Browsing by Author "Heymsfield, Steven B."
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Item Errors in the implementation, analysis, and reporting of randomization within obesity and nutrition research: a guide to their avoidance(2021) Vorland, Colby J.; Brown, Andrew W.; Dawson, John A. (TTU); Dickinson, Stephanie L.; Golzarri-Arroyo, Lilian; Hannon, Bridget A.; Heo, Moonseong; Heymsfield, Steven B.; Jaywardene, Wasantha P.; Kahathuduwa, Chanaka N. (TTUHSC); Keith, Scott W.; Oakes, J. Michael; Tekwe, Carmen D.; Thabane, Lehana; Allison, David B.Randomization is an important tool used to establish causal inferences in studies designed to further our understanding of questions related to obesity and nutrition. To take advantage of the inferences afforded by randomization, scientific standards must be upheld during the planning, execution, analysis, and reporting of such studies. We discuss ten errors in randomized experiments from real-world examples from the literature and outline best practices for their avoidance. These ten errors include: representing nonrandom allocation as random, failing to adequately conceal allocation, not accounting for changing allocation ratios, replacing subjects in nonrandom ways, failing to account for non-independence, drawing inferences by comparing statistical significance from within-group comparisons instead of between-groups, pooling data and breaking the randomized design, failing to account for missing data, failing to report sufficient information to understand study methods, and failing to frame the causal question as testing the randomized assignment per se. We hope that these examples will aid researchers, reviewers, journal editors, and other readers to endeavor to a high standard of scientific rigor in randomized experiments within obesity and nutrition research.Item Publisher Correction: Total and regional appendicular skeletal muscle mass prediction from dual-energy X-ray absorptiometry body composition models (Scientific Reports, (2023), 13, 1, (2590), 10.1038/s41598-023-29827-y)(2023) McCarthy, Cassidy; Tinsley, Grant M. (TTU); Bosy-Westphal, Anja; Müller, Manfred J.; Shepherd, John; Gallagher, Dympna; Heymsfield, Steven B.Correction to: Scientific Reports, published online 14 February 2023 The original version of this Article contained errors. In Figures 2 and 3, the labels and text did not display correctly. The original Figures 2 and 3 and accompanying legends appear below. (Figure presented.) (Figure presented.) Predicted total, arm, and leg skeletal muscle (SM) mass versus corresponding value measured with MRI in the validation sample (n = 47 women; 48 men) on the left (A,C,E) and associated Bland–Altman plots on the right (B,D,F). The regression equations, lines, R2s, and 95% limits of agreement (LOA) are shown in the figures. The statistical significance of each panel is summarized in the text. Total skeletal muscle (SM) mass predicted by Kim’s equation8 versus SM measured with MRI at Kiel (A) and corresponding Bland–Altman plot (B) (n = 475). Total body skeletal muscle (SM) mass predicted by the newly developed Kiel equation versus SM measured with MRI by Kim et al.8 (C) and corresponding Bland–Altman plot (D) (n = 270). The lines of identity (thin solid line), regression equations and lines (solid lines with gray shading indicating 95% CI), and R2s are shown in (A,C). The regression lines with 95% CI and 95% limits of agreement (LOA) (dashed lines) are shown in (B,D). Statistical significance of each panel is summarized in the text. The original Article has been corrected.Item Total and regional appendicular skeletal muscle mass prediction from dual-energy X-ray absorptiometry body composition models(2023) McCarthy, Cassidy; Tinsley, Grant M. (TTU); Bosy-Westphal, Anja; Müller, Manfred J.; Shepherd, John; Gallagher, Dympna; Heymsfield, Steven B.Sarcopenia, sarcopenic obesity, frailty, and cachexia have in common skeletal muscle (SM) as a main component of their pathophysiology. The reference method for SM mass measurement is whole-body magnetic resonance imaging (MRI), although dual-energy X-ray absorptiometry (DXA) appendicular lean mass (ALM) serves as an affordable and practical SM surrogate. Empirical equations, developed on relatively small and diverse samples, are now used to predict total body SM from ALM and other covariates; prediction models for extremity SM mass are lacking. The aim of the current study was to develop and validate total body, arm, and leg SM mass prediction equations based on a large sample (N = 475) of adults evaluated with whole-body MRI and DXA for SM and ALM, respectively. Initial models were fit using ordinary least squares stepwise selection procedures; covariates beyond extremity lean mass made only small contributions to the final models that were developed using Deming regression. All three developed final models (total, arm, and leg) had high R2s (0.88–0.93; all p < 0.001) and small root-mean square errors (1.74, 0.41, and 0.95 kg) with no bias in the validation sample (N = 95). The new total body SM prediction model (SM = 1.12 × ALM – 0.63) showed good performance, with some bias, against previously reported DXA-ALM prediction models. These new total body and extremity SM prediction models, developed and validated in a large sample, afford an important and practical opportunity to evaluate SM mass in research and clinical settings.