Sample Size Calculations For Clustered And Longitudinal Outcomes In Clinical Research (chapman & Hall/crc Biostatistics Series)
by Song Zhang /
2014 / English / PDF
1.6 MB Download
Accurate sample size calculation ensures that clinical studies
have adequate power to detect clinically meaningful effects. This
results in the efficient use of resources and avoids exposing a
disproportionate number of patients to experimental treatments
caused by an overpowered study.
Accurate sample size calculation ensures that clinical studies
have adequate power to detect clinically meaningful effects. This
results in the efficient use of resources and avoids exposing a
disproportionate number of patients to experimental treatments
caused by an overpowered study.Sample Size Calculations for Clustered and Longitudinal
Outcomes in Clinical Research
Sample Size Calculations for Clustered and Longitudinal
Outcomes in Clinical Research explains how to determine
sample size for studies with correlated outcomes, which are
widely implemented in medical, epidemiological, and behavioral
studies.
explains how to determine
sample size for studies with correlated outcomes, which are
widely implemented in medical, epidemiological, and behavioral
studies.
The book focuses on issues specific to the two types of
correlated outcomes: longitudinal and clustered. For clustered
studies, the authors provide sample size formulas that
accommodate variable cluster sizes and within-cluster
correlation. For longitudinal studies, they present sample size
formulas to account for within-subject correlation among repeated
measurements and various missing data patterns. For multiple
levels of clustering, the level at which to perform randomization
actually becomes a design parameter. The authors show how this
can greatly impact trial administration, analysis, and sample
size requirement.
The book focuses on issues specific to the two types of
correlated outcomes: longitudinal and clustered. For clustered
studies, the authors provide sample size formulas that
accommodate variable cluster sizes and within-cluster
correlation. For longitudinal studies, they present sample size
formulas to account for within-subject correlation among repeated
measurements and various missing data patterns. For multiple
levels of clustering, the level at which to perform randomization
actually becomes a design parameter. The authors show how this
can greatly impact trial administration, analysis, and sample
size requirement.
Addressing the overarching theme of sample size determination for
correlated outcomes, this book provides a useful resource for
biostatisticians, clinical investigators, epidemiologists, and
social scientists whose research involves trials with correlated
outcomes. Each chapter is self-contained so readers can explore
topics relevant to their research projects without having to
refer to other chapters.
Addressing the overarching theme of sample size determination for
correlated outcomes, this book provides a useful resource for
biostatisticians, clinical investigators, epidemiologists, and
social scientists whose research involves trials with correlated
outcomes. Each chapter is self-contained so readers can explore
topics relevant to their research projects without having to
refer to other chapters.