Using time-to-event analysis methodology requires careful definition of the event, censored observation, provision of adequate follow-up, number of events, and independence or "noninformativeness" of the censoring mechanisms relative to the event. Design and Analysis of Clinical Trials with Time-to-Event Endpoints provides a thorough presentation of the design, monitoring, analysis, and interpretation of clinical trials in which time-to-event is of critical interest.
After reviewing time-to-event endpoint methodology, clinical trial issues, and the design and monitoring of clinical trials, the book focuses on inferential analysis methods, including parametric, semiparametric, categorical, and Bayesian methods; an alternative to the Cox model for small samples; and estimation and testing for change in hazard. It then presents descriptive and graphical methods useful in the analysis of time-to-event endpoints. The next several chapters explore a variety of clinical trials, from analgesic, antibiotic, and antiviral trials to cardiovascular and cancer prevention, prostate cancer, astrocytoma brain tumor, and chronic myelogonous leukemia trials. The book then covers areas of drug development, medical practice, and safety assessment. It concludes with the design and analysis of clinical trials of animals required by the FDA for new drug applications.
Drawing on the expert contributors’ experiences working in biomedical research and clinical drug development, this comprehensive resource covers an array of time-to-event methods and explores an assortment of real-world applications.
Overview of Time-to-Event Endpoint Methodology Karl E. Peace
Design (and Monitoring) of Clinical Trials with Time-to-Event Endpoints Michael W. Sill and Larry Rubinstein
Overview of Time-to-Event Parametric Methods Karl E. Peace and Kao-Tai Tsai
Overview of Semiparametric Inferential Methods for Time-to-Event Endpoints Jianwen Cai and Donglin Zeng
Overview of Inferential Methods for Categorical Time-to-Event Data Eric V. Slud
Overview of Bayesian Inferential Methods Including Time-to-Event Endpoints Laura H. Gunn
An Efficient Alternative to the Cox Model for Small Time-to-Event Trials Devan V. Mehrotra and Arthur J. Roth
Estimation and Testing for Change in Hazard for Time-to-Event Endpoints Rafia Bhore and Mohammad Huque
Overview of Descriptive and Graphical Methods for Time-to-Event Data Michael O’Connell and Bob Treder
Design and Analysis of Analgesic Trials Akiko Okamoto, Julia Wang, and Surya Mohanty
Design and Analysis of Analgesic Trials with Paired Time-to-Event Endpoints Zhu Wang and Hon Keung Tony Ng
Time-to-Event Endpoint Methods in Antibiotic Trials Karl E. Peace
Design and Analysis of Cardiovascular Prevention Trials Michelle McNabb and Andreas Sashegyi
Design and Analysis of Antiviral Trials Anthony C. Segreti and Lynn P. Dix
Cure Rate Models with Applications to Melanoma and Prostate Cancer Data Ming-Hui Chen and Sungduk Kim
Parametric Likelihoods for Multiple Nonfatal Competing Risks and Death, with Application to Cancer Data Peter F. Thall and Xuemei Wang
Design, Summarization, Analysis, and Interpretation of Cancer Prevention Trials Matthew C. Somerville, Jennifer B. Shannon, and Timothy H. Wilson
LASSO Method in Variable Selection for Right-Censored Time-to-Event Data with Application to Astrocytoma Brain Tumor and Chronic Myelogenous Leukemia Lili Yu and Dennis Pearl
Selecting Optimal Treatments Based on Predictive Factors Eric C. Polley and Mark J. van der Laan
Application of Time-to-Event Methods in the Assessment of Safety in Clinical Trials Kelly L. Moore and Mark J. van der Laan
Design and Analysis of Chronic Carcinogenicity Studies of Pharmaceuticals in Rodents Mohammad Atiar Rahman and Karl K. Lin
Design and Analysis of Time-to-Tumor Response in Animal Studies: A Bayesian Perspective Steve Thomson and Karl K. Lin
… One of the strengths of the book is the collection, discussion and illustration of the many diverse time-to-event problems that may occur in practice. … this publication provides a comprehensive overview of classical and emerging ideas in the analysis of time-to-event problems. Written by experts in their area, the book has a wealth of references in each topic should the reader wish to learn about or extend their understanding of individual concepts or analysis methods. It is a worthwhile book to have in the library for anyone working in designing, conducting, analysing or interpreting studies with time-to-event outcomes.
—Australian & New Zealand Journal of Statistics, 2011