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Addressing continuous data for participants excluded from trial analysis: a guide for systematic reviewers
Shanil Ebrahim1, Elie A Akl, Reem A Mustafa
1Department of Clinical Epidemiology & Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Canada, L8S 4K1.
Journal of Clinical Epidemiology
|June 19, 2013
Summary
This study introduces a framework to assess how missing participant data affects continuous outcomes in systematic reviews. The developed imputation strategies help evaluate the risk of bias and confidence in review results.
Area of Science:
- Biostatistics
- Evidence Synthesis
- Clinical Research Methodology
Background:
- Current methods inadequately address the impact of missing participant data for continuous outcomes in systematic reviews concerning risk of bias.
- Assessing the influence of missing data on risk of bias assessments is crucial for reliable systematic reviews.
Purpose of the Study:
- To develop and present a quantitative framework for handling missing participant data in systematic reviews with continuous outcomes.
- To provide guidance on judging the impact of missing data on the risk of bias in systematic reviews.
Main Methods:
- A consultative, iterative process was employed to create a framework for managing missing participant data.
- The framework considered real observed outcomes and developed plausible imputation strategies.
- The approach was applied to two distinct systematic reviews to demonstrate its utility.
Main Results:
- Five data sources were utilized for imputing means for participants with missing data.
- Median standard deviation (SD) from control arms of included trials was used for imputing SD.
- Four imputation strategies, increasing in stringency, were developed and applied, showing varied impacts on effect estimates and confidence in results across two example reviews.
Conclusions:
- The developed approach offers practical, quantitative guidance for assessing the risk of bias stemming from missing participant data in systematic reviews.
- This framework aids in determining the impact of missing data on the reliability and confidence of systematic review findings for continuous outcomes.
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