A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens
This work addresses a technical bottleneck in personalized medicine for improving treatment optimization, though it is incremental as it builds on existing Q-learning methods.
The paper tackled the problem of non-convergence in the Q-shared algorithm for dynamic treatment regimens by developing a penalized version that ensures convergence and outperforms the original method, as demonstrated in real-world applications and synthetic simulations.
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.