Bayesian methods in pharmaceutical research / edited by Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger.

Format:

Book

Contributor:

Lesaffre, Emmanuel, editor.

Baio, Gianluca, editor.

Boulanger, Bruno, editor.

Series:

Chapman & Hall/CRC biostatistics series.

Chapman & Hall/CRC biostatistics series

Language:

English

Subjects (All):

Clinical trials--Statistical methods.

Clinical trials.

Physical Description:

1 online resource (xxx, 516 pages) : illustrations.

Edition:

1st ed.

Place of Publication:

Boca Raton, Florida ; London ; New York : CRC Press, [2020]

Summary:

Bayesian methods have emerged as the driving force for methodological development in drug development. This edited book provides broad coverage of Bayesian methods in pharmaceutical research. The book includes contributions from some of the leading researchers in the field, and has been edited to ensure consistency in level and style.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Dedication

Contents

Preface

Editors

Contributors

List of abbreviations

Part I: Introduction

1. Bayesian Background

1.1 Introduction

1.2 The frequentist approach to inference

1.3 Bayesian concepts

1.4 More than one parameter

1.5 Choosing the prior distribution

1.6 Determining the posterior distribution numerically

1.7 Hierarchical models and data augmentation

1.8 Model selection and model checking

1.9 Bayesian nonparametric methods

1.10 Bayesian software

1.11 Further reading

2. FDA Regulatory Acceptance of Bayesian Statistics

2.1 Introduction

2.2 Medical devices

2.3 Pharmaceutical products

2.4 Differences between devices and drugs

2.5 Some promising opportunities in pharmaceutical drugs

2.6 The future

2.7 Conclusion

3. Bayesian Tail Probabilities for Decision Making

3.1 Introduction

3.2 Posterior tail probabilities

3.3 Predictive tail probabilities

3.4 Discussion

Part II: Clinical development

4. Clinical Development in the Light of Bayesian Statistics

4.1 Introduction

4.2 Introduction to drug development

4.3 Quantitative decision making in drug development

4.4 Bayesian thinking

4.5 Applications of Bayesian methods in drug development

4.6 Conclusion

5. Prior Elicitation

5.1 Introduction

5.2 Methods for prior elicitation

5.3 Examples

5.4 Impact and outlook

6. Use of Historical Data

6.1 Introduction

6.2 Identifying historical or co-data

6.3 An example: Guillain-Barré syndrome in children

6.4 Methods

6.5 Application: Non-inferiority trials

6.6 Discussion

6.7 Code

7. Dose Ranging Studies and Dose Determination

7.1 Introduction

7.2 Dose-response studies

7.3 Dose escalation trials in oncology

7.4 Conclusions.

8. Bayesian Adaptive Designs in Drug Development

8.1 Introduction

8.2 Brief history of adaptive designs

8.3 What is an adaptive clinical trial?

8.4 Types of adaptation

8.5 Reasons we might consider adaptive designs

8.6 Example of an adaptive design

8.7 Adaptive enrichment designs

8.8 Some criticisms of adaptive designs

8.9 Summary

9. Bayesian Methods for Longitudinal Data with Missingness

9.1 Introduction

9.2 Common frequentist approaches

9.3 Bayesian approaches

9.4 Ignorable and nonignorable missingness

9.5 Posterior inference

9.6 Model selection

9.7 Model checking and assessment

9.8 Practical example: Growth hormone trial

9.9 Wrap-up and open problems

10. Survival Analysis and Censored Data

10.1 Introduction

10.2 Review of survival analysis

10.3 Software

10.4 Applications

10.5 Reporting

10.6 Other comments

11. Benefit of Bayesian Clustering of Longitudinal Data: Study of Cognitive Decline for Precision Medicine

11.1 Introduction

11.2 Motivating example

11.3 Nonparametric Bayesian models

11.4 Standard frequentist analysis: Latent class mixed models

11.5 Profile regression analysis

11.6 Conclusion

12. Bayesian Frameworks for Rare Disease Clinical Development Programs

