efficiency. Authoritative
coverage of additional topics including recurrent event analysis,
multistate modeling, dependent censoring, and others, ensures it
will serve as an excellent reference for those with interest in
life history analysis. Illustrative examples given in the chapters
help make the issues and approaches for dealing with them tangible,
while the exercises at the end of each chapter give readers an
opportunity to gauge their understanding of the material. It will
therefore also serve very nicely as a basis for a second graduate
course on specialized topics of life history analysis."
~Richard Cook, University of Waterloo"Let me congratulate the
authors with this impressive work…This book could be a textbook for
an advanced masters or Ph.D level course for a degree in
biostatistics and statistics…This book focusses on the case that we
want to understand the association between covariate process and a
multivariate survival outcome. It includes targeting the univariate
conditional hazards as well as the multivariate hazards functions.
Instead of targeting intensities that condition on the full
observed history, it focusses on histories that exclude the failure
time history, so that a change in the Z process represents a change
in future multivariate survival experience. The book also reviews
copula models, frailty models, and models of intensities of
counting processes, beyond their marginal hazard modeling
approach…An important strength is the illustrations with real world
interesting data from the Women's Health Study. Another important
strength is its overview of various competing approaches, making it
comprehensive, beyond the presentation of the unique marginal
modeling approach developed by the authors. ~Mark van der Laan,
University of California, Berkeley"I expect this book to be highly
useful to: (i) researches dealing with developing statistical
multivariate survival methods; (ii) teachers of advanced survival
methods for graduate classes; and (iii) Biostatistics/statistics
PhD students focusing in the area of multivariate survival
analysis. I believe that the book would be very useful as a
reference and as a textbook. I, personally, would definitely use it
for both purposes.. There is a need for a well-organized book
focusing mainly on the recent developments in this research area,
that are not included in older books...The manuscript is
technically correct, very clearly written, and it is a pleasure
reading it." (
~Malka Gorfine, Tel Aviv University"This well-written book offers
the basics of innovative approach to analyse and interpret
correlated failure times data . . . I enjoyed reading this book. I
highly recommend this book to statistics researchers, graduate
students, engineers, and computing professionals."
~ Ramalingam Shanmugam, Texas State University
"Here, Prentice (Univ. of Washington) and Zhao (National Inst. of
Environmental Health Sciences) provide a systematic introduction to
novel statistical methodology, using a “marginal modeling approach”
relevant to a number of fields where interpretation of survival
outcomes and failure over time data is required.The authors explore
the entirety of each method covered, progressing from background
mathematics to assumptions and caveats, and finally to
interpretation. Intended for biostatistical researchers engaged in
analysis of complex population data sets as encountered, for
example, in randomized clinical trials, this volume may also serve
as a reference for quantitative epidemiologists. Readers will need
a solid understanding of statistical estimation methods and a
reasonable command of calculus and probability theory. Appropriate
exercises accompany each chapter, and links to software and sample
data are provided (appendix B)."
~K. J. Whitehair, independent scholar, CHOICE, January 2020 Vol. 57
No. 5Summing Up: Recommended. Graduate students, faculty and
practitioners."This book gives thorough coverage and rigorous
discussion of statistical methods for the analysis of multivariate
failure time data. The structure of the book has been thoughtfully
planned and it is carefully and clearly written - it does a nice
job of clearly introducing concepts and models, as well as
describing nonparametric methods of estimation. For the core theme
on the analysis of multiple failure times, it explores different
approaches to estimation and inference, and critiques competing
methods in terms of robustness and efficiency. Authoritative
coverage of additional topics including recurrent event analysis,
multistate modeling, dependent censoring, and others, ensures it
will serve as an excellent reference for those with interest in
life history analysis. Illustrative examples given in the chapters
help make the issues and approaches for dealing with them tangible,
while the exercises at the end of each chapter give readers an
opportunity to gauge their understanding of the material. It will
therefore also serve very nicely as a basis for a second graduate
course on specialized topics of life history analysis."
~Richard Cook, University of Waterloo"Let me congratulate the
authors with this impressive work…This book could be a textbook for
an advanced masters or Ph.D level course for a degree in
biostatistics and statistics…This book focusses on the case that we
want to understand the association between covariate process and a
multivariate survival outcome. It includes targeting the univariate
conditional hazards as well as the multivariate hazards functions.
Instead of targeting intensities that condition on the full
observed history, it focusses on histories that exclude the failure
time history, so that a change in the Z process represents a change
in future multivariate survival experience. The book also reviews
copula models, frailty models, and models of intensities of
counting processes, beyond their marginal hazard modeling
approach…An important strength is the illustrations with real world
interesting data from the Women's Health Study. Another important
strength is its overview of various competing approaches, making it
comprehensive, beyond the presentation of the unique marginal
modeling approach developed by the authors. ~Mark van der Laan,
University of California, Berkeley"I expect this book to be highly
useful to: (i) researches dealing with developing statistical
multivariate survival methods; (ii) teachers of advanced survival
methods for graduate classes; and (iii) Biostatistics/statistics
PhD students focusing in the area of multivariate survival
analysis. I believe that the book would be very useful as a
reference and as a textbook. I, personally, would definitely use it
for both purposes.. There is a need for a well-organized book
focusing mainly on the recent developments in this research area,
that are not included in older books...The manuscript is
technically correct, very clearly written, and it is a pleasure
reading it." (
~Malka Gorfine, Tel Aviv University"This well-written book offers
the basics of innovative approach to analyse and interpret
correlated failure times data . . . I enjoyed reading this book. I
highly recommend this book to statistics researchers, graduate
students, engineers, and computing professionals."
~ Ramalingam Shanmugam, Texas State University
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