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

Alessandro Gasparini*, Keith R. Abrams, Jessica K. Barrett, Rupert W. Major, Michael J. Sweeting, Nigel J. Brunskill, Michael J. Crowther

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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 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.

Original languageEnglish
Pages (from-to)5-23
Number of pages19
JournalStatistica Neerlandica
Volume74
Issue number1
DOIs
Publication statusPublished - 1 Feb 2020
Externally publishedYes

Bibliographical note

Funding Information:
The authors would like to thank all primary care practices and Nene Clinical Commissioning Group for participating in the PSP-CKD study. The PSP-CKD study was funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East Midlands. Ongoing support for the study is funded by NIHR CLAHRC East Midlands and Kidney Research UK (Grant TF2/2015). JKB is supported by the MRC Unit Programme MC_UU_00002/5. MJC is partially supported by a MRC New Investigator Research Grant (MR/P015433/1).

Funding Information:
The authors would like to thank all primary care practices and Nene Clinical Commissioning Group for participating in the PSP‐CKD study. The PSP‐CKD study was funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East Midlands. Ongoing support for the study is funded by NIHR CLAHRC East Midlands and Kidney Research UK (Grant TF2/2015). JKB is supported by the MRC Unit Programme MC_UU_00002/5. MJC is partially supported by a MRC New Investigator Research Grant (MR/P015433/1).

Publisher Copyright:
© 2019 The Authors. Statistica Neerlandica Published by John Wiley & Sons, Ltd. on behalf of VVS.

Keywords

  • electronic health records
  • informative visiting process
  • inverse intensity of visiting weighting
  • longitudinal data
  • mixed-effects models
  • Monte Carlo simulation
  • recurrent-events models
  • selection bias

Fingerprint

Dive into the research topics of 'Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study'. Together they form a unique fingerprint.

Cite this