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To fully understand the impact on public measures, we created our cohorts and assessed outcome measures according to the current measurement specifications of the Minnesota State Quality Measures (as specified by MNCM).
To create comparable measures, we created an indicator variable for waves 1 and 2 for whether the respondent reported a headache "almost every day" or "every day".
To assess potential selection bias related to the exclusion of the large number of Pelotas and Bt20 participants with missing 12-mo weight measures, we created a 24-mo CW variable conditional only on BW.
Because of the presence of a ceiling effect at the highest MMSE score (MMSE score = 29), which was observed in 13.06% of our measures, we created a dichotomized variable for inferior MMSE performance.
To examine the independent associations of baseline demographics with patient-reported outcome measures, we created separate multivariable linear regression models for RMDQ and back pain scores, adjusting for all available baseline demographic variables as well as recruitment site.
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In the present study, we did not find sufficient evidence