In modern clinical trials, there is immense pressure to use surrogate markers
in place of an expensive or long-term primary outcome to make more timely
decisions about treatment effectiveness. Cependant, using a surrogate marker to
test for a treatment effect can be difficult and controversial. Existing
methods tend to either rely on fully parametric methods where strict
assumptions are made about the relationship between the surrogate and the
outcome, or assume the surrogate marker is valid for the entire study
population. In this paper, we develop a fully nonparametric method for
efficient testing using surrogate information (ETSI). Our approach is
specifically designed for settings where there is heterogeneity in the utility
of the surrogate marker, i.e., the surrogate is valid for certain patient
subgroups and not others. ETSI enables treatment effect estimation and
hypothesis testing via kernel-based estimation for a setting where the
surrogate is used in place of the primary outcome for individuals for whom the
surrogate is valid, and the primary outcome is purposefully only measured in
the remaining patients. En outre, we provide a framework for future study
design with power and sample size estimates based on our proposed testing
procedure. We demonstrate the performance of our methods via a simulation study
and application to two distinct HIV clinical trials.

Cet article explore les excursions dans le temps et leurs implications.

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