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Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study |
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| Authors: | Alessandro Gasparini Keith R Abrams Jessica K Barrett Rupert W Major Michael J Sweeting Nigel J Brunskill Michael J Crowther |
| Institution: | 1. Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK;2. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK;3. Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK
Department of Nephrology, University Hospitals of Leicester NHS Trust, Leicester, UK;4. Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK;5. Department of Nephrology, University Hospitals of Leicester NHS Trust, Leicester, UK Department of Infection Immunity and Inflammation, University of Leicester, Leicester, UK |
| 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. |
| 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 |