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Surrogate endpoint metaregression: useful statistics for regulators and trialists
Stuart G Baker1, Marissa N D Lassere2, Wang Pok Lo3
1Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA.
Journal of Clinical Epidemiology
|September 2, 2024
Summary
Surrogate endpoints help estimate treatment effects faster. New metaregression statistics improve the application and evaluation of these surrogate endpoints in clinical trials.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Pharmacometrics
Background:
- Surrogate endpoints offer a faster way to assess treatment efficacy compared to true endpoints.
- Evaluating the reliability of surrogate endpoints is crucial for drug development and regulatory decisions.
Purpose of the Study:
- To introduce and discuss statistical methods for applying and evaluating surrogate endpoints using metaregression analysis of historical randomized trials.
- To provide practical statistics for regulators and clinical trialists involved in new treatment evaluations.
Main Methods:
- Employed two types of linear metaregressions (simple and novel random effects) on trial-level data.
- Calculated key statistics including the estimated intercept, 95% prediction interval, surrogate threshold effect proportion, sample size multiplier, and true endpoint advantage.
Main Results:
- Demonstrated the application of these statistics using metaregressions from studies in antihypertension, breast cancer screening, and colorectal cancer treatment.
- Highlighted the importance of a small or non-significant intercept for increased confidence in extrapolating treatment effects.
Conclusions:
- Recommended the use of these novel statistical measures for regulators and trialists when working with surrogate endpoints.
- Emphasized that these statistics enhance the rigor and efficiency of evaluating new treatments based on surrogate endpoint data.
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