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This book brings together the voices of leading experts in the
frontiers of biostatistics, biomedicine, and the health sciences to
discuss the statistical procedures, useful methods, and novel
applications in biostatistics research. It also includes
discussions of potential future directions of biomedicine and new
statistical developments for health research, with the intent of
stimulating research and fostering the interactions of scholars
across health research related disciplines. Topics covered include:
Health data analysis and applications to EHR data Clinical trials,
FDR, and applications in health science Big network analytics and
its applications in GWAS Survival analysis and functional data
analysis Graphical modelling in genomic studies The book will be
valuable to data scientists and statisticians who are working in
biomedicine and health, other practitioners in the health sciences,
and graduate students and researchers in biostatistics and health.
In the statistical domain, certain topics have received
considerable attention during the last decade or so, necessitated
by the growth and evolution of data and theoretical challenges.
This growth has invariably been accompanied by computational
advancement, which has presented end users as well as researchers
with the necessary opportunities to handle data and implement
modelling solutions for statistical purposes. Showcasing the
interplay among a variety of disciplines, this book offers
pioneering theoretical and applied solutions to practice-oriented
problems. As a carefully curated collection of prominent
international thought leaders, it fosters collaboration between
statisticians and biostatisticians and provides an array of thought
processes and tools to its readers. The book thereby creates an
understanding and appreciation of recent developments as well as an
implementation of these contributions within the broader framework
of both academia and industry. Computational and Methodological
Statistics and Biostatistics is composed of three main themes: *
Recent developments in theory and applications of statistical
distributions;* Recent developments in supervised and unsupervised
modelling;* Recent developments in biostatistics; and also features
programming code and accompanying algorithms to enable readers to
replicate and implement methodologies. Therefore, this monograph
provides a concise point of reference for a variety of current
trends and topics within the statistical domain. With
interdisciplinary appeal, it will be useful to researchers,
graduate students, and practitioners in statistics, biostatistics,
clinical methodology, geology, data science, and actuarial science,
amongst others.
This book examines statistical methods and models used in the
fields of global health and epidemiology. It includes methods such
as innovative probability sampling, data harmonization and
encryption, and advanced descriptive, analytical and monitory
methods. Program codes using R are included as well as real data
examples. Contemporary global health and epidemiology involves a
myriad of medical and health challenges, including inequality of
treatment, the HIV/AIDS epidemic and its subsequent control, the
flu, cancer, tobacco control, drug use, and environmental
pollution. In addition to its vast scales and telescopic
perspective; addressing global health concerns often involves
examining resource-limited populations with large geographic,
socioeconomic diversities. Therefore, advancing global health
requires new epidemiological design, new data, and new methods for
sampling, data processing, and statistical analysis. This book
provides global health researchers with methods that will enable
access to and utilization of existing data. Featuring contributions
from both epidemiological and biostatistical scholars, this book is
a practical resource for researchers, practitioners, and students
in solving global health problems in research, education, training,
and consultation.
This monograph provides a concise point of research topics and
reference for modeling correlated response data with time-dependent
covariates, and longitudinal data for the analysis of
population-averaged models, highlighting methods by a variety of
pioneering scholars. While the models presented in the volume are
applied to health and health-related data, they can be used to
analyze any kind of data that contain covariates that change over
time. The included data are analyzed with the use of both R and
SAS, and the data and computing programs are provided to readers so
that they can replicate and implement covered methods. It is an
excellent resource for scholars of both computational and
methodological statistics and biostatistics, particularly in the
applied areas of health.
In the statistical domain, certain topics have received
considerable attention during the last decade or so, necessitated
by the growth and evolution of data and theoretical challenges.
