Modeling energy balance while correcting for measurement error via free knot splines

Journal Article · Thu Aug 30 04:00:00 EDT 2018 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0201892· OSTI ID:1473959

Ries, Daniel ^[1]; Carriquiry, Alicia ^[2]; Shook, Robin ^[3]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Iowa State Univ., Ames, IA (United States)
2. Iowa State Univ., Ames, IA (United States)
3. Children's Mercy, Kansas City, MO (United States)

Measurements of energy balance components (energy intake, energy expenditure, changes in energy stores) are often plagued with measurement error. Doubly-labeled water can measure energy intake (EI) with negligible error, but is expensive and cumbersome. An alternative approach that is gaining popularity is to use the energy balance principle, by measuring energy expenditure (EE) and change in energy stores (ES) and then back-calculate EI. Gold standard methods for EE and ES exist and are known to give accurate measurements, albeit at a high cost. We propose a joint statistical model to assess the measurement error in cheaper, non-intrusive measures of EE and ES. We let the unknown true EE and ES for individuals be latent variables, and model them using a bivariate distribution. We try both a bivariate Normal as well as a Dirichlet Process Mixture Model, and compare the results via simulation. Our approach, is the first to account for the dependencies that exist in individuals’ daily EE and ES. We employ semiparametric regression with free knot splines for measurements with error, and linear components for error free covariates. We adopt a Bayesian approach to estimation and inference and use Reversible Jump Markov Chain Monte Carlo to generate draws from the posterior distribution. Based on the semiparameteric regression, we develop a calibration equation that adjusts a cheaper, less reliable estimate, closer to the true value. Along with this calibrated value, our method also gives credible intervals to assess uncertainty. A simulation study shows our calibration helps produce a more accurate estimate. Furthermore, our approach compares favorably in terms of prediction to other commonly used models.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1473959
• Report Number(s): SAND--2018-9850J; 667696
• Journal Information: PLoS ONE, Journal Name: PLoS ONE Journal Issue: 8 Vol. 13; ISSN 1932-6203
• Publisher: Public Library of Science
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES