Clinical Trials With Missing Data - A Guide for Practitioners

Informationen zum Autor MICHAEL O'KELLY , Senior Strategic Biostatistics Director, Quintiles Ireland Ltd, Ireland. BOHDANA RATITCH , Senior Biostatistician, Quintiles, Montreal, Canada. Klappentext This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable.The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively.The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included - the reader is given a toolbox for implementing analyses under a variety of assumptions. Zusammenfassung A practical guide for handling and planning for missing data in clinical trials, Clinical Trials with Missing Data provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents statisticians, biostatisticians, and researchers with approaches to effectively address them. Inhaltsverzeichnis Preface xv References xvii Acknowledgments xix Notation xxi Table of SAS code fragments xxv Contributors xxix 1 What's the problem with missing data? 1 Michael O'Kelly and Bohdana Ratitch 1.1 What do we mean by missing data? 2 1.1.1 Monotone and non-monotone missing data 3 1.1.2 Modeling missingness, modeling the missing value and ignorability 4 1.1.3 Types of missingness (MCAR, MAR and MNAR) 4 1.1.4 Missing data and study objectives 5 1.2 An illustration 6 1.3 Why can't I use only the available primary endpoint data? 7 1.4 What's the problem with using last observation carried forward? 9 1.5 Can we just assume that data are missing at random? 11 1.6 What can be done if data may be missing not at random? 14 1.7 Stress-testing study results for robustness to missing data 15 1.8 How the pattern of dropouts can bias the outcome 15 1.9 How do we formulate a strategy for missing data? 16 1.10 Description of example datasets 18 1.10.1 Example dataset in Parkinson's disease treatment 18 1.10.2 Example dataset in insomnia treatment 23 1.10.3 Example dataset in mania treatment 28 Appendix 1.A: Formal definitions of MCAR, MAR and MNAR 33 References 34 2 The prevention of missing data 36 Sara Hughes 2.1 Introduction 36 2.2 The impact of "too much" missing data 37 2.2.1 Example from human immunodeficiency virus 38 2.2.2 Example from acute coronary syndrome 38 2.2.3 Example from studies in pain 39 2.3 The role of the statistician in the prevention of missing data 39 2.3.1 Illustrative example from HIV 41 2.4 Methods for increasing subject retention 48 2.5 Improving understanding of reasons for subject withdrawal 49 Acknowledgments 49 Appendix 2.A: Example protocol text for missing data prevention 49 References 50 3 Regulatory guidance - a quick tour 53 Michael O'Kelly 3.1 International conference on harmonization guideline: Statistical principles for clinical trials: E9 54 3.2 The US and EU...

Sommario

Preface
 
References
 
Acknowledgements
 
Notation
 
1. What's the problem with missing data?
 
1.1 What do we mean by missing data?
 
1.1.1 Monotone and non-monotone missing data
 
1.1.2 Modeling missingness, modeling the missing value and ignorability
 
1.1.3 Types of missingness (MCAR, MAR, and MNAR)
 
1.1.4 Missing data and study objectives
 
1.2 An illustration
 
1.3 Why can't I use only the available primary endpoint data?
 
1.4 What's the problem with using last observation carried forward?
 
1.5 Can we just assume that data are missing at random?
 
1.6 What can be done if data may be missing not at random?
 
1.7 Stress-testing study results for robustness to missing data
 
1.8 How the pattern of dropouts can bias the outcome
 
1.9 How do we formulate a strategy for missing data?
 
1.10 Description of Example Datasets
 
1.10.1 Example dataset in Parkinson's disease treatment
 
1.10.2 Example dataset in insomnia treatment
 
1.10.3 Example dataset in mania treatment
 
1.A Appendix: Formal definitions of MCAR, MAR, and MNAR
 
References
 
2 The prevention of missing data
 
2.1 Introduction
 
2.2 The impact of 'too much' missing data
 
2.2.1 Example from human immunodeficiency virus
 
2.2.2 Example from acute coronary syndrome
 
2.2.3 Example from studies in pain
 
2.3 The role of the statistician in the prevention of missing data
 
2.3.1 Illustrative example from HIV
 
Step 1: Quantifying the amount of missing data in previous trials, and its resultant impact
 
Step 2: Identifying subgroups of subjects who require an increased level of trial retention support
 
Step 3: Translating statistical analysis of previous trial data into information to inform future subject care
 
Step 4: Education of the clinical trial team and participation in the creation of missing data prevention plans
 
2.4 Methods for increasing subject retention
 
2.5 Improving understanding of reasons for subject withdrawal
 
2.6 Acknowledgements
 
2.7 Appendix 2.A: example protocol text for missing data prevention
 
References
 
3 Regulatory guidance - a quick tour
 
3.1 International Conference on Harmonization guideline: Statistical principles for clinical trials: E9
 
3.2 The U.S. and EU regulatory documents
 
3.3 Key points in the regulatory documents on missing data
 
3.4 Regulatory guidance on particular statistical approaches
 
3.4.1 Available cases
 
3.4.2 Single imputation methods
 
3.4.3 Methods that generally assume MAR
 
3.4.4 Methods that are used assuming MNAR
 
3.5 Guidance about how to plan for missing data in a study
 
3.6 Differences in emphasis between the NRC report and EU guidance documents
 
3.6.1 The term "conservative"
 
3.6.2 Last observation carried forward
 
3.6.3 Post hoc analyses
 
3.6.4 Non-monotone or intermittently missing data
 
3.6.5 Assumptions should be readily interpretable
 
3.6.6 Study report
 
3.6.7 Training
 
3.7 Other technical points from the NRC report
 
3.7.1 Time-to-event analyses
 
3.7.2 Tipping point sensitivity analyses
 
3.8 Other U.S./EU/international guidance documents that refer to missing data
 
3.8.1 Committee for Medicinal Products for Human Use guideline on anticancer products, recommendations on survival analysis
 
3.8.2 U.S. guidance on considerations when research supported by Office of Human Research Prot

Relazione

"This is an excellent addition to the field, dealing with problems arising from missing data or unobserved data in clinical trials. It successfully bridges the gap between clinicians and statisticians using relatively common language to build common ground." (Doody's , 9 January 2015)

Dettagli sul prodotto

• Autori: &apos, Michael Ratitch kelly, O&apos, O'Kelly, M O'Kelly, Michae O'Kelly, Michael O'Kelly, M O''kelly, Michael Ratitch O''kelly, Bohdana Ratitch
• Editore: Wiley, John and Sons Ltd
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 28.03.2014
• EAN: 9781118460702
• ISBN: 978-1-118-46070-2
• Pagine: 480
• Serie: Statistics in Practice; Statistics in Practice
• Categoria: Scienze naturali, medicina, informatica, tecnica > Matematica