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Abstract:
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Most methods for evaluating biomarkers as potential surrogate endpoints measure validity in terms of net effects (i.e., treatment effects adjusted for the biomarker measured after randomization). Frangakis and Rubin (2002, Biometrics) criticized these approaches because net effects may reflect selection bias, and suggested an alternative definition of a surrogate endpoint (a principal surrogate) based on causal effects. This talk will introduce a "causal effect predictiveness surface" that can be used for evaluating and comparing the quality of biomarkers as surrogate endpoints. Assumptions and study designs that identify this surface, and parametric likelihood-based methods for estimating it, will be described. This work is motivated by the problem of assessing an immune response to an HIV vaccine as a surrogate endpoint for HIV infection, which will be used as an illustrative example.
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