Sample Size Calculations in Clinical Research
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Summary
Sample size calculation plays an important role in clinical research. It is not uncommon, however, to observe discrepancies among study objectives (or hypotheses), study design, statistical analysis (or test statistic), and sample size calculation. Focusing on sample size calculation for studies conducted during the various phases of clinical research and development, Sample Size Calculation in Clinical Research explores the causes of discrepancies and how to avoid them.
This volume provides formulas and procedures for determination of sample size required not only for testing equality, but also for testing non-inferiority/superiority, and equivalence (similarity) based on both untransformed (raw) data and log-transformed data under a parallel-group design or a crossover design with equal or unequal ratio of treatment allocations. It contains a comprehensive and unified presentation of statistical procedures for sample size calculation that are commonly employed at various phases of clinical development. Each chapter includes, whenever possible, real examples of clinical studies from therapeutic areas such as cardiovascular, central nervous system, anti-infective, oncology, and women's health to demonstrate the clinical and statistical concepts, interpretations, and their relationships and interactions.
The book highlights statistical procedures for sample size calculation and justification that are commonly employed in clinical research and development. It provides clear, illustrated explanations of how the derived formulas and/or statistical procedures can be used.
Table of Contents
INTRODUCTION
Regulatory Requirement
Basic Considerations
Procedures for Sample Size Calculation
Aims and Structure of the Book
CONSIDERATIONS PRIOR TO SAMPLE SIZE CALCULATION
Confounding and Interaction
One-Sided Test Versus Two-Sided Test
Crossover Design Versus Parallel Design
Subgroup/Interim Analyses
Data Transformation
Practical Issues
COMPARING MEANS
One-Sample Design
Two-Sample Parallel Design
Two-Sample Crossover Design
Multiple-Sample One-Way ANOVA
Multiple-Sample Williams Design
Practical Issues
LARGE SAMPLE TESTS FOR PROPORTIONS
One-Sample Design
Two-Sample Parallel Design
Two-Sample Crossover Design
One-Way Analysis of Variance
Williams Design
Relative Risk - Parallel Design
Relative Risk - Crossover Design
Practical Issues
EXACT TESTS FOR PROPORTIONS
Binomial Test
Fisher's Exact Test
Optimal Multiple-Stage Designs for Single Arm Trials
Flexible Designs for Multiple-Arm Trials
Remarks
TESTS FOR GOODNESS-OF-FIT AND CONTINGENCY TABLES
Tests for Goodness-of-Fit
Test for Independence -Single Stratum
Test for Independence -Multiple Strata
Test for Categorical Shift
Carry-Over Effect Test
Practical Issues
COMPARING TIME-TO-EVENT DATA
Basic Concepts
Exponential Model
Cox's Proportional Hazards Model
Weighted Log-Rank Test
Practical Issues
GROUP SEQUENTIAL METHODS
Pocock's Test
O'Brien and Fleming's Test
Wang and Tsiatis' Test
Inner Wedge Test
Binary Variables
Time-to-Event Data
Alpha Spending Function
Sample Size Re-Estimation
Conditional Power
Practical Issues
COMPARING VARIABILITIES
Comparing Intra-Subject Variabilities
Comparing Intra-Subject CVs
Comparing Inter-Subject Variabilities
Comparing Total Variabilities
Practical Issues
BIOEQUIVALENCE TESTING
Bioequivalence Criteria
Average Bioequivalence
Population Bioequivalence
Individual Bioequivalence
In Vitro Bioequivalence
NONPARAMETRICS
Violation of Assumptions
One-Sample Location Problem
Two-Sample Location Problem
Test for Independence
Practical Issues
SAMPLE SIZE CALCULATION IN OTHER AREAS
Dose Response Studies
ANOVA with Repeated Measures
Quality of Life
Bridging Studies
Vaccine Clinical Trials
Appendix: Tables of Quantiles
References
Index
Editorial Reviews
"This well composed book … contains sample size formulas and examples. … [A] good reference book for researchers in clinical trials."
- Journal of Statistical Computation & Simulation, Vol. 74, No. 5, May 2004
"The reference list contains details of an excellent collection of articles. The examples are clearly illustrated. This is a fascinating book, and applied statisticians, health and medical researchers will like it a lot. Statistical consultants will be fond of the book as a reference guide."
-Journal of Statistical Computation and Simulation, Vol. 75, No. 9, Sept 2005
". . . this book is an excellent reference for people involved in the designing of clinical research projects." – Michael Roughton, Medical Statistician, Royal Brompton Hospital, in Journal of Perioperative Practice, September 2008, Vol. 18, No. 9
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