  Treatment benefit predictors (TBPs) map patient characteristics into an
estimate of the treatment benefit tailored to individual patients, which can
support optimizing treatment decisions. However, the assessment of their
performance might be challenging with the non-random treatment assignment. This
study conducts a conceptual analysis, which can be applied to finite-sample
studies. We present a framework for evaluating TBPs using observational data
from a target population of interest. We then explore the impact of confounding
bias on TBP evaluation using measures of discrimination and calibration, which
are the moderate calibration and the concentration of the benefit index
($C_b$), respectively. We illustrate that failure to control for confounding
can lead to misleading values of performance metrics and establish how the
confounding bias propagates to an evaluation bias to quantify the explicit bias
for the performance metrics. These findings underscore the necessity of
accounting for confounding factors when evaluating TBPs, ensuring more reliable
and contextually appropriate treatment decisions.
