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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 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.
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.
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 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.
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.
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