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Design of multiple binary outcome studies with intentionally missing data
1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.
Biometrics
|December 1, 1996
Summary
This study introduces a global test statistic for analyzing multiple binary outcomes with intentionally missing data, crucial for toxicological studies. The method enhances study design efficiency when not all outcomes can be measured.
Area of Science:
- Biostatistics
- Toxicology
- Statistical Modeling
Background:
- Studies with multiple binary outcomes often face practical constraints leading to intentionally missing data.
- Toxicology studies frequently encounter situations where measuring all outcomes per individual is infeasible or uneconomical.
Purpose of the Study:
- To present and evaluate a global test statistic for analyzing multiple binary outcomes with intentionally missing data.
- To assess the efficiency of this statistic under various missing data patterns and correlation structures.
Main Methods:
- Development of a global test statistic using generalized estimating equations (GEE).
- Evaluation of the statistic's performance with different missing data patterns and correlation structures.
- Extension of the statistic to accommodate clustered data.
Main Results:
- The global test statistic demonstrates robustness across various missing data scenarios.
- Relative efficiency calculations show the impact of missing data on study power.
- Differential exposure effects on multiple endpoints are considered.
Conclusions:
- The proposed global test statistic is a valuable tool for studies with multiple binary outcomes and missing data.
- Efficiency calculations provide critical insights for optimizing study design in toxicology and related fields.
- Recommendations are offered for practical study design and analysis.
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