ArticleStatistica Neerlandica2020
Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.
Article in Statistica Neerlandica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Dynamic Oculomics for Diagnostic Monitoring: Personal Ocular Trends, Risk Stratification and Treatment Response.Diagnostics (Basel, Switzerland) · 2026Review
- Assessing the impact of variance heterogeneity and misspecification in mixed-effects location-scale models.BMC medical research methodology · 2026Article
- Omitting patients with no follow-up leads to bias when using inverse-intensity weighted GEEs to handle irregular and informative assessment times.BMC medical research methodology · 2025Article
- Why Recommended Visit Intervals Should Be Extracted When Conducting Longitudinal Analyses Using Electronic Health Record Data: Examining Visit Mechanism and Sensitivity to Assessment Not at Random.Statistics in medicine · 2025Article
- Epidemiology of multimorbidity in childhood cancer survivors: a matched cohort study of inpatient hospitalisations in Western Australia.BJC reports · 2025Article
- A mixed-effect model for the evolution of unbalanced longitudinal hematocrit levels in chronic kidney failure patients.SAGE open medicine · 2025Article
- Psychiatric disorders in childhood cancer survivors: A retrospective matched cohort study of inpatient hospitalisations and community-based mental health services utilisation in Western Australia.The Australian and New Zealand journal of psychiatry · 2024Article
- Kidney function in healthcare clients in Khayelitsha, South Africa: Routine laboratory testing and results reflect distinct healthcare experiences by age for healthcare clients with and without HIV.PLOS global public health · 2024Article
- Studying the association between longitudinal mammographic density measurements and breast cancer risk: a joint modelling approach.Breast cancer research : BCR · 2023Article
- Estimation of marginal structural models under irregular visits and unmeasured confounder: calibrated inverse probability weights.BMC medical research methodology · 2023Article
- Randomized Trials With Repeatedly Measured Outcomes: Handling Irregular and Potentially Informative Assessment Times.Epidemiologic reviews · 2022Review
- Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).Annals of operations research · 2021Article
- Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.Statistica Neerlandica · 2020Article
- How and when informative visit processes can bias inference when using electronic health records data for clinical research.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record.Clinical epidemiology · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.