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Preventing and treating missing data in longitudinal clinical trials : a practical guide /
Focuses on the prevention and treatment of missing data in longitudinal clinical trials, looking at key principles and explaining analytic methods.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge :
Cambridge University Press,
2013.
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| Colección: | Practical guides to biostatistics and epidemiology.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- List of Figures; List of Tables; Acknowledgments; Preface; Part I BACKGROUND AND SETTING; 1 Why Missing Data Matter; 2 Missing Data Mechanisms; 2.1 Introduction; 2.2 Missing Data Taxonomy; 3 Estimands; 3.1 Introduction; 3.2 Hypotheses; 3.3 Considerations; Part II PREVENTING MISSING DATA; 4 Trial Design Considerations; 4.1 Introduction; 4.2 Design Options to Reduce Missing Data; Run-in Periods and Enrichment Designs; Randomized Withdrawal Studies; Choice of Target Population; Titration and Flexible Dosing; Add-on Studies; Shorter Assessment Periods; Rescue Mediations; Follow-up Data.
- Definition of Ascertainable OutcomesSample Size; 4.3 Considerations; 5 Trial Conduct Considerations; 5.1 Introduction; 5.2 Trial Conduct Options to Reduce Missing Data; Actions for Design and Management Teams; Actions for Investigators and Site Personnel; 5.3 Considerations; Part III ANALYTIC CONSIDERATIONS; 6 Methods of Estimation; 6.1 Introduction; 6.2 Least Squares; 6.3 Maximum Likelihood; 6.4 Generalized Estimating Equations; 6.5 Considerations; 7 Models and Modeling Considerations; 7.1 Introduction; 7.2 Correlation between Repeated Measurements; 7.3 Time Trends; 7.4 Model Formulation.
- 7.5 Modeling Philosophies8 Methods of Dealing with Missing Data; 8.1 Introduction; 8.2 Complete Case Analysis; 8.3 Simple Forms of Imputation; 8.4 Multiple Imputation; 8.5 Inverse Probability Weighting; 8.6 Modeling Approaches; Ignorable Methods; Non-Ignorable Methods; 8.7 Considerations; Part IV ANALYSES AND THE ANALYTIC ROAD MAP; 9 Analyses of Incomplete Data; 9.1 Introduction; 9.2 Simple Methods for Incomplete Data; 9.3 Likelihood-Based Analyses of Incomplete Data; 9.4 Multiple Imputation-Based Methods; 9.5 Weighted Generalized Estimating Equations; 9.6 Doubly Robust Methods.
- 9.7 Considerations10 MNAR Analyses; 10.1 Introduction; 10.2 Selection Models; 10.3 Shared Parameter Models; 10.4 Pattern-Mixture Models; 10.5 Controlled Imputation Methods; 10.6 Considerations; 11 Choosing Primary Estimands and Analyses; 11.1 Introduction; 11.2 Estimands, Estimators, and Choice of Data; Estimand 1; Difference in Outcome Improvement at the Planned Endpoint for all Randomized Subjects; Estimand 2; Difference in Outcome Improvement in Tolerators; Estimand 3; Difference in Outcome Improvement if all Subjects that Tolerated or Adhered; Estimand 4.
- Difference in Areas under the Outcome Curve During Adherence to TreatmentEstimand 5; Difference in Outcome Improvement During Adherence to Treatment; Estimand 6; Difference in Outcome Improvement in all Randomized Patients at the Planned Endpoint of the Trial Attributable to the Initially Randomized Medication; 11.3 Considerations; 12 The Analytic Road Map; 12.1 Introduction; 12.2 The Analytic Road Map; 12.3 Testable Assumptions; Standard Diagnostics; Influence Diagnostics; 12.4 Assessing Sensitivity to Missing Data Assumptions; 12.5 Considerations; 13 Analyzing Incomplete Categorical Data.