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Published on: January 8, 2020
Statistical identifiability and the surrogate endpoint problem, with application to vaccine trials
Julian Wolfson1, Peter Gilbert
1Department of Biostatistics, University of Washington, Seattle, Washington 98195-7232, USA. julianw@u.washington.edu
Biometrics
|January 29, 2010
Summary
Establishing a biomarker's surrogate value is challenging. This study explores principal stratification methods, finding that causal associations are often unidentifiable, and even marginal risks require specific assumptions, complicating surrogate endpoint validation.
Area of Science:
- Biostatistics
- Clinical Trials
- Epidemiology
Background:
- The surrogate endpoint problem seeks to determine if a biomarker (S) can predict a treatment's (Z) effect on a clinical outcome (Y).
- Principal stratification is a key framework for evaluating biomarker surrogate value in statistical analyses.
Purpose of the Study:
- To analyze two principal stratification estimands: joint risks and marginal risks for surrogate endpoint evaluation.
- To investigate the identifiability of these estimands under various assumptions, particularly relaxing assumptions about early treatment effects.
Main Methods:
- Utilized the principal stratification framework to define and analyze joint and marginal risks.
- Examined the statistical identifiability of estimands under different assumptions.
- Proposed a sensitivity analysis approach based on algebraic relationships between joint and marginal risks.
Main Results:
- Joint risks, measuring causal associations of treatment effects on S and Y, are not statistically identifiable from typical vaccine trial data.
- Marginal risks, while not measuring causal associations, can be identifiable under specific data collection schemes and assumptions.
- Relaxing the assumption of no individual treatment effects before biomarker measurement impacts identifiability.
Conclusions:
- Establishing a biomarker's surrogate value is often difficult, even with large sample sizes, due to identifiability challenges.
- Sensitivity analysis is proposed to assess surrogate value when direct causal inference is not possible.
- The study highlights the complexities and limitations in using biomarkers as surrogate endpoints in clinical research.
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