# Finding and assessing treatment effect sweet spots in clinical trial data

arXiv: https://arxiv.org/abs/2011.10157 · HTML: https://mlsanity.com/paper/2011.10157/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation](https://mlsanity.com/paper/2111.14272/) (2021)

## Abstract (arXiv)

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.