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When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical
Janus Christian Jakobsen1,2, Christian Gluud3, Jørn Wetterslev3
1The Copenhagen Trial Unit, Centre for Clinical Intervention Research, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark. jcj@ctu.dk.
BMC Medical Research Methodology
|December 7, 2017
Summary
Appropriate handling of missing data is crucial for unbiased results in randomized clinical trials. This guide offers practical strategies and flowcharts for managing missing data, emphasizing multiple imputation for accurate analysis.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Data Analysis
Background:
- Missing data can significantly bias randomized clinical trial (RCT) inferences.
- Bias potential is influenced by the missing data mechanism and analytical methods.
- Careful planning and attention are essential for analyzing RCT data with missing values.
Purpose of the Study:
- To provide a practical guide for optimizing the handling of missing data in RCTs.
- To recommend analytical approaches that minimize bias from unavoidable missing data.
- To outline steps for addressing missing data during trial planning and analysis.
Main Methods:
- Literature search of PubMed and reference lists for methods on handling missing data in RCTs.
- Discussion and consideration of various statistical approaches.
- Development of practical flowcharts for data handling.
Main Results:
- Identified key considerations for optimizing missing data handling during RCT planning.
- Evaluated strengths and limitations of sensitivity analyses (best-worst, worst-best), multiple imputation, and full information maximum likelihood.
- Presented practical flowcharts and an overview of analysis steps.
Conclusions:
- A practical guide and flowcharts are provided for using multiple imputation to handle missing data in RCTs.
- Emphasizes the importance of strategic planning and appropriate analytical methods for missing data.
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