# Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions

arXiv: https://arxiv.org/abs/2208.05844 · HTML: https://mlsanity.com/paper/2208.05844/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 76 references that are in the map.
- First posted on arXiv 11 Aug 2022.

## Builds on (most cited first, 21 of 21)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [Optimal Best Arm Identification with Fixed Confidence](https://mlsanity.com/paper/1602.04589/) (2016)
- [lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits](https://mlsanity.com/paper/1312.7308/) (2013)
- [Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem](https://mlsanity.com/paper/1605.09004/) (2016)
- [On Inductive Biases for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2106.03765/) (2021)
- [Pure Exploration with Multiple Correct Answers](https://mlsanity.com/paper/1902.03475/) (2019)
- [An optimal algorithm for the Thresholding Bandit Problem](https://mlsanity.com/paper/1605.08671/) (2016)
- [The True Sample Complexity of Identifying Good Arms](https://mlsanity.com/paper/1906.06594/) (2019)
- [Why Adaptively Collected Data Have Negative Bias and How to Correct for It](https://mlsanity.com/paper/1708.01977/) (2017)
- [Good Arm Identification via Bandit Feedback](https://mlsanity.com/paper/1710.06360/) (2017)
- [Best arm identification in multi-armed bandits with delayed feedback](https://mlsanity.com/paper/1803.10937/) (2018)
- [Are sample means in multi-armed bandits positively or negatively biased?](https://mlsanity.com/paper/1905.11397/) (2019)
- [Always Valid Inference: Bringing Sequential Analysis to A/B Testing](https://mlsanity.com/paper/1512.04922/) (2015)
- [A/B/n Testing with Control in the Presence of Subpopulations](https://mlsanity.com/paper/2110.15573/) (2021)
- [Thresholding Bandits with Augmented UCB](https://mlsanity.com/paper/1704.02281/) (2017)
- [A unified framework for bandit multiple testing](https://mlsanity.com/paper/2107.07322/) (2021)
- [Online control of the familywise error rate](https://mlsanity.com/paper/1910.04900/) (2019)
- [Asynchronous Parallel Empirical Variance Guided Algorithms for the Thresholding Bandit Problem](https://mlsanity.com/paper/1704.04567/) (2017)
- [Thresholding Bandit with Optimal Aggregate Regret](https://mlsanity.com/paper/1905.11046/) (2019)
- [Blinded sample size re-calculation in multiple composite population designs with normal data and baseline adjustments](https://mlsanity.com/paper/2011.14735/) (2020)

## Built on by (most cited first, 2 of 2)

- [Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions](https://mlsanity.com/paper/2503.09226/) (2025)
- [Active Context Selection Improves Simple Regret in Contextual Bandits](https://mlsanity.com/paper/2605.20040/) (2026)

## Abstract (arXiv)

We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. This type of adaptive clinical trial has been thoroughly studied in biostatistics, but has been allowed only limited adaptivity so far. Here, we aim to relax classical restrictions on such designs and investigate how to incorporate ideas from the recent machine learning literature on adaptive and online experimentation to make trials more flexible and efficient. We find that the unique characteristics of the subpopulation selection problem -- most importantly that (i) one is usually interested in finding subpopulations with any treatment benefit (and not necessarily the single subgroup with largest effect) given a limited budget and that (ii) effectiveness only has to be demonstrated across the subpopulation on average -- give rise to interesting challenges and new desiderata when designing algorithmic solutions. Building on these findings, we propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction. We empirically investigate their performance across a range of simulation scenarios and derive insights into their (dis)advantages across different settings.