Academic Journal
Bayesian Joint Modeling for Longitudinal Magnitude Data With Informative Dropout: An Application to Critical Care Data.
| Title: | Bayesian Joint Modeling for Longitudinal Magnitude Data With Informative Dropout: An Application to Critical Care Data. |
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| Authors: | Teng W; Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada., Ferguson ND; Department of Medicine, Division of Respirology, University Health Network, Toronto, Ontario, Canada., Goligher EC; Department of Medicine, Division of Respirology, University Health Network, Toronto, Ontario, Canada., Heath A; Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada.; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.; Department of Statistical Science, University College London, London, UK. |
| Source: | Biometrical journal. Biometrische Zeitschrift [Biom J] 2026 Aug; Vol. 68 (4), pp. e70156. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Wiley-VCH Verlag GmbH & Co. KGaA Country of Publication: Germany NLM ID: 7708048 Publication Model: Print Cited Medium: Internet ISSN: 1521-4036 (Electronic) Linking ISSN: 03233847 NLM ISO Abbreviation: Biom J Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2005-> : Weinheim : Wiley-VCH Verlag GmbH & Co. KGaA Original Publication: Weinheim, Wiley-VCH Verlag GmbH & Co. KGaA |
| MeSH Terms: | Biometry*/methods , Models, Statistical* , Critical Care* , Patient Dropouts*, Bayes Theorem ; Humans ; Longitudinal Studies |
| Abstract: | In various biomedical studies, analysis often focuses on data magnitudes, particularly when algebraic signs are irrelevant or lost. For repeated measures studies involving magnitude outcomes, incorporating random effects is essential as they account for individual heterogeneity, thereby enhancing parameter estimation precision. However, established regression methods specifically designed for magnitude outcomes that incorporate random effects are currently lacking. This article bridges this gap by introducing Bayesian regression modeling approaches for analyzing magnitude data, with a key focus on incorporating random effects. The proposed method is further extended to address multiple causes of informative dropout, a common challenge in repeated measures studies. To tackle this missing data challenge, a joint modeling strategy is developed, building upon the introduced regression techniques. Two numerical simulation studies assess the validity of our method. The chosen simulation scenarios are designed to resemble the conditions of our motivating study. Results demonstrate that the proposed method for magnitude data performs well in terms of estimation accuracy, and the joint models effectively mitigate bias due to missing data. Finally, we apply these models to analyze magnitude data from the motivating study, investigating whether sex impacts the magnitude change in diaphragm thickness over time for ICU patients. (© 2026 Wiley‐VCH GmbH.) |
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| Grant Information: | RGPIN-2021-03366 Natural Sciences and Engineering Research Council of Canada; Statistical Trial Design Canada Research Chairs |
| Contributed Indexing: | Keywords: Bayesian joint modeling; folded normal regression; informative dropout; magnitude data |
| Entry Date(s): | Date Created: 20260804 Date Completed: 20260804 Latest Revision: 20260813 |
| Update Code: | 20260814 |
| PubMed Central ID: | PMC13433986 |
| DOI: | 10.1002/bimj.70156 |
| PMID: | 42548054 |
| Database: | MEDLINE |
| ISSN: | 1521-4036 |
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| DOI: | 10.1002/bimj.70156 |