  Identifying heterogeneous treatment effects (HTEs) in randomized controlled
trials is an important step toward understanding and acting on trial results.
However, HTEs are often small and difficult to identify, and HTE modeling
methods which are very general can suffer from low power. We present a method
that exploits any existing relationship between illness severity and treatment
effect, and identifies the "sweet spot", the contiguous range of illness
severity where the estimated treatment benefit is maximized. We further compute
a bias-corrected estimate of the conditional average treatment effect (CATE) in
the sweet spot, and a $p$-value. Because we identify a single sweet spot and
$p$-value, we believe our method to be straightforward to interpret and
actionable: results from our method can inform future clinical trials and help
clinicians make personalized treatment recommendations.
