Glossary |
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xiii | |
Summary |
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1 | (6) |
1 Introduction And Background |
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7 | (14) |
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Randomization and Missing Data, |
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8 | (4) |
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Three Kinds of Trials as Case Studies, |
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12 | (4) |
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12 | (1) |
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Trials for the Treatment of HIV, |
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13 | (1) |
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Trials for Mechanical Circulatory Devices for Severe Symptomatic Heart Failure, |
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14 | (2) |
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Clinical Trials in a Regulatory Setting, |
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16 | (2) |
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Domestic and International Guidelines on Missing Data in Clinical Trials, |
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18 | (1) |
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Report Scope and Structure, |
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19 | (2) |
2 Trial Designs To Reduce The Frequency Of Missing Data |
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21 | (18) |
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Trial Outcomes and Estimands, |
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22 | (5) |
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Minimizing Dropouts in Trial Design, |
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27 | (3) |
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Continuing Data Collection for Dropouts, |
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30 | (1) |
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Reflecting Loss of Power from Missing Data, |
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31 | (1) |
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Design Issues in the Case Studies, |
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32 | (4) |
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32 | (2) |
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Trials for Treatment of HIV, |
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34 | (2) |
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Trials for Mechanical Circulatory Devices for Severe Symptomatic Heart Failure, |
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36 | (3) |
3 Trial Strategies To Reduce The Frequency Of Missing Data |
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39 | (8) |
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39 | (1) |
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Actions for Design and Management Teams, |
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40 | (1) |
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Actions for Investigators and Site Personnel, |
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41 | (2) |
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Targets for Acceptable Rates of Missing Data, |
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43 | (4) |
4 Drawing Inferences From Incomplete Data |
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47 | (36) |
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48 | (1) |
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49 | (1) |
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Assumptions About Missing Data and Missing Data Mechanisms, |
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50 | (4) |
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Missing Data Patterns and Missing Data Mechanisms, |
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50 | (1) |
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Missing Completely at Random, |
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51 | (1) |
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51 | (1) |
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MAR for Monotone Missing Data Patterns, |
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52 | (1) |
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53 | (1) |
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Example: Hypertension Trial with Planned and Unplanned Missing Data, |
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54 | (1) |
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54 | (1) |
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Commonly Used Analytic Methods Under MAR, |
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54 | (16) |
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Deletion of Cases with Missing Data, |
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55 | (1) |
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Inverse Probability Weighting, |
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56 | (3) |
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59 | (6) |
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Imputation-Based Approaches, |
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65 | (5) |
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70 | (1) |
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Analytic Methods Under MNAR, |
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70 | (8) |
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Definitions: Full Data, Full Response Data, and Observed Data, |
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71 | (1) |
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72 | (1) |
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73 | (1) |
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Advantages and Disadvantages of Selection and Pattern Mixture Models, |
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74 | (2) |
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76 | (2) |
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Instrumental Variable Methods for Estimating Treatment Effects Among Compliers, |
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78 | (3) |
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Missing Data in Auxiliary Variables, |
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81 | (2) |
5 Principles And Methods Of Sensitivity Analyses |
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83 | (24) |
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83 | (2) |
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85 | (1) |
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Example: Single Outcome, No Auxiliary Data, |
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86 | (5) |
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Pattern Mixture Model Approach, |
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88 | (1) |
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Selection Model Approach, |
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89 | (2) |
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Example: Single Outcome with Auxiliary Data, |
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91 | (5) |
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Pattern Mixture Model Approach, |
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91 | (3) |
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Selection Model Approach, |
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94 | (2) |
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Example: General Repeated Measures Setting, |
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96 | (7) |
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98 | (5) |
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Nonmonotone Missing Data, |
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103 | (1) |
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Comparing Pattern Mixture and Selection Approaches, |
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103 | (1) |
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104 | (1) |
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105 | (1) |
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106 | (1) |
6 Conclusions And Recommendations |
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107 | (8) |
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108 | (1) |
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Reducing Dropouts Through Trial Design, |
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108 | (1) |
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Reducing Dropouts Through Trial Conduct, |
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109 | (1) |
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110 | (1) |
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Understanding the Causes and Degree of Dropouts in Clinical Trials, |
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111 | (4) |
References |
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115 | (8) |
Appendixes |
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A Clinical Trials: Overview and Terminology |
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123 | (16) |
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B Biographical Sketches of Panel Members and Staff |
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139 | |