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.

MICHAEL O'KELLY, Senior Strategic Biostatistics Director, Quintiles Ireland Ltd, Ireland.

BOHDANA RATITCH, Senior Biostatistician, Quintiles, Montreal, Canada.

Klappentext

Clinical Trials with Missing Data A Guide for Practitioners

How to plan and execute a successful approach to missing data in most clinical trials.

Missing data occur in nearly all clinical trials. In order to maintain the credibility of results, it is vital that measures to minimize the amount of missing data are carried out and that appropriate analysis techniques are identified and implemented correctly. 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.

This book:

• Presents clear and concise guidelines to enable good planning for missing data
• Is illustrated throughout with realistic case studies and worked examples
• Demonstrates how missing data can be treated in a way that is transparent and easy to understand for clinicians, regulators, and patients
• Explores new developments in the choice and implementation of primary and sensitivity analyses for missing data
• Includes many SAS code examples – a toolbox for implementing analyses under a variety of assumptions

Clinical Trials with Missing Data provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organizations. Academics and students needing an introduction to handling missing data will also find this book invaluable.

Zusammenfassung

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.

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 regulatory documents 55

3.3 Key points in the regulatory documents on missing data 55

3.4 Regulatory guidance on particular statistical approaches 57

3.4.1 Available cases 57

3.4.2 Single imputation methods 57

3.4.3 Methods that generally assume MAR 59

3.4.4 Methods that are used assuming MNAR 60

3.5 Guidance about how to plan for missing data in a study 62

3.6 Differences in emphasis between the NRC report and EU guidance documents 63

3.6.1 The term conservative 63

3.6.2 Last observation carried forward 63

3.6.3 Post hoc analyses 63

3.6.4 Non-monotone or intermittently missing data 63

3.6.5 Assumptions should be readily interpretable 65

3.6.6 Study report 65

3.6.7 Training 65

3.7 Other technical points from the NRC report 66

3.7.1 Time-to-event analyses 66

3.7.2 Tipping point sensitivity analyses 66

3.8 Other US/EU/international guidance documents that refer to missing data 66

3.8.1 Committee for medicinal products for human use guideline on anti-cancer products, recommendations on survival analysis 66

3.8.2 US guidance on considerations when research supported by office of human research protections is discontinued 67

3.8.3 FDA guidance on data retention 67

3.9 And in practice? 67

References 69

4 A guide to planning for missing data 71
Michael O'Kelly and Bohdana Ratitch

4.1 Introduction 72

4.1…