  In electronic health records (EHRs), latent subgroups of patients may exhibit
distinctive patterning in their longitudinal health trajectories. For such
data, growth mixture models (GMMs) enable classifying patients into different
latent classes based on individual trajectories and hypothesized risk factors.
However, the application of GMMs is hindered by the special missing data
problem in EHRs, which manifests two patient-led missing data processes: the
visit process and the response process for an EHR variable conditional on a
patient visiting the clinic. If either process is associated with the process
generating the longitudinal outcomes, then valid inferences require accounting
for a nonignorable missing data mechanism. We propose a Bayesian shared
parameter model that links GMMs of multiple longitudinal health outcomes, the
visit process, and the response process of each outcome given a visit using a
discrete latent class variable. Our focus is on multiple longitudinal health
outcomes for which there can be a clinically prescribed visit schedule. We
demonstrate our model in EHR measurements on early childhood weight and height
z-scores. Using data simulations, we illustrate the statistical properties of
our method with respect to subgroup-specific or marginal inferences. We built
the R package EHRMiss for model fitting, selection, and checking.
