The use of weight adjusted for height rather than body mass index to assess growth trajectory: Results from a population ‐based cohort

We compared different growth models parameterizations regarding (i) adjustment of weight ‐for‐height, as denoted by body mass index (BMI); (ii) adjustment for different covariates, ie, age or height; and (iii) the use of different smoothing methods, ie, polynomial, fractional polynomial, or linear splines. A total of 11 459 measurements of weight and height from 719 participants w ere used, obtained from the EPITeen cohort at 13, 17, and 21 years, and extracted from child health books. The individual growth curves were modeled using mixed‐effects polynomial, fractional polynomial, and linear splines, and each model parameterization included as covariate age or height. The goodness‐of‐fit of the model parametrizations was compared using the relative squared error (RSE) and the relative absolute error (RAE). The adjustment of weight‐for‐height as denoted by BMI was found to be biased, especially for extreme values of height and presented the worst fit indexes f rom all model parameterizations tested (RSE = 12.46%; RAE = 22.63%). Regardless of the smoothing method, the weight‐for‐height retrieved the best fit indexes in comparison to the adjustment for age. With regard to the smoothing methods and comparing weight‐for‐height model parameterizati ons, the fractional polynomial model performed better (RSE = 0.75%; RAE = 5.70%), followed by linear splines (RSE = 0.77%; RAE = 5.82%), and conventional polynomial (RSE = 0.91%; RAE = 6.82%). The...
Source: Statistics in Medicine - Category: Statistics Authors: Tags: RESEARCH ARTICLE Source Type: research