12.1 Introduction

12.2 Natural history studies

12.3 Long-term safety evaluation with Real-World Data

12.4 Bayesian approaches in rare diseases

12.5 Case study

12.6 Conclusions and future directions

13. Bayesian Hierarchical Models for Data Extrapolation and Analysis in Pediatric Disease Clinical Trials

13.1 Introduction

13.2 Classical statistical approaches to data extrapolation

13.3 Current Bayesian approaches

13.4 Practical example

13.5 Outlook

Part III: Post-marketing

14. Bayesian Methods for Meta-Analysis

14.1 Introduction.

14.2 Pairwise meta-analysis

14.3 Network meta-analysis

14.4 Bias modeling in pairwise and network meta-analysis

14.5 Using meta-analysis to inform study design

14.6 Further reading

15. Economic Evaluation and Cost-Effectiveness of Health Care Interventions

15.1 Introduction

15.2 Economic evaluation: A Bayesian decision theoretic analysis

15.3 Trial-based economic evaluation

15.4 Model-based economic evaluation

15.5 Value of information

15.6 Conclusion / outlook

16. Bayesian Modeling for Economic Evaluation Using "Real-World Evidence

16.1 Introduction

16.2 Real World Evidence

16.3 Economic modeling and survival analysis

16.4 Case study: ICDs in cardiac arrhythmia

16.5 Conclusions and further developments

17. Bayesian Benefit-Risk Evaluation in Pharmaceutical Research

17.1 Introduction

17.2 Classical approaches to quantitative benefit-risk

17.3 Bayesian approaches to quantitative benefit-risk

17.4 Outlook for Bayesian benefit-risk

17.5 Discussion

Part IV: Product development and manufacturing

18. Product Development and Manufacturing

18.1 Introduction

18.2 What is the question in manufacturing?

18.3 Bayesian statistics for comparability and analytical similarity

18.4 Bayesian approach to comparability and biosimilarity

18.5 Conclusions

19. Process Development and Validation

19.1 Introduction

19.2 ICH Q8 design space

19.3 Assay robustness

19.4 Challenges for the Bayesian approach

20. Analytical Method and Assay

20.1 Introduction

20.2 Analytical quality by design

20.3 Assay development

20.4 Analytical validation and transfer

20.5 Routine

20.6 Conclusion

21. Bayesian Methods for the Design and Analysis of Stability Studies

21.1 Introduction

21.2 New perspectives on stability data analysis.

21.3 Stability designs, models and assumptions

21.4 Overview of frequentist methods in stability data

21.5 Bayesian methods of analysis of stability data

21.6 Conclusions

22. Content Uniformity Testing

22.1 Introduction

22.2 Classical procedures for testing content uniformity

22.3 Bayesian procedures for testing content uniformity and risk

22.4 Challenges for the Bayesian procedures

23. Bayesian Methods for In Vitro Dissolution Drug Testing and Similarity Comparisons

23.1 Introduction

23.2 Current statistical practices in IV dissolution and their limitations

23.3 The value of adopting Bayesian paradigms

23.4 Applying Bayesian approaches: Two examples

23.5 Conclusions

24. Bayesian Statistics for Manufacturing

24.1 Introduction

24.2 Manufacturing situation 1: Revalidation/transfer

24.3 Manufacturing situation 2: Evaluating process capability

24.4 Manufacturing situation 3: Bayesian modeling of complex testing schemes

24.5 Discussion

Part V: Additional topics

25. Bayesian Statistical Methodology in the Medical Device Industry

25.1 Introduction

25.2 Use of stochastic engineering models in the medical device design stage

25.3 Bayesian design and analysis of medical device trials

25.4 Challenges

26. Program and Portfolio Decision-Making

26.1 Introduction

26.2 Classical approaches

26.3 Current Bayesian approaches to program design

26.4 Program and portfolio-level Bayesian decision analysis

26.5 Research opportunities

Index.

Notes:

Description based on print version record.

ISBN:

1-351-71866-5

1-315-18021-9

9781315180212

OCLC:

1152525270