Sample Size Calculations for Clustered and Longitudinal Outcomes in Clinical Research

Sample size calculation is an important component at the design stage of clinical trials, health outcomes, and behavioral research. This book deals with how to estimate sample size for correlated outcomes, such as clustered data and longitudinal data. It provides sample size formulas incorporating intracluster correlation and the variability in cluster sizes. It also describes how missing data and correlation structures affect the sample size estimates in longitudinal data using generalized estimating equation (GEE) and mixed model approaches.

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.

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.

Song 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.