ArticleStatistical methods in medical research2019
Bayesian imputation of time-varying covariates in linear mixed models.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Trial
- Bayesian multivariate linear mixed-effects models with varied association structures.Statistical methods in medical research · 2026Article
- Adapting tree-based multiple imputation methods for multilevel data? A simulation study.Behavior research methods · 2026Article
- Multiple imputation of multilevel data with single-level models: A fully conditional specification approach using adjusted group means.Behavior research methods · 2026Article
- Maximum Likelihood and Bayesian Estimation in Cross-Domain Latent Growth Curve Modeling: The Impact of Reliability, Sample Size, and Missing Data.Structural equation modeling : a multidisciplinary journal · 2026Article
- Health inequality in the disease burden of non-communicable diseases among the elderly from 1990 to 2021, and projections to 2050: a systematic analysis of global burden of disease study.BMC geriatrics · 2025Article
- Bias and Efficiency Comparison between Multiple Imputation and Available-Case Analysis for Missing Data in Longitudinal Models.Statistics in biosciences · 2025Article
- Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials.Statistical methods in medical research · 2024Article
- Comparing DIC and WAIC for multilevel models with missing data.Behavior research methods · 2024Article
- Article
- Impact of timing of primary ileocecal resection on prognosis in patients with Crohn's disease.BJS open · 2023Article
- A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis.BMC medical research methodology · 2023Article
- Methods for comparative effectiveness based on time to confirmed disability progression with irregular observations in multiple sclerosis.Statistical methods in medical research · 2023Article
- Noninterventional studies in the COVID-19 era: methodological considerations for study design and analysis.Journal of clinical epidemiology · 2023Article
- Evaluation of approaches for accommodating interactions and non-linear terms in multiple imputation of incomplete three-level data.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Unveiling mechanisms of change in digital interventions for depression: Study protocol for a systematic review and individual participant data meta-analysis.Frontiers in psychiatry · 2022Article
- Article
- Recurrence of primary sclerosing cholangitis after liver transplantation - analysing the European Liver Transplant Registry and beyond.Transplant international : official journal of the European Society for Organ Transplantation · 2021Article
- Using the Movember Foundation's GAP3 cohort to measure the effect of active surveillance on patient-reported urinary and sexual function-a retrospective study in low-risk prostate cancer patients.Translational andrology and urology · 2021Article
- Tracking Study on the Relapse and Aftercare Effect of Drug Patients Released From a Compulsory Isolated Detoxification Center.Frontiers in psychiatry · 2021Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Studies involving large observational datasets commonly face the challenge of dealing with multiple missing values. The most popular approach to overcome this challenge, multiple imputation using chained equations, however, has been shown to be sub-optimal in complex settings, specifically in settings with longitudinal outcomes, which cannot be easily and adequately included in the imputation models. Bayesian methods avoid this difficulty by specification of a joint distribution and thus offer an alternative. A popular choice for that joint distribution is the multivariate normal distribution. In more complicated settings, as in our two motivating examples that involve time-varying covariates, additional issues require consideration: the endo- or exogeneity of the covariate and its functional relation with the outcome. In such situations, the implied assumptions of standard methods may be violated, resulting in bias. In this work, we extend and study a more flexible, Bayesian alternative to the multivariate normal approach, to better handle complex incomplete longitudinal data. We discuss and compare assumptions of the two Bayesian approaches about the endo- or exogeneity of the covariates and the functional form of the association with the outcome, and illustrate and evaluate consequences of violations of those assumptions using simulation studies and two real data examples.
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