ArticleStatistics in medicine2025
Multiple Imputation for Longitudinal Data: A Tutorial.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- The effect of SGLT2 inhibitors, GLP-1 receptor agonists, or DPP-4 inhibitors on stroke risk in type 2 diabetes: a nationwide longitudinal cohort study in Sweden with contextualisation analysis.EClinicalMedicine · 2026Article
- What Is a Clinically Meaningful Change in Diabetes Distress? Findings for Diabetes Care and Research From the SFDT1 Cohort.Diabetes, obesity & metabolism · 2026Article
- Study protocol for a target trial emulation: CHoosing the right diAlysis Modality in clinical PractIce: hemOdiafiltratioN or hemodialysis (CHAMPION).BMC nephrology · 2026Article
- Determinants of Sustainable Return to Work After Burnout or Depression: A Longitudinal Cohort Study.Journal of occupational rehabilitation · 2026Article
- Mind the Gap: Addressing Missing Person Time When Estimating Outcome Incidence in Longitudinal Data.Epidemiology (Cambridge, Mass.) · 2026Article
- Changes in Workplace Productivity and Estimated Cost Savings During Internet-Based Cognitive Behavioral Therapy in the Irish National Health Service: Naturalistic, Repeated-Measures, Retrospective Survey Study.Journal of medical Internet research · 2026Article
- Dissociable contributions of cortical thickness and surface area to cognitive ageing: evidence from multiple longitudinal cohorts.bioRxiv : the preprint server for biology · 2026Article
- Article
- Intersectional approaches to cognitive aging: a practical guide to modeling heterogeneous trajectories with GLMM-trees.Frontiers in psychology · 2026Article
- Missing data in microrandomized trials: Challenges and opportunities.Behavior research methods · 2025Article
- Alcohol Consumption, Risk of Periodontitis and Change of Periodontal Parameters in a Population-Based Cohort Study.Journal of clinical periodontology · 2025Article
- Bayesian Analysis of Longitudinal Ordinal Data with Missing Values Using Multivariate Probit Models.Journal of statistics applications & probability · 2025Article
- Robustness of the Pairwise-Fitting Approach Under Missing at Random Dropout: A Case and Simulation Study.Pharmaceutical statisticsArticle
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6 authors.
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Abstract
Longitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thus an analytic approach that accounts for this clustering by individual is required. While almost all research suffers from missing data, this can be particularly problematic in longitudinal studies as participation often becomes harder to maintain over time. Multiple imputation (MI) is widely used to handle missing data in such studies. When using MI, it is important that the imputation model is compatible with the proposed analysis model. In a longitudinal analysis, this implies that the clustering considered in the analysis model should be reflected in the imputation process. Several MI approaches have been proposed to impute incomplete longitudinal data, such as treating repeated measurements of the same variable as distinct variables or using generalized linear mixed imputation models. However, the uptake of these methods has been limited, as they require additional data manipulation and use of advanced imputation procedures. In this tutorial, we review the available MI approaches that can be used for handling incomplete longitudinal data, including where individuals are clustered within higher-level clusters. We illustrate implementation with replicable R and Stata code using a case study from the Childhood to Adolescence Transition Study.
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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.