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Eliciting and using expert opinions about dropout bias in randomized controlled trials
Ian R White1, James Carpenter, Stephen Evans
1MRC Biostatistics Unit, Cambridge UK. ian.white@mrc-bsu.cam.ac.uk
Clinical Trials (London, England)
|April 26, 2007
Summary
This study introduces a practical method for analyzing clinical trial data with missing outcomes. It incorporates expert opinions to assess uncertainty, offering a more conservative approach than standard methods.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Data Analysis
Background:
- Clinical trial analysis often assumes data are 'missing at random' (MAR), which is frequently unrealistic.
- Assessing the impact of departures from MAR is crucial for robust trial conclusions.
Purpose of the Study:
- To develop a practical and accessible method for eliciting and incorporating expert opinions on departures from MAR in clinical trial sensitivity analyses.
- To improve the accuracy and meaningfulness of sensitivity analyses when data are potentially not missing at random.
Main Methods:
- Elicited expert prior beliefs regarding the mean difference between missing and observed outcomes.
- Performed Bayesian synthesis of trial data with expert prior beliefs using full Bayesian analysis and a simple approximate formula.
- Illustrated the approach with a re-analysis of a peer review quality improvement trial.
Main Results:
- The approximate formula showed good agreement with the full Bayesian analysis.
- Both methods revealed substantially larger standard errors compared to analyses assuming MAR.
- The approach demonstrated robustness in handling potentially informatively missing data.
Conclusions:
- The proposed method provides a more conservative estimate of treatment effects when data are potentially informatively missing.
- It offers a practical and accessible alternative to methods like 'last observation carried forward'.
- This approach should be considered for future clinical trial design and analysis to account for greater uncertainty.
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