  Dynamic prediction of causal effects under different treatment regimes
conditional on an individual's characteristics and longitudinal history is an
essential problem in precision medicine. This is challenging in practice
because outcomes and treatment assignment mechanisms are unknown in
observational studies, an individual's treatment efficacy is a counterfactual,
and the existence of selection bias is often unavoidable.
  We propose a Bayesian framework for identifying subgroup counterfactual
benefits of dynamic treatment regimes by adapting Bayesian g-computation
algorithm (J. Robins, 1986; Zhou, Elliott, & Little, 2019) to incorporate
multivariate generalized linear mixed-effects models. Unmeasured time-invariant
factors are identified as subject-specific random effects in the assumed joint
distribution of outcomes, time-varying confounders, and treatment assignments.
Existing methods mostly assume no unmeasured confounding and focus on balancing
the observed confounder distributions between different treatments, while our
method allows the presence of time-invariant unmeasured confounding. We propose
a sequential ignorability assumption based on treatment assignment
heterogeneity, which is analogous to balancing the latent tendency toward each
treatment due to unmeasured time-invariant factors beyond the observables. We
use simulation studies to assess the sensitivity of the proposed method's
performance to various model assumptions. The method is applied to
observational clinical data to investigate the efficacy of continuously using
mycophenolate in different subgroups of scleroderma patients who were treated
with the drug.
