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Identifying heterogeneous treatment effects (HTEs) in randomized controlledtrials is an important step toward understanding and acting on trial results.However, HTEs are often small and difficult to identify, and HTE modelingmethods which are very general can suffer from low power. We present a methodthat exploits any existing relationship between illness severity and treatmenteffect, and identifies the "sweet spot", the contiguous range of illnessseverity where the estimated treatment benefit is maximized. We further computea bias-corrected estimate of the conditional average treatment effect (CATE) inthe 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 andactionable: results from our method can inform future clinical trials and helpclinicians make personalized treatment recommendations.

Finding and assessing treatment effect sweet spots in clinical trial data