ArticleObesity (Silver Spring, Md.)2019
Illustration of Measurement Error Models for Reducing Bias in Nutrition and Obesity Research Using 2-D Body Composition Data.
Article in Obesity (Silver Spring, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- Body shape matters: Evidence from machine learning on body shape-income relationship.PloS one · 2021Article
- Comparison of Nutritional Behaviors and Physical Activities between Overweight/Obese and Normal-Weight Adults.Advanced biomedical research · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
objectiveThis study aimed to illustrate the use and value of measurement error models for reducing bias when evaluating associations between body fat and having type 2 diabetes (T2D) or being physically active.
methodsLogistic regression models were used to evaluate T2D and physical activity among adults aged 19 to 80 years from the Photobody Study (n = 558). Self-reported T2D and physical activity were categorized as "yes" or "no." Body fat measured by two-dimensional photographs was adjusted for bias using dual-energy x-ray absorptiometry scans as a reference. Three approaches were applied: regression calibration (RC), simulation extrapolation (SIMEX), and multiple imputation (MI).
resultsUnadjusted two-dimensional measures of body fat had upward biases of 30% and 233% for physical activity and T2D, respectively. For the physical activity model, RC-adjusted values had a 13% upward bias, whereas MI and SIMEX decreased the bias to 9% and 91%, respectively. For the T2D model, MI reduced the bias to 0%, whereas RC and SIMEX increased the upward bias to > 300%.
conclusionsOf three statistical approaches to reducing bias due to measurement errors, MI performed best in comparison to RC and SIMEX. Measurement error methods can improve the reliability of analyses that look for relations between body fat measures and health outcomes.
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