ArticleBiometrical journal. Biometrische Zeitschrift2019
Multiple imputation for discrete data: Evaluation of the joint latent normal model.
Article in Biometrical journal. Biometrische Zeitschrift, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review.BMC medical research methodology · 2024Pooled it
- Two-stage or not two-stage? That is the question for IPD meta-analysis projects.Research synthesis methods · 2023Pooled it
- The stroke transitional care intervention for older adults with stroke and multimorbidity: a multisite pragmatic randomized controlled trial.BMC geriatrics · 2023Trial
- Comparison of methods to handle missing values in a binary index test in a diagnostic accuracy study - a simulation study.BMC medical research methodology · 2026Article
- Multiple imputation for missing values in ordinal variables from cancer registry data when performing Cox proportional hazards regression.BMC medical research methodology · 2026Article
- Multiple imputation and pooling strategies for handling wide-format missing data in latent growth curve modeling.Frontiers in psychology · 2026Article
- A literature-based gut microbiota dietary index and its association with obstructive sleep apnea risk: a population-based, cross-sectional study.BMC nutrition · 2025Article
- Temporal Trends in Sexual Identity and Sociodemographic Disparities in Stockholm, Sweden, 2010-2021.American journal of public health · 2025Article
- Conceptual framework as a guide to choose appropriate imputation method for missing values in a clinical structured dataset.BMC medical research methodology · 2025Article
- Multiple Imputation for Longitudinal Data: A Tutorial.Statistics in medicine · 2025Article
- Reference-Based Multiple Imputation for Longitudinal Binary Data.Statistics in medicine · 2025Article
- A novel machine learning-based imputation strategy for missing data in step-stress accelerated degradation test.Heliyon · 2024Article
- Multiple imputation methods for missing multilevel ordinal outcomes.BMC medical research methodology · 2023Article
- How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion.Psychological methods · 2023Article
- Substantive model compatible multilevel multiple imputation: A joint modeling approach.Statistics in medicine · 2022Article
- Multiple imputation for cause-specific Cox models: Assessing methods for estimation and prediction.Statistical methods in medical research · 2022Article
- Longitudinal determinants of anal intercourse among women with, and without HIV in the United States.BMC women's health · 2022Article
- Assessing Alternative Imputation Strategies for Infrequently Missing Items on Multi-item Scales.Communications in statistics. Case studies, data analysis and applications · 2022Article
- Handling missing data in a composite outcome with partially observed components: simulation study based on clustered paediatric routine data.Journal of applied statistics · 2022Article
- Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach.Behavior research methods · 2021Article
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2 authors.
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Abstract
Missing data are ubiquitous in clinical and social research, and multiple imputation (MI) is increasingly the methodology of choice for practitioners. Two principal strategies for imputation have been proposed in the literature: joint modelling multiple imputation (JM-MI) and full conditional specification multiple imputation (FCS-MI). While JM-MI is arguably a preferable approach, because it involves specification of an explicit imputation model, FCS-MI is pragmatically appealing, because of its flexibility in handling different types of variables. JM-MI has developed from the multivariate normal model, and latent normal variables have been proposed as a natural way to extend this model to handle categorical variables. In this article, we evaluate the latent normal model through an extensive simulation study and an application on data from the German Breast Cancer Study Group, comparing the results with FCS-MI. We divide our investigation in four sections, focusing on (i) binary, (ii) categorical, (iii) ordinal, and (iv) count data. Using data simulated from both the latent normal model and the general location model, we find that in all but one extreme general location model setting JM-MI works very well, and sometimes outperforms FCS-MI. We conclude the latent normal model, implemented in the R package jomo, can be used with confidence by researchers, both for single and multilevel multiple imputation.
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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.