Estimation of Optimal Treatment Regimes with Irregularly Observed Data via Bayesian Joint Modelling

Oregon Convention Center 

Optimal dynamic treatment regimes (DTR) are sequences of decision rules aimed at determining the sequence of treatments tailored to patients, maximizing a long-term outcome. While conventional DTR estimation uses longitudinal data, there is little work on devising methods that use irregularly observed data to infer optimal DTRs. In this work, we first extend the target trial framework -- a paradigm to estimate specified statistical estimands under hypothetical scenarios using observational data -- to the DTR context; this extension allows treatment regimes to be defined with intervenable visit times. We propose an adapted version of G-computation marginalizing over random effects for rewards that encapsulate a treatment strategy's value. To estimate components of the G-computation formula, we then articulate a Bayesian joint model to handle correlated random effects between the outcome, visit and treatment processes. We also extend this model to allow flexible specifications of the random effects' distribution. Lastly, we show via simulation studies that failure to account for the observational treatment and visit processes produces bias in the estimation of regime rewards.

Dynamic treatment regime

Bayesian joint modelling

Target Trial Framework

G-computation

Irregularly observed data 

Section on Bayesian Statistical Science