|
Showing 1 - 5 of
5 matches in All Departments
The concepts of estimands, analyses (estimators), and sensitivity
are interrelated. Therefore, great need exists for an integrated
approach to these topics. This book acts as a practical guide to
developing and implementing statistical analysis plans by
explaining fundamental concepts using accessible language,
providing technical details, real-world examples, and SAS and R
code to implement analyses. The updated ICH guideline raises new
analytic and cross-functional challenges for statisticians. Gaps
between different communities have come to surface, such as between
causal inference and clinical trialists, as well as among
clinicians, statisticians, and regulators when it comes to
communicating decision-making objectives, assumptions, and
interpretations of evidence. This book lays out a path toward
bridging some of these gaps. It offers A common language and
unifying framework along with the technical details and practical
guidance to help statisticians meet the challenges A thorough
treatment of intercurrent events (ICEs), i.e., postrandomization
events that confound interpretation of outcomes and five strategies
for ICEs in ICH E9 (R1) Details on how estimands, integrated into a
principled study development process, lay a foundation for coherent
specification of trial design, conduct, and analysis needed to
overcome the issues caused by ICEs: A perspective on the role of
the intention-to-treat principle Examples and case studies from
various areas Example code in SAS and R A connection with causal
inference Implications and methods for analysis of longitudinal
trials with missing data Together, the authors have offered the
readers their ample expertise in clinical trial design and
analysis, from an industrial and academic perspective.
The concepts of estimands, analyses (estimators), and sensitivity
are interrelated. Therefore, great need exists for an integrated
approach to these topics. This book acts as a practical guide to
developing and implementing statistical analysis plans by
explaining fundamental concepts using accessible language,
providing technical details, real-world examples, and SAS and R
code to implement analyses. The updated ICH guideline raises new
analytic and cross-functional challenges for statisticians. Gaps
between different communities have come to surface, such as between
causal inference and clinical trialists, as well as among
clinicians, statisticians, and regulators when it comes to
communicating decision-making objectives, assumptions, and
interpretations of evidence. This book lays out a path toward
bridging some of these gaps. It offers ? A common language and
unifying framework along with the technical details and practical
guidance to help statisticians meet the challenges ? A thorough
treatment of intercurrent events (ICEs), i.e., postrandomization
events that confound interpretation of outcomes and five strategies
for ICEs in ICH E9 (R1) ? Details on how estimands, integrated into
a principled study development process, lay a foundation for
coherent specification of trial design, conduct, and analysis
needed to overcome the issues caused by ICEs: ? A perspective on
the role of the intention-to-treat principle ? Examples and case
studies from various areas ? Example code in SAS and R ? A
connection with causal inference ? Implications and methods for
analysis of longitudinal trials with missing data Together, the
authors have offered the readers their ample expertise in clinical
trial design and analysis, from an industrial and academic
perspective.
Analyzing Longitudinal Clinical Trial Data: A Practical Guide
provides practical and easy to implement approaches for bringing
the latest theory on analysis of longitudinal clinical trial data
into routine practice.The book, with its example-oriented approach
that includes numerous SAS and R code fragments, is an essential
resource for statisticians and graduate students specializing in
medical research. The authors provide clear descriptions of the
relevant statistical theory and illustrate practical considerations
for modeling longitudinal data. Topics covered include choice of
endpoint and statistical test; modeling means and the correlations
between repeated measurements; accounting for covariates; modeling
categorical data; model verification; methods for incomplete
(missing) data that includes the latest developments in sensitivity
analyses, along with approaches for and issues in choosing
estimands; and means for preventing missing data. Each chapter
stands alone in its coverage of a topic. The concluding chapters
provide detailed advice on how to integrate these independent
topics into an over-arching study development process and
statistical analysis plan.
Analyzing Longitudinal Clinical Trial Data: A Practical Guide
provides practical and easy to implement approaches for bringing
the latest theory on analysis of longitudinal clinical trial data
into routine practice.The book, with its example-oriented approach
that includes numerous SAS and R code fragments, is an essential
resource for statisticians and graduate students specializing in
medical research. The authors provide clear descriptions of the
relevant statistical theory and illustrate practical considerations
for modeling longitudinal data. Topics covered include choice of
endpoint and statistical test; modeling means and the correlations
between repeated measurements; accounting for covariates; modeling
categorical data; model verification; methods for incomplete
(missing) data that includes the latest developments in sensitivity
analyses, along with approaches for and issues in choosing
estimands; and means for preventing missing data. Each chapter
stands alone in its coverage of a topic. The concluding chapters
provide detailed advice on how to integrate these independent
topics into an over-arching study development process and
statistical analysis plan.
|
You may like...
Atmosfire
Jan Braai
Hardcover
R590
R425
Discovery Miles 4 250
Loot
Nadine Gordimer
Paperback
(2)
R383
R318
Discovery Miles 3 180
|