Gray, L.A. orcid.org/0000-0001-6365-7710 (2024) The importance of missing data in estimating BMI trajectories. Scientific Reports, 14 (1). 17740. ISSN 2045-2322
Abstract
Body Mass Index (BMI) trajectories are important for understanding how BMI develops over time. Missing data is often stated as a limitation in studies that analyse BMI over time and there is limited research exploring how missing data influences BMI trajectories. This study explores the influence missing data has in estimating BMI trajectories and the impact on subsequent analysis. This study uses data from the English Longitudinal Study of Ageing. Distinct BMI trajectories are estimated for adults aged 50 years and over. Next, multiple methods accounting for missing data are implemented and compared. Estimated trajectories are then used to predict the risk of developing type 2 diabetes mellitus (T2DM). Four distinct trajectories are identified using each of the missing data methods: stable overweight, elevated BMI, increasing BMI, and decreasing BMI. However, the likelihoods of individuals following the different trajectories differ between the different methods. The influence of BMI trajectory on T2DM is reduced after accounting for missing data. More work is needed to understand which methods for missing data are most reliable. When estimating BMI trajectories, missing data should be considered. The extent to which accounting for missing data influences cost-effectiveness analyses should be investigated.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | BMI; Growth mixture model; Longitudinal analysis; Missing data; Humans; Body Mass Index; Middle Aged; Diabetes Mellitus, Type 2; Female; Male; Longitudinal Studies; Aged; Overweight; Obesity |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
Funding Information: | Funder Grant number MEDICAL RESEARCH COUNCIL MR/R01664X/1/MR/S009868/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Aug 2024 16:05 |
Last Modified: | 05 Aug 2024 16:05 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s41598-024-68764-2 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:215717 |