Missing Data in Clinical Studies provides a comprehensive
account of the problems arising when data from clinical and related
studies are incomplete, and presents the reader with approaches to
effectively address them. The text provides a critique of
conventional and simple methods before moving on to discuss more
advanced approaches. The authors focus on practical and modeling
concepts, providing an extensive set of case studies to illustrate
the problems described.
* Provides a practical guide to the analysis of clinical trials
and related studies with missing data.
* Examines the problems caused by missing data, enabling a
complete understanding of how to overcome them.
* Presents conventional, simple methods to tackle these problems,
before addressing more advanced approaches, including sensitivity
analysis, and the MAR missingness mechanism.
* Illustrated throughout with real-life case studies and worked
examples from clinical trials.
* Details the use and implementation of the necessary statistical
software, primarily SAS.
Missing Data in Clinical Studies has been developed
through a series of courses and lectures. Its practical approach
will appeal to applied statisticians and biomedical researchers, in
particular those in the biopharmaceutical industry, medical and
public health organisations. Graduate students of biostatistics
will also find much of benefit.
Autorentext
Geert Molenberghs and Michael Kenward are the authors of Missing Data in Clinical Studies, published by Wiley.
Klappentext
The detrimental effects of incomplete data sets on the results of clinical trials are both well known and all too commonly recurrent. It is essential that the correct statistical methodology be applied in order to effectively analyse the results of trials affected by missing data.
Missing Data in Clinical Trials provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described.
- Provides a practical guide to the analysis of clinical trials and related studies with missing data.
- Examines the problems caused by missing data, enabling a complete understanding of how to overcome them.
- Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism.
- Illustrated throughout with real-life case studies and worked examples from clinical trials.
- Details the use and implementation of the necessary statistical software, primarily SAS.
Missing Data in Clinical Trials has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.
Zusammenfassung
Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described.
- Provides a practical guide to the analysis of clinical trials and related studies with missing data.
- Examines the problems caused by missing data, enabling a complete understanding of how to overcome them.
- Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism.
- Illustrated throughout with real-life case studies and worked examples from clinical trials.
- Details the use and implementation of the necessary statistical software, primarily SAS.
Missing Data in Clinical Studies has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.
Inhalt
Preface.
Acknowledgements.
I Preliminaries.
1 Introduction.
1.1 From Imbalance to the Field of Missing Data Research.
1.2 Incomplete Data in Clinical Studies.
1.3 MAR, MNAR, and Sensitivity Analysis.
1.4 Outline of the Book.
2 Key Examples.
2.1 Introduction.
2.2 The Vorozole Study.
2.3 The Orthodontic Growth Data.
2.4 Mastitis in Dairy Cattle.
2.5 The Depression Trials.
2.6 The Fluvoxamine Trial.
2.7 The Toenail Data.
2.8 Age-Related Macular Degeneration Trial.
2.9 The Analgesic Trial.
2.10 The Slovenian Public Opinion Survey.
3 Terminology and Framework.
3.1 Modelling Incompleteness.
3.2 Terminology.
3.3 Missing Data Frameworks.
3.4 Missing Data Mechanisms.
3.5 Ignorability.
3.6 Pattern-Mixture Models.
II Classical Techniques and the Need for Modelling.
4 A Perspective on Simple Methods.
4.1 Introduction.
4.2 Simple Methods.
4.3 Problems with Complete Case Analysis and Last Observation Carried Forward.
4.4 Using the Available Cases: a Frequentist versus a Likelihood Perspective.
4.5 Intention to Treat.
4.6 Concluding Remarks.
5 Analysis of the Orthodontic Growth Data.
5.1 Introduction and Models.
5.2 The Original, Complete Data.
5.3 Direct Likelihood.
5.4 Comparison of Analyses.
5.5 Example SAS Code for Multivariate Linear Models.
5.6 Comparative Power under Different Covariance Structures.
5.7 Concluding Remarks.
6 Analysis of the Depression Trials.
6.1 View 1: Longitudinal Analysis.
6.2 Views 2a and 2b and All versus Two Treatment Arms.
III Missing at Random and Ignorability.
7 The Direct Likelihood Method.
7.1 Introduction.
7.2 Ignorable Analyses in Practice.
7.3 The Linear Mixed Model.
7.4 Analysis of the Toenail Data.
7.5 The Generalized Linear Mixed Model.
7.6 The Depression Trials.
7.7 The Analgesic Trial.
8 The Expectation-Maximization Algorithm.
8.1 Introduction.
8.2 The Algorithm.
8.3 Missing Information.
8.4 Rate of Convergence.
8.5 EM Acceleration.
8.6 Calculation of Precision Estimates.
8.7 A Simple Illustration.
8.8 Concluding Remarks.
9 Multiple Imputation.
9.1 Introduction.
9.2 The Basic Procedure.
9.3 Theoretical Justification.
9.4 Inference under Multiple Imputation.
9.5 Efficiency.
9.6 Making Proper Imputations.
9.7 Some Roles for Multiple Imputation.
9.8 Concluding Remarks.
10 Weighted Estimating Equations.
10.1 Introduction.
10.2 Inverse Probability Weighting.
10.3 Generalized Estimating Equations for Marginal Models.
10.4 Weighted Generalized Estimating Equations.
10.5 The Depression Trials.
10.6 The Analgesic Trial.
10.7 Double Robustness.…