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Flexible Individualized Treatment Strategies in Micro Randomized Trials with Binary Rewards

Abstract Number:

1651 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Madeline Abbott (1), Rachel Gonzalez (2), Walter Dempsey (2), Scott Hummel (2), Brahmajee Nallamothu (2), Michael Dorsch (2)

Institutions:

(1) N/A, N/A, (2) University of Michigan, N/A

Co-Author(s):

Rachel Gonzalez  
University of Michigan
Walter Dempsey  
University of Michigan
Scott Hummel  
University of Michigan
Brahmajee Nallamothu  
University of Michigan
Michael Dorsch  
University of Michigan

First Author:

Madeline Abbott  
N/A

Presenting Author:

Rachel Gonzalez  
University of Michigan

Abstract Text:

Micro-randomized trials (MRTs) are often used in mHealth studies to assess app-based interventions. Participants are randomized to receive treatment at a series of decision points, traditionally using the same rule across individuals. Several recent MRTs utilize Thompson Sampling (TS), a reinforcement learning algorithm, to build individualized treatment strategies that optimize delivery with respect to a reward. Treatment may interact with several contextual features, but estimation of models in this setting can be unreliable. This is especially difficult with a binary reward where complete separation often occurs, even with a large sample and few features. We present an approach to balance algorithmic flexibility and computational cost in the context of a binary reward that (1) uses partial pooling and weakly informative priors that apply more shrinkage to higher-order interactions and (2) considers the amount of information available in the data when defining a model. Our approach is useful in MRTs where the TS algorithm must be automated. We demonstrate the empirical utility of our method in a digital twin of an ongoing MRT study, LowSalt4Life, compared to logical alternatives.

Keywords:

Mobile health|Micro-randomized trials|Clinical trials|Reinforcement learning|Individualized treatment|

Sponsors:

Biometrics Section

Tracks:

Personalized/Precision Medicine

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