Estimands, Estimators and Sensitivity Analysis in Clinical Trials
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Estimands, Estimators and Sensitivity Analysis in Clinical Trials
Ratitch, Bohdana; Mallinckrodt, Craig; Lipkovich, Ilya; Molenberghs, Geert
Taylor & Francis Ltd
02/2020
318
Dura
Inglês
9781138592506
15 a 20 dias
793
Descrição não disponível.
Section I Setting the Stage 1. Introduction 2. Why Are Estimands Important? Section II Estimands 3. Estimands and How to Define Them 4. Strategies for Dealing with Intercurrent Events 5. Examples from Actual Clinical Trials in Choosing and Specifying Estimands 6. Causal Inference and Estimands 7. Putting the Principles into Practice Section III Estimators and Sensitivity 8. Overview of Estimators 9. Modeling Considerations 10. Overview of Analyses for Composite Intercurrent Event Strategies 11. Overview of Analyses for Hypothetical Intercurrent Event Strategies 12. Overview of Analyses for Principal Stratification Intercurrent Event Strategies 13. Overview of Analyses for While-on-Treatment Intercurrent Event Strategies 14. Overview of Analyses for Treatment Policy Intercurrent Event Strategies 15. Missing Data 16. Sensitivity Analyses Section IV Technical Details on Selected Analyses 17. Example Data 18. Direct Maximum Likelihood 19. Multiple Imputation 20. Inverse Probability Weighted Generalized Estimated Equations 21. Doubly Robust Methods 22. Reference-Based Imputation 23. Delta Adjustment 24. Overview of Principal Stratification Methods Section V Case Studies: Detailed Analytic Examples 25. Analytic Case Study of Depression Clinical Trials 26. Analytic Case Study Based on the ACTG 175 HIV Trial
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
LOCF Approach;Complier Average Causal Effect;likelihood based methods;NRC Report;time-to event methods;Ich E9;composite approaches;IPW Estimator;repeated measures;Full Data Likelihood;SAS;Intermittent Missing Data;R;MNAR Model;primary estimand;Missing Data;intercurrent events;Data Set;estimator's methodology;Imputation Model;clinical trials;IPW;sensitivity analysis;Principal Stratification;SAS Code;Post-baseline Visit;Missing Data Assumption;MAR Mechanism;Baseline Observation Carried Forward;Augmented Inverse Probability Weighted;Proc Sort Data;Clinical Practice;Average Causal Effect;MCAR Mechanism;CD4 Count;Proc Mi
Section I Setting the Stage 1. Introduction 2. Why Are Estimands Important? Section II Estimands 3. Estimands and How to Define Them 4. Strategies for Dealing with Intercurrent Events 5. Examples from Actual Clinical Trials in Choosing and Specifying Estimands 6. Causal Inference and Estimands 7. Putting the Principles into Practice Section III Estimators and Sensitivity 8. Overview of Estimators 9. Modeling Considerations 10. Overview of Analyses for Composite Intercurrent Event Strategies 11. Overview of Analyses for Hypothetical Intercurrent Event Strategies 12. Overview of Analyses for Principal Stratification Intercurrent Event Strategies 13. Overview of Analyses for While-on-Treatment Intercurrent Event Strategies 14. Overview of Analyses for Treatment Policy Intercurrent Event Strategies 15. Missing Data 16. Sensitivity Analyses Section IV Technical Details on Selected Analyses 17. Example Data 18. Direct Maximum Likelihood 19. Multiple Imputation 20. Inverse Probability Weighted Generalized Estimated Equations 21. Doubly Robust Methods 22. Reference-Based Imputation 23. Delta Adjustment 24. Overview of Principal Stratification Methods Section V Case Studies: Detailed Analytic Examples 25. Analytic Case Study of Depression Clinical Trials 26. Analytic Case Study Based on the ACTG 175 HIV Trial
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
LOCF Approach;Complier Average Causal Effect;likelihood based methods;NRC Report;time-to event methods;Ich E9;composite approaches;IPW Estimator;repeated measures;Full Data Likelihood;SAS;Intermittent Missing Data;R;MNAR Model;primary estimand;Missing Data;intercurrent events;Data Set;estimator's methodology;Imputation Model;clinical trials;IPW;sensitivity analysis;Principal Stratification;SAS Code;Post-baseline Visit;Missing Data Assumption;MAR Mechanism;Baseline Observation Carried Forward;Augmented Inverse Probability Weighted;Proc Sort Data;Clinical Practice;Average Causal Effect;MCAR Mechanism;CD4 Count;Proc Mi
