Mixed effects models for healthcare longitudinal data with an informative visiting process: a Monte Carlo simulation study

TL;DR

This study evaluates methods for analyzing healthcare longitudinal data with informative visit times, proposing a joint modeling approach and demonstrating its effectiveness through simulations and real data application.

Contribution

It introduces a joint modeling framework for the observation process and outcomes, addressing biases caused by informative visiting times in healthcare data.

Findings

Joint model reduces bias in estimates

Simulation shows improved accuracy over traditional methods

Software implementation facilitates practical application

Abstract

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with healthcare data such assumptions unlikely holds. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.