Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study
This work addresses a specific bottleneck in applying contextual bandits to mHealth studies, offering incremental improvements for researchers and practitioners in personalized health interventions.
The paper tackled the challenge of modeling count outcomes in contextual bandits for mobile health interventions by combining four count data models with Thompson sampling, showing improved user engagement in a real trial and better cumulative outcomes in simulations.
Mobile health (mHealth) interventions often aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions. Contextual bandits provide a suitable framework for customizing such interventions according to individual time-varying contexts. However, unique challenges, such as modeling count outcomes within bandit frameworks, have hindered the widespread application of contextual bandits to mHealth studies. The current work addresses this challenge by leveraging count data models into online decision-making approaches. Specifically, we combine four common offline count data models (Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regressions) with Thompson sampling, a popular contextual bandit algorithm. The proposed algorithms are motivated by and evaluated on a real dataset from the Drink Less trial, where they are shown to improve user engagement with the mHealth platform. The proposed methods are further evaluated on simulated data, achieving improvement in maximizing cumulative proximal outcomes over existing algorithms. Theoretical results on regret bounds are also derived. The countts R package provides an implementation of our approach.