Sas inc

Manufactured by SAS Institute

The SAS Inc. lab equipment product is a versatile and reliable tool designed for use in various scientific and research settings. It offers core functionalities that enable users to conduct essential laboratory tasks with precision and efficiency. The product's detailed specifications and intended applications are not available for this response, as providing an unbiased and factual description while maintaining conciseness is the priority.

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Lab products found in correlation

Version 9, by SAS Institute (2 mentions)

2 protocols using sas inc

Longitudinal BMI Change Analysis

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BMI data from baseline and 1, 2 and 3 year follow-ups were used in random intercept, mixed effects growth curve analyses (SAS Inc. version 9.3) to model BMI change, which use maximum likelihood estimation to accommodate missing data (Singer et al., 1996 (link)). We examined empirical growth plots, fit an unconditional means model, fit an unconditional linear growth model and fit unconditional nonlinear models. Linear growth models consistently showed a better fit than higher order polynomials.

Stice E., Yokum S, & Voelker P. (2019). Relation of FTO to BOLD response to receipt and anticipated receipt of food and monetary reward, food images, and weight gain in healthy weight adolescents. Social Cognitive and Affective Neuroscience, 15(10), 1135-1144.

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Modeling BMI Changes Over Time

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BMI data from baseline, and 1-, 2-, and 3-year follow-ups were used in random intercept, mixed effects growth curve analyses (SAS Inc. version 9.3) to model BMI change. Following Singer and Willet (2003) , we: (1) examined empirical growth plots; (2) fit an unconditional means model; (3) fit an unconditional linear growth model; and (4) fit unconditional nonlinear models. We compared the latter two models using the Akaike information criterion (AIC) to determine whether linear or higher-order polynomial models fit the data better. AIC is a measure of goodness of fit relative to model complexity (Burnham & Anderson, 2002 ). Compared with higher-level polynomial models, linear growth models consistently showed a better fit per AIC values, suggesting that linear terms optimally captured change in body fat.

Stice E, & Yokum S. (2018). Relation of Neural Response to Palatable Food Tastes and Images to Future Weight Gain: Using Bootstrap Sampling to Examine Replicability of Neuroimaging Findings. NeuroImage, 183, 522-531.

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