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E-BOOK
Title Preventing and treating missing data in longitudinal clinical trials : a practical guide / Craig H. Mallinckrodt.
Imprint Cambridge : Cambridge University Press, 2013.

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 Internet  Electronic Book    AVAILABLE
Description 1 online resource (186 pages)
Series Practical guides to biostatistics and epidemiology
Practical guides to biostatistics and epidemiology.
Bibliog. Includes bibliographical references and index.
Note Available only to authorized UTEP users.
Print version record.
Subject Clinical trials -- Longitudinal studies.
Medical sciences -- Statistical methods.
Regression analysis -- Data processing.
Genre Longitudinal studies.
Contents 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.
Summary Focuses on the prevention and treatment of missing data in longitudinal clinical trials, looking at key principles and explaining analytic methods.
Other Title Print version: Mallinckrodt, Craig. Preventing and Treating Missing Data in Longitudinal Clinical Trials : A Practical Guide. Cambridge : Cambridge University Press, ©2013 9781107031388