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E-raamat: Prevention and Treatment of Missing Data in Clinical Trials

  • Formaat: 162 pages
  • Ilmumisaeg: 21-Dec-2010
  • Kirjastus: National Academies Press
  • Keel: eng
  • ISBN-13: 9780309158152
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  • Formaat: 162 pages
  • Ilmumisaeg: 21-Dec-2010
  • Kirjastus: National Academies Press
  • Keel: eng
  • ISBN-13: 9780309158152
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"Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data."--Publisher's description.
Glossary xiii
Summary 1(6)
1 Introduction And Background 7(14)
Randomization and Missing Data,
8(4)
Three Kinds of Trials as Case Studies,
12(4)
Trials for Chronic Pain,
12(1)
Trials for the Treatment of HIV,
13(1)
Trials for Mechanical Circulatory Devices for Severe Symptomatic Heart Failure,
14(2)
Clinical Trials in a Regulatory Setting,
16(2)
Domestic and International Guidelines on Missing Data in Clinical Trials,
18(1)
Report Scope and Structure,
19(2)
2 Trial Designs To Reduce The Frequency Of Missing Data 21(18)
Trial Outcomes and Estimands,
22(5)
Minimizing Dropouts in Trial Design,
27(3)
Continuing Data Collection for Dropouts,
30(1)
Reflecting Loss of Power from Missing Data,
31(1)
Design Issues in the Case Studies,
32(4)
Trials for Chronic Pain,
32(2)
Trials for Treatment of HIV,
34(2)
Trials for Mechanical Circulatory Devices for Severe Symptomatic Heart Failure,
36(3)
3 Trial Strategies To Reduce The Frequency Of Missing Data 39(8)
Reasons for Dropouts,
39(1)
Actions for Design and Management Teams,
40(1)
Actions for Investigators and Site Personnel,
41(2)
Targets for Acceptable Rates of Missing Data,
43(4)
4 Drawing Inferences From Incomplete Data 47(36)
Principles,
48(1)
Notation,
49(1)
Assumptions About Missing Data and Missing Data Mechanisms,
50(4)
Missing Data Patterns and Missing Data Mechanisms,
50(1)
Missing Completely at Random,
51(1)
Missing at Random,
51(1)
MAR for Monotone Missing Data Patterns,
52(1)
Missing Not at Random,
53(1)
Example: Hypertension Trial with Planned and Unplanned Missing Data,
54(1)
Summary,
54(1)
Commonly Used Analytic Methods Under MAR,
54(16)
Deletion of Cases with Missing Data,
55(1)
Inverse Probability Weighting,
56(3)
Likelihood Methods,
59(6)
Imputation-Based Approaches,
65(5)
Event Time Analyses,
70(1)
Analytic Methods Under MNAR,
70(8)
Definitions: Full Data, Full Response Data, and Observed Data,
71(1)
Selection Models,
72(1)
Pattern Mixture Models,
73(1)
Advantages and Disadvantages of Selection and Pattern Mixture Models,
74(2)
Recommendations,
76(2)
Instrumental Variable Methods for Estimating Treatment Effects Among Compliers,
78(3)
Missing Data in Auxiliary Variables,
81(2)
5 Principles And Methods Of Sensitivity Analyses 83(24)
Background,
83(2)
Framework,
85(1)
Example: Single Outcome, No Auxiliary Data,
86(5)
Pattern Mixture Model Approach,
88(1)
Selection Model Approach,
89(2)
Example: Single Outcome with Auxiliary Data,
91(5)
Pattern Mixture Model Approach,
91(3)
Selection Model Approach,
94(2)
Example: General Repeated Measures Setting,
96(7)
Monotone Missing Data,
98(5)
Nonmonotone Missing Data,
103(1)
Comparing Pattern Mixture and Selection Approaches,
103(1)
Time-to-Event Data,
104(1)
Decision Making,
105(1)
Recommendation,
106(1)
6 Conclusions And Recommendations 107(8)
Trial Objectives,
108(1)
Reducing Dropouts Through Trial Design,
108(1)
Reducing Dropouts Through Trial Conduct,
109(1)
Treating Missing Data,
110(1)
Understanding the Causes and Degree of Dropouts in Clinical Trials,
111(4)
References 115(8)
Appendixes
A Clinical Trials: Overview and Terminology
123(16)
B Biographical Sketches of Panel Members and Staff
139