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Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials
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Book Description
Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials provides a practical introduction to unconditional approaches to planning randomised clinical trials, particularly aimed at drug development in the pharmaceutical industry. This book is aimed at providing guidance to practitioners in using average power, assurance and related concepts. This book brings together recent research and sets them in a consistent framework and provides a fresh insight into how such methods can be used.
Features:
- A focus on normal theory linking average power, expected power, predictive power, assurance, conditional Bayesian power and Bayesian power.
- Extensions of the concepts to binomial, and time-to-event outcomes and non-inferiority trials
- An investigation into the upper bound on average power, assurance and Bayesian power based on the prior probability of a positive treatment effect
- Application of assurance to a series of trials in a development program and an introduction of the assurance of an individual trial conditional on the positive outcome of an earlier trial in the program, or to the successful outcome of an interim analysis
- Prior distribution of power and sample size
- Extension of the basic approach to proof-of-concept trials with dual success criteria
- Investigation of the connection between conditional and predictive power at an interim analysis and power and assurance
- Introduction of the idea of surety in sample sizing of clinical trials based on the width of the confidence intervals for the treatment effect, and an unconditional version.
Table of Contents
List of Figures..........................................................................................................xi
List of Tables......................................................................................................... xiii
Preface......................................................................................................................xv
Acknowledgements..............................................................................................xix
Author.....................................................................................................................xxi
List of Acronyms................................................................................................ xxiii
1. Introduction......................................................................................................1
2. All Power Is Conditional Unless It’s Absolute..........................................9
3. Assurance........................................................................................................33
4. Average Power in Non-Normal Settings...................................................59
5. Bayesian Power..............................................................................................75
6. Prior Distributions of Power and Sample Size........................................87
7. Interim Predictions......................................................................................101
8. Case Studies in Simulation........................................................................ 113
9. Decision Criteria in Proof-of-Concept Trials..........................................127
10. Surety and Assurance in Estimation........................................................149
References.............................................................................................................161
Appendix 1 Evaluation of a Double Normal Integral...................................171
Appendix 2 Besag’s Candidate Formula.........................................................173
Index......................................................................................................................175
Author(s)
Biography
Andrew P. Grieve is a Statistical Research Fellow in the Centre of Excellence in Statistical Innovation at UCB Pharma. He is a former Chair of PSI (Statisticians in the Pharmaceutical Industry) and a past-President of the Royal Statistical Society. He has over 45 years of experience as a biostatistician working in the pharmaceutical industry and academia and has been active in most areas of pharmaceutical R&D in which statistical methods and statisticians are intimately involved, including drug discovery, pre-clinical toxicology, pharmaceutical development, pharmacokinetics and pharmacodynamics, phase I–IV of clinical development, manufacturing, health economics and clinical operations.