Bayesian likelihood-based regression for estimation of optimal dynamic treatment regimes

Clinicians often make sequences of treatment decisions that can be framed as dynamic treatment regimes. In this paper, we propose a Bayesian likelihood-based dynamic treatment regime model that incorporates regression specifications to yield interpretable relationships between covariates and stage-wise outcomes. We define a set of probabilistically-coherent properties for dynamic treatment regime processes and present the theoretical advantages that are consequential to these properties. We justify the likelihood-based approach by showing that it guarantees these probabilistically-coherent properties, whereas existing methods lead to process spaces that typically violate these properties and lead to modelling assumptions that are infeasible. Through a numerical study, we show that our proposed method can achieve superior performance over existing state-of-the-art methods.

Automated summary

In this paper, a Bayesian likelihood-based dynamic treatment regime model is proposed, which incorporates regression specifications to interpret the relationships between covariates and stage-wise outcomes. A set of probabilistically-coherent properties for dynamic treatment regime processes are defined, and the theoretical advantages consequential to these properties are presented. Through a numerical study, it is demonstrated that the proposed method outperforms existing state-of-the-art methods.