Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.
Abrams, Keith R
Barrett, Jessica K
Major, Rupert W
Sweeting, Michael J
Brunskill, Nigel J
Crowther, Michael J
Statistica Neerlandica, volume 74, issue 1
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Gasparini, A., Abrams, K. R., Barrett, J. K., Major, R. W., Sweeting, M. J., Brunskill, N. J., & Crowther, M. J. (2019). Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.. Statistica Neerlandica, volume 74, issue 1 https://doi.org/10.1111/stan.12188
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.
Longitudinal data, Monte Carlo simulation, Selection Bias, Electronic Health Records, Mixed‐effects Models, Informative Visiting Process, Inverse Intensity Of Visiting Weighting, Recurrent‐events Models
External DOI: https://doi.org/10.1111/stan.12188
This record's URL: https://www.repository.cam.ac.uk/handle/1810/301624
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/