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Sample size calculations for evaluating a diagnostic test when the gold standard is missing at random.
Andrzej S Kosinski1, Ying Chen, Robert H Lyles
1Department of Biostatistics and Bioinformatics, Duke University Medical Center, Duke Clinical Research Institute, P. O. Box 17969, Durham, NC 27715, USA. andrzej.kosinski@duke.edu
Statistics in Medicine
|June 17, 2010
Summary
This study addresses missing gold standard data in diagnostic test evaluations. We developed a sample size calculation method to ensure accurate test performance estimates when some gold standard results are missing.
Area of Science:
- Biostatistics
- Diagnostic Test Evaluation
- Clinical Trial Design
Background:
- Diagnostic test performance is typically assessed against a gold standard.
- Missing gold standard data is common in studies due to ethical or cost constraints.
- Sensitivity and specificity are key performance measures, often summarized by a joint confidence region (CR).
Purpose of the Study:
- To present a method for sample size calculation in studies with missing at random (MAR) gold standard data.
- To determine the necessary sample size increase to maintain the precision of sensitivity and specificity estimates under MAR.
- To ensure the joint confidence region under MAR is contained within the CR from complete data.
Main Methods:
- Developed a sample size computation approach for diagnostic studies with MAR gold standard data.
- Calculated the required increase in sample size to achieve desired precision.
- Focused on maintaining the integrity of the joint confidence region for sensitivity and specificity.
Main Results:
- The proposed method provides a way to adjust sample size for missing gold standard data.
- Quantified the increase in sample size needed to compensate for MAR data.
- Demonstrated how to ensure the joint CR under MAR remains within the bounds of a complete-data CR.
Conclusions:
- Accurate sample size calculation is crucial for diagnostic studies with missing gold standard data.
- The presented approach allows for robust estimation of test performance even with incomplete gold standard verification.
- This method aids in designing studies that yield reliable sensitivity and specificity estimates.
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