Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.

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 health care data, such assumptions unlikely hold. 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 o

SUBMITTER: Gasparini A 

PROVIDER: S-EPMC6919310 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature