# A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens

arXiv: https://arxiv.org/abs/2107.07875 · HTML: https://mlsanity.com/paper/2107.07875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Interpretable Dynamic Treatment Regimes](https://mlsanity.com/paper/1606.01472/) (2016)
- [Robust Q-learning](https://mlsanity.com/paper/2003.12427/) (2020)

## Abstract (arXiv)

A dynamic treatment regimen (DTR) is a set of decision rules to personalize treatments for an individual using their medical history. The Q-learning-based Q-shared algorithm has been used to develop DTRs that involve decision rules shared across multiple stages of intervention. We show that the existing Q-shared algorithm can suffer from non-convergence due to the use of linear models in the Q-learning setup, and identify the condition under which Q-shared fails. We develop a penalized Q-shared algorithm that not only converges in settings that violate the condition, but can outperform the original Q-shared algorithm even when the condition is satisfied. We give evidence for the proposed method in a real-world application and several synthetic simulations.