# Active Clinical Trials for Personalized Medicine

arXiv: https://arxiv.org/abs/1404.2971 · HTML: https://mlsanity.com/paper/1404.2971/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2014.

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

- [Active Learning for Stochastic Contextual Linear Bandits](https://mlsanity.com/paper/2605.24803/) (2026)
- [Beyond ATE: Multi-Criteria Design for A/B Testing](https://mlsanity.com/paper/2509.05864/) (2025)

## Abstract (arXiv)

Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this paper, we propose a cost-effective estimation method from an active learning perspective. In particular, our method recruits only the "most informative" patients (in terms of learning the optimal ITRs) from an ongoing clinical trial. Simulation studies and real-data examples show that our active clinical trial method significantly improves on competing methods. We derive risk bounds and show that they support these observed empirical advantages.