Bayesian Semiparametric Estimation of Optimal Treatment Regimes with Irregularly Observed Data

Music City Center 

In statistical precision medicine, optimal dynamic treatment regimes (DTR) are sequences of decision rules assigning treatment tailored to patients' observed history, maximizing long-term patient outcome, referred to as the reward. While Bayesian estimation of reward under different treatment regimes often require explicit specification of all model components, non-tailoring confounders can instead be accounted for using inverse weighting through maximizing the utility, defined as the log-likelihood marginalized over these confounders. DTR estimation methods focus on correcting for confounding under observed longitudinal data. However, they can be also irregular, where visit times can be driven by patient history; failure to account for this irregularity can induce bias in reward and optimal DTR estimates. In this work, we extend existing weighting approaches for DTR estimation within the Bayesian paradigm using irregularly observed data. We showcase through simulation studies that we can estimate of rewards and optimal regimes without bias by using a double weighting approach, with inverse weighting terms to control both confounding and visit irregularity.

Dynamic treatment regimes

irregularly observed data

inverse weighting

Bayesian semiparametric estimation

statistical precision medicine 

Section on Bayesian Statistical Science