  The rich longitudinal individual level data available from electronic health
records (EHRs) can be used to examine treatment effect heterogeneity. However,
estimating treatment effects using EHR data poses several challenges, including
time-varying confounding, repeated and temporally non-aligned measurements of
covariates, treatment assignments and outcomes, and loss-to-follow-up due to
dropout. Here, we develop the Subgroup Discovery for Longitudinal Data (SDLD)
algorithm, a tree-based algorithm for discovering subgroups with heterogeneous
treatment effects using longitudinal data by combining the generalized
interaction tree algorithm, a general data-driven method for subgroup
discovery, with longitudinal targeted maximum likelihood estimation. We apply
the algorithm to EHR data to discover subgroups of people living with human
immunodeficiency virus (HIV) who are at higher risk of weight gain when
receiving dolutegravir-containing antiretroviral therapies (ARTs) versus when
receiving non dolutegravir-containing ARTs.