This growth has invariably been accompanied by computational
advancement, which has presented end users as well as researchers
with the necessary opportunities to handle data and implement
modelling solutions for statistical purposes. Showcasing the
interplay among a variety of disciplines, this book offers
pioneering theoretical and applied solutions to practice-oriented
problems. As a carefully curated collection of prominent
international thought leaders, it fosters collaboration between
statisticians and biostatisticians and provides an array of thought
processes and tools to its readers. The book thereby creates an
understanding and appreciation of recent developments as well as an
implementation of these contributions within the broader framework
of both academia and industry. Computational and Methodological
Statistics and Biostatistics is composed of three main themes: *
Recent developments in theory and applications of statistical
distributions;* Recent developments in supervised and unsupervised
modelling;* Recent developments in biostatistics; and also features
programming code and accompanying algorithms to enable readers to
replicate and implement methodologies. Therefore, this monograph
provides a concise point of reference for a variety of current
trends and topics within the statistical domain. With
interdisciplinary appeal, it will be useful to researchers,
graduate students, and practitioners in statistics, biostatistics,
clinical methodology, geology, data science, and actuarial science,
amongst others.
This book provides an overview of the theories and applications on
subgroups in the biopharmaceutical industry. Drawing from a range
of expert perspectives in academia and industry, this collection
offers an overarching dialogue about recent advances in
biopharmaceutical applications, novel statistical and
methodological developments, and potential future directions. The
volume covers topics in subgroups in clinical trial design;
subgroup identification and personalized medicine; and general
issues in subgroup analyses, including regulatory ones. Included
chapters present current methods, theories, and case applications
in the diverse field of subgroup application and analysis. Offering
timely perspectives from a range of authoritative sources, the
volume is designed to have wide appeal to professionals in the
pharmaceutical industry and to graduate students and researchers in
academe and government.
This book examines statistical methods and models used in the
fields of global health and epidemiology. It includes methods such
as innovative probability sampling, data harmonization and
encryption, and advanced descriptive, analytical and monitory
methods. Program codes using R are included as well as real data
examples. Contemporary global health and epidemiology involves a
myriad of medical and health challenges, including inequality of
treatment, the HIV/AIDS epidemic and its subsequent control, the
flu, cancer, tobacco control, drug use, and environmental
pollution. In addition to its vast scales and telescopic
perspective; addressing global health concerns often involves
examining resource-limited populations with large geographic,
socioeconomic diversities. Therefore, advancing global health
requires new epidemiological design, new data, and new methods for
sampling, data processing, and statistical analysis. This book
provides global health researchers with methods that will enable
access to and utilization of existing data. Featuring contributions
from both epidemiological and biostatistical scholars, this book is
a practical resource for researchers, practitioners, and students
in solving global health problems in research, education, training,
and consultation.
This book brings together the voices of leading experts in the
frontiers of biostatistics, biomedicine, and the health sciences to
discuss the statistical procedures, useful methods, and novel
applications in biostatistics research. It also includes
discussions of potential future directions of biomedicine and new
statistical developments for health research, with the intent of
stimulating research and fostering the interactions of scholars
across health research related disciplines. Topics covered include:
Health data analysis and applications to EHR data Clinical trials,
FDR, and applications in health science Big network analytics and
its applications in GWAS Survival analysis and functional data
analysis Graphical modelling in genomic studies The book will be
valuable to data scientists and statisticians who are working in
biomedicine and health, other practitioners in the health sciences,
and graduate students and researchers in biostatistics and health.
This monograph provides a concise point of research topics and
reference for modeling correlated response data with time-dependent
covariates, and longitudinal data for the analysis of
population-averaged models, highlighting methods by a variety of
pioneering scholars. While the models presented in the volume are
applied to health and health-related data, they can be used to
analyze any kind of data that contain covariates that change over
time. The included data are analyzed with the use of both R and
SAS, and the data and computing programs are provided to readers so
that they can replicate and implement covered methods. It is an
excellent resource for scholars of both computational and
methodological statistics and biostatistics, particularly in the
applied areas of health.
This book provides an overview of the theories and applications on
subgroups in the biopharmaceutical industry. Drawing from a range
of expert perspectives in academia and industry, this collection
offers an overarching dialogue about recent advances in
biopharmaceutical applications, novel statistical and
methodological developments, and potential future directions. The
volume covers topics in subgroups in clinical trial design;
subgroup identification and personalized medicine; and general
issues in subgroup analyses, including regulatory ones. Included
chapters present current methods, theories, and case applications
in the diverse field of subgroup application and analysis. Offering
timely perspectives from a range of authoritative sources, the
volume is designed to have wide appeal to professionals in the
pharmaceutical industry and to graduate students and researchers in
academe and government.
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