Modeling Heterogeneity and Missing Data of Multiple Longitudinal Outcomes in Electronic Health Records

TL;DR

This paper introduces a Bayesian shared parameter model for analyzing multiple longitudinal health outcomes in EHR data, accounting for nonignorable missing data mechanisms and heterogeneity among patient subgroups.

Contribution

It develops a novel joint modeling approach linking growth mixture models with visit and response processes, implemented in the R package EHRMiss.

Findings

Model effectively captures patient heterogeneity.

Handles nonignorable missing data mechanisms.

Demonstrates accurate subgroup inference in simulations.

Abstract

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.