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Surrogacy marker paradox measures in meta-analytic settings.
Michael R Elliott1, Anna S C Conlon2, Yun Li2
1Department of Biostatistics, University of Michigan, School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109 USA mrelliot@umich.edu.
Biostatistics (Oxford, England)
|September 20, 2014
Summary
Clinical trials use surrogate markers to predict treatment outcomes, but "surrogate paradoxes" can lead to incorrect conclusions. This study proposes methods to assess and mitigate the risk of these paradoxes in clinical research.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Causal Inference
Background:
- Clinical trials often use surrogate markers due to the time and cost of measuring clinical outcomes.
- Traditional surrogate marker evaluation focused on explained treatment effects.
- Causal inference frameworks offer formal definitions for high-quality surrogate markers.
Purpose of the Study:
- To develop methods for assessing the risk of surrogate paradoxes.
- To quantify the probability of differing treatment effect directions between a marker and an outcome.
- To determine the necessary treatment effect on a marker to minimize risks of harmful effects on the outcome.
Main Methods:
- Utilizing a meta-analytic causal association framework.
- Developing measures to assess the risk of surrogate paradoxes.
- Conducting simulations and applying methods to two real-world applications.
Main Results:
- Identified surrogate paradoxes as a significant risk in clinical trials.
- Proposed quantitative measures for assessing surrogate paradox risk.
- Demonstrated the utility of the causal association framework for evaluating surrogate markers.
Conclusions:
- The proposed measures can help identify and mitigate risks associated with surrogate markers.
- Accurate surrogate marker evaluation is crucial for reliable clinical trial conclusions.
- The causal association framework provides a robust approach to surrogate marker assessment.
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