Fr. 190.00

Hybrid Frequentist;bayesian Power and Bayesian Power in Planning - Clinical Trial

English · Hardback

Shipping usually within 1 to 3 weeks (not available at short notice)

Description

Read more










This book 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.

List 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


About the author










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.


Summary

This book 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.

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.