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

Author

Listed:
  • Alessandro Gasparini
  • Keith R. Abrams
  • Jessica K. Barrett
  • Rupert W. Major
  • Michael J. Sweeting
  • Nigel J. Brunskill
  • Michael J. Crowther

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.

Suggested Citation

  • Alessandro Gasparini & Keith R. Abrams & Jessica K. Barrett & Rupert W. Major & Michael J. Sweeting & Nigel J. Brunskill & Michael J. Crowther, 2020. "Mixed‐effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(1), pages 5-23, February.
    • Handle: RePEc:bla:stanee:v:74:y:2020:i:1:p:5-23
    DOI: 10.1111/stan.12188
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    References listed on IDEAS

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    1. Charles E. McCulloch & John M. Neuhaus & Rebecca L. Olin, 2016. "Biased and unbiased estimation in longitudinal studies with informative visit processes," Biometrics, The International Biometric Society, vol. 72(4), pages 1315-1324, December.
    2. Peter Tanuseputro & Walter P Wodchis & Rob Fowler & Peter Walker & Yu Qing Bai & Sue E Bronskill & Douglas Manuel, 2015. "The Health Care Cost of Dying: A Population-Based Retrospective Cohort Study of the Last Year of Life in Ontario, Canada," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-11, March.
    3. Lei Liu & Xuelin Huang & John O'Quigley, 2008. "Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data," Biometrics, The International Biometric Society, vol. 64(3), pages 950-958, September.
    4. Michael J. Crowther & Paul C. Lambert, 2012. "Simulating complex survival data," Stata Journal, StataCorp LP, vol. 12(4), pages 674-687, December.
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