Randomized Phase II Cancer Clinical Trials
By Sin-Ho Jung
Chapman and Hall/CRC – 2013 – 244 pages
In cancer research, a traditional phase II trial is designed as a single-arm trial that compares the experimental therapy to a historical control. This simple trial design has led to several adverse issues, including increased false positivity of phase II trial results and negative phase III trials. To rectify these problems, oncologists and biostatisticians have begun to use a randomized phase II trial that compares an experimental therapy with a prospective control therapy.
Randomized Phase II Cancer Clinical Trials explains how to properly select and accurately use diverse statistical methods for designing and analyzing phase II trials. The author first reviews the statistical methods for single-arm phase II trials since some methodologies for randomized phase II trials stem from single-arm phase II trials and many phase II cancer clinical trials still use single-arm designs. The book then presents methods for randomized phase II trials and describes statistical methods for both single-arm and randomized phase II trials. Although the text focuses on phase II cancer clinical trials, the statistical methods covered can also be used (with minor modifications) in phase II trials for other diseases and in phase III cancer clinical trials.
Suitable for cancer clinicians and biostatisticians, this book shows how randomized phase II trials with a prospective control resolve the shortcomings of traditional single-arm phase II trials. It provides readers with numerous statistical design and analysis methods for randomized phase II trials in oncology.
"Randomized Phase II Cancer Clinical Trials will be an invaluable source of information and reference for anyone interested in phase II cancer clinical trials, be it a graduate student, a biostatistics professor, or a cancer clinician in need of flexible designs and statistical analyses. … informative and interesting to read. The first of its kind, this book introduces the recent development of the promising randomized phase II trials. … This book has a very coherent structure and a legible style. … The author did an excellent job providing both contextual and technical details in a form that is both engaging and very readable. … a practical guidance book for cancer clinicians, as well as an excellent reference book for a more broad course, say, for example, clinical trials."
—Journal of the American Statistical Association, December 2014
"… this book is very timely and it can help biostatisticians and oncologists design more elaborate cancer clinical trials. This book is well written and nicely organized to illustrate statistical concepts and methods in both single-arm and randomized phase II cancer clinical trials. … This book is certainly one of the best textbooks for a graduate-level clinical trial course in the biostatistics department. Also, oncologists with weak statistical background can easily understand the statistical concepts of the phase II cancer clinical trials since the author tries to explain the key concepts with many tables and figures instead of relying on equations."
—Biometrics, September 2014
"… the book is unique in that it focuses solely on phase II cancer clinical trials with its emphasis on randomised trials. It goes far beyond what is covered on phase II clinical trials in cancer in books, for example, in Buyse et al. (1984) and more recently in Crowley & Hoering (2012) or Green et al. (2012). As such, it will definitely serve well as a reference for those involved in phase II cancer clinical trials, including clinical biostatisticians as well as clinical oncologists. … a useful reference in a graduate course in statistical methods for clinical trials."
—International Statistical Review (2014), 82
"… an excellent book covering various practical phase II designs with their associated statistical methods for cancer clinical trials. It is an asset to statistical professionals who work at industry or academia and would like to learn more about or improve their understanding of phase II trial design and methods for cancer clinical trials. The book can also be used as a text book for graduate student with statistical major."
—Steven Sun, Janssen Research Development, New Jersey, USA
Single-Arm Phase II Trial Designs
Two-Stage Designs with Both Upper and Lower Stopping Values
Inference on the Binomial Probability in Single-Arm Multistage Clinical Trials
When Realized Sample Size Is Different from That Specified in Design
Single-Arm Phase II Clinical Trials with Time-to-Event Endpoints
A Test Based on Median Survival Time
Maximum Likelihood Method for Exponential Distribution
One-Sample Log-Rank Test
Two-Stage Trials Using One-Sample Log-Rank Test
Binomial Testing on t-Year Survival Probability
Single-Arm Phase II Trials with Heterogeneous Patient Populations: Binary and Survival Outcomes
Binary Outcome Case
Survival Outcome Case: Stratified One-Sample Log-Rank Test
Randomized Phase II Trials for Selection: No Prospective Control Arms
With a Historical Control
When No Historical Control Exists
Extension to More Than Two Arms
Randomized Phase II Cancer Clinical Trials with a Prospective Control on Binary Endpoints (I): Two-Sample Binomial Test
Two-Sample Binomial Test
Two-Stage Designs with Both Upper and Lower Stopping Values
Randomized Phase II Cancer Clinical Trials with a Prospective Control on Binary Endpoints (II): Fisher’s Exact Test
Randomized Phase II Trials with Heterogeneous Patient Populations: Stratified Fisher’s Exact Test
Single-Stage Stratified Fisher’s Exact Test
Two-Stage Designs with an Interim Futility Test
Randomized Phase II Clinical Trials Based on Survival Endpoints: Two-Sample Log-Rank Test
Two-Sample Log-Rank Test
Two-Stage Log-Rank Test
Stratified Two-Sample Log-Rank Test for Single-Stage Designs
Some Flexible Phase II Clinical Trial Designs
Comparing Survival Distributions under General Hypothesis Testing
Randomized Phase II Trials for Comparing Maintenance Therapies
References appear at the end of each chapter.
Sin-Ho Jung is a professor of biostatistics and bioinformatics at Duke University School of Medicine. He earned a PhD from the University of Wisconsin-Madison. His research interests include clinical trial design, survival analysis, longitudinal data analysis, clustered data analysis, ROC curve analysis, and microarray studies.