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Handling missing within-study correlations in the evaluation of surrogate endpoints.
Willem Collier1,2, Benjamin Haaland2, Lesley Inker3
1Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Statistics in Medicine
|October 17, 2023
Summary
Evaluating surrogate endpoints requires analyzing associations between treatment effects across trials. Improperly handling missing within-study correlations can bias results, but novel strategies can improve surrogate quality assessment.
Area of Science:
- Biostatistics
- Clinical Trial Analysis
- Pharmacometrics
Background:
- Surrogate endpoints are crucial for efficient clinical trial evaluation.
- Assessing surrogate endpoint performance requires trial-level meta-analysis of treatment effects.
- Bias can arise from unaddressed sampling error and correlations in treatment effects.
Approach:
- Quantifies the association between treatment effects on clinical and surrogate endpoints across multiple trials.
- Employs statistical models that account for sampling error and potential correlations in trial-level treatment effects.
- Introduces novel methods to address missing within-study correlation data.
Key Points:
- Within-study correlations between treatment effects are vital for unbiased surrogate endpoint evaluation.
- Missing correlation data can significantly distort the perceived performance of surrogate endpoints.
- Accurate statistical modeling is essential for reliable surrogate endpoint validation.
Conclusions:
- Novel strategies are presented to effectively handle missing within-study correlation data.
- Improved methods enhance the rigorous evaluation of surrogate endpoints in meta-analyses.
- Accurate surrogate endpoint assessment is critical for drug development and regulatory decision-making.
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