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Index
Cover
Series
Title Page
Copyright
Dedication
Preface
References
Acknowledgments
Notation
Table of SAS code fragments
Contributors
Chapter 1: What's the problem with missing data?
1.1 What do we mean by missing data?
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
Appendix 1.A: Formal definitions of MCAR, MAR and MNAR
References
Chapter 2: The prevention of missing data
2.1 Introduction
2.2 The impact of “too much” missing data
2.3 The role of the statistician in the prevention of missing data
2.4 Methods for increasing subject retention
2.5 Improving understanding of reasons for subject withdrawal
Acknowledgments
Appendix 2.A: Example protocol text for missing data prevention
References
Chapter 3: Regulatory guidance – a quick tour
3.1 International conference on harmonization guideline: Statistical principles for clinical trials: E9
3.2 The US and EU regulatory documents
3.3 Key points in the regulatory documents on missing data
3.4 Regulatory guidance on particular statistical approaches
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
3.7 Other technical points from the NRC report
3.8 Other US/EU/international guidance documents that refer to missing data
3.9 And in practice?
References
Chapter 4: A guide to planning for missing data
4.1 Introduction
4.2 Planning for missing data
4.3 Exploring and presenting missingness
4.4 Model checking
4.5 Interpreting model results when there is missing data
4.6 Sample size and missing data
Appendix 4.A: Sample protocol/SAP text for study in Parkinson's disease
Appendix 4.B: A formal definition of a sensitivity parameter
References
Chapter 5: Mixed models for repeated measures using categorical time effects (MMRM)
5.1 Introduction
5.2 Specifying the mixed model for repeated measures
5.3 Understanding the data
5.4 Applying the mixed model for repeated measures
5.5 Additional mixed model for repeated measures topics
5.6 Logistic regression mixed model for repeated measures using the generalized linear mixed model
References
Table of SAS Code Fragments
Chapter 6: Multiple imputation
6.1 Introduction
6.2 Imputation phase
6.3 Analysis phase: Analyzing multiple imputed datasets
6.4 Pooling phase: Combining results from multiple datasets
6.5 Required number of imputations
6.6 Some practical considerations
6.7 Pre-specifying details of analysis with multiple imputation
Appendix 6.A: Additional methods for multiple imputation
References
Table of SAS Code Fragments
Chapter 7: Analyses under missing-not-at-random assumptions
7.1 Introduction
7.2 Background to sensitivity analyses and pattern-mixture models
7.3 Two methods of implementing sensitivity analyses via pattern-mixture models
7.4 A “toolkit”: Implementing sensitivity analyses via SAS
7.5 Examples of realistic strategies and results for illustrative datasets of three indications
Appendix 7.A How one could implement the neighboring case missing value assumption using visit-by-visit multiple imputation
Appendix 7.B SAS code to model withdrawals from the experimental arm, using observed data from the control arm
Appendix 7.C SAS code to model early withdrawals from the experimental arm, using the last-observation-carried-forward-like values
Appendix 7.D SAS macro to impose delta adjustment on a responder variable in the mania dataset
Appendix 7.E SAS code to implement tipping point via exhaustive scenarios for withdrawals in the mania dataset
Appendix 7.F SAS code to perform sensitivity analyses for the Parkinson's disease dataset
Appendix 7.G SAS code to perform sensitivity analyses for the insomnia dataset
Appendix 7.H SAS code to perform sensitivity analyses for the mania dataset
Appendix 7.I Selection models
Appendix 7.J Shared parameter models
References
Table of SAS Code Fragments
Chapter 8: Doubly robust estimation
8.1 Introduction
8.2 Inverse probability weighted estimation
8.3 Doubly robust estimation
8.4 Vansteelandt et al. method for doubly robust estimation
8.5 Implementing the Vansteelandt et al. method via SAS
Appendix 8.A How to implement Vansteelandt et al. method for mania dataset (binary response)
Appendix 8.B SAS code to calculate estimates from the bootstrapped datasets
Appendix 8.C How to implement Vansteelandt et al. method for insomnia dataset
References
Table of SAS Code Fragments
Bibliography
Index
Statistics in Practice
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