English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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