Simulation studies of surrogate endpoint validation using single trial and multitrial statistical approaches.
Marissa Lassere1, Kent Johnson, Michael Hughes
1Department of Rheumatology, St. George Hospital, University of New South Wales, Sydney, Australia. marissa.lassere@sesiahs.health.nsw.gov.au
The Journal of Rheumatology
|March 9, 2007
Summary
Multitrial statistical approaches best validate surrogate markers, demonstrating a treatment-associated change in the surrogate predicts outcome changes. Single trial methods and simple statistics like proportion explained are insufficient for surrogate validity.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Epidemiology
Background:
- A schema for assessing surrogate validity includes Target, Study Design, Statistical Strength, and Penalties.
- This study focuses on the statistical validation methods for surrogate markers within this schema.
Purpose of the Study:
- To survey and compare statistical methods for surrogate marker validation.
- To evaluate these methods using simulated datasets under various scenarios.
Main Methods:
- Generated simulated datasets with continuous, multivariate normal distributions.
- Simulated three relationships between surrogate (S) and true (T) outcomes (none, weak, strong).
- Applied four treatment effect scenarios (on both, neither, S only, T only).
- Analyzed data using single and multitrial statistical approaches.
Main Results:
- The multitrial surrogate threshold effect effectively captured the validation requirement.
- This method showed that a treatment-associated change in the surrogate predicts a treatment-associated change in the outcome.
Conclusions:
- Neither single trial nor single trial statistical methods adequately establish surrogate validity.
- Summary statistics like proportion of effect explained were found to be problematic.
- Further research is needed on subject-level vs. trial-level data modeling and robust multitrial approaches for few trials.
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