Sample Size Calculations in Clinical Research

Sample Size Calculations for Clustered and Longitudinal Outcomes in Clinical Research
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• Discusses sample size determination for independent outcomes as well as variants of clustered trials, including one- and two-sample trials for continuous and binary outcomes, stratified cluster design, and nonparametric approaches
• Presents sample size methods based on summary statistics of longitudinal outcomes
• Explains how to determine sample size using GEE approaches for various types of correlated outcomes, including continuous, binary, and count
• Describes mixed-effects model approaches for randomized clinical trials with two level data structures
• Extends the mixed-effects model sample size approaches to scenarios where three level data structures are involved in randomized trials
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SummaryAccurate 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.
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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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Table of Contents

1 x7 Y* F/ s" o: O  |* J: s$ GSample Size Determination for Independent Outcomes
$ ]5 q1 i$ T8 Z5 u& x5 \' M' gIntroduction
Precision Analysis
Power Analysis
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Sample Size Determination for Clustered Outcomes
$ k% i" F% t: \  U5 q9 }Introduction
One-Sample Clustered Continuous Outcomes
One-Sample Clustered Binary Outcomes
Two-Sample Clustered Continuous Outcomes
Two-Sample Clustered Binary Outcomes
4 @- \. h! ~8 t3 A  o- x3 d7 ~  mStratified Cluster Randomization for Binary Outcomes
0 F$ y, v6 ~" j" T/ U8 Y  w* ZNonparametric Approach for One-Sample Clustered Binary Outcomes
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' s& a  k& P- i& nSample Size Determination for Repeated Measurement Outcomes Using Summary Statistics
9 N" D3 j/ }3 Y) wIntroduction
Information Needed for Sample Size Estimation
Summary Statistics


' Z* h# x  ^5 w; ^/ F3 _# }Sample Size Determination for Correlated Outcome Measurements Using GEE
0 M- E& c+ z+ [7 [$ ?0 i5 J) SMotivation
# H, X  z# M( ^" QReview of GEE
2 I! j4 b6 G7 L9 YCompare the Slope for a Continuous Outcome
Test the TAD for a Continuous Outcome
! ~- U! ?/ \6 S# i: sCompare the Slope for a Binary Outcome
4 y# u' M+ {4 u) V+ ^  QTest the TAD for a Binary Outcome
9 v& e1 ~1 c- N  tCompare the Slope for a Count Outcome
6 }  S+ c4 Y9 U3 `- YTest the TAD for a Count Outcome
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Sample Size Determination for Correlated Outcomes from Two-Level Randomized Clinical Trials
Introduction
# ^4 l: J( g( g2 m  h8 x# HStatistical Models for Continuous Outcomes
Testing Main Effects
5 d8 u" t* o3 UTwo Level Longitudinal Designs: Testing Slope Differences
& h8 r; e# w- uCross-Sectional Factorial Designs: Interactions between Treatments
Longitudinal Factorial Designs: Treatment Effects on Slopes
3 z  `1 U0 a6 A2 d# R, W2 ySample Sizes for Binary Outcomes
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: e; s/ h6 W8 s- ?5 RSample Size Determination for Correlated Outcomes from Three Level Randomized Clinical Trials
* n9 e: O) S% Y' sIntroduction
. C3 A1 |1 k1 \8 Q+ O5 l) U3 c1 ]Statistical Model for Continuous Outcomes
6 t# P- A' i3 zTesting Main Effects
; {2 S& \+ W9 ]/ E4 tTesting Slope Differences
$ r7 E0 d1 L; ~. w/ \& L6 QCross-Sectional Factorial Designs: Interactions between Treatments
8 y- v8 o! e# `5 l- k" E9 y+ [& Y7 hLongitudinal Factorial Designs: Treatment Effects on Slopes
Sample Sizes for Binary Outcomes


Further Readings appear at the end of each chapter.
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Author(s) Bio
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Chul Ahn, PhD, is a professor in the Department of Clinical Sciences and the cancer center associate director for biostatistics and bioinformatics in the Simmons Comprehensive Cancer Center at the University of Texas Southwestern Medical Center. He is also director of Biostatistics and Research Design for the NIH-sponsored Clinical and Translational Science Award (CTSA). He has published more than 370 peer-reviewed papers addressing the design and analysis of clinical trials and epidemiological studies as well as the evaluation of repeated measurements and correlated data.
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Moonseong Heo, PhD, is a professor in the Department of Epidemiology & Population Health at the Albert Einstein College of Medicine. His research includes sample size determinations for clinical trials, meta-analysis, longitudinal data analysis applying mixed-effects models, handling attrition problems in clinical trials data, and epidemiology in the fields of obesity and psychiatry.
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0 w$ K6 M' y. z3 ^+ F) MSong Zhang, PhD, is an associate professor in the Department of Clinical Sciences at the University of Texas Southwestern Medical Center. He has extensive experience in the design of clinical trials with correlated outcomes, addressing challenges that involve different correlation structures, missing data patterns, financial constraints, and historical controls. He is also interested in Bayesian statistical methods and their application in longitudinal and survival data analysis, high-throughput data analysis, disease mapping, adaptive design for clinical trials, and missing data imputation.
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