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Missing data and sensitivity analysis for binary data with implications for sample size and power of randomized
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin.
Statistics in Medicine
|November 15, 2019
Summary
Missing data in clinical trials can mislead results, especially with larger sample sizes. Adjusting sample sizes for missing data is crucial, often requiring significantly larger increases than traditionally assumed.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Statistical Analysis
Background:
- Missing outcomes are frequent in randomized controlled clinical trials.
- Further research is needed on the impact of missing data on trial power and acceptable missingness rates.
Purpose of the Study:
- To demonstrate the misleading nature of complete-case analyses in clinical trials.
- To assess the impact of missing data rates and sample sizes on study conclusions.
- To provide methods for sensitivity analysis and sample size adjustment for missing data.
Main Methods:
- Illustrative analysis using binary responses.
- Application of principled sensitivity analysis to assess robustness.
- Sample size adjustment calculations accounting for expected missingness.
Main Results:
- Complete-case analyses can yield seriously misleading conclusions.
- The adverse impact of missing data intensifies with higher missingness rates and larger sample sizes.
- Sensitivity analysis reveals dramatically larger sample size adjustments are needed than traditional methods suggest.
Conclusions:
- Missing data significantly compromises the validity of clinical trial conclusions.
- Principled sensitivity analysis is essential for robust trial interpretation.
- Achieving desired power can be impossible in large trials with small effect sizes when accounting for missing data.
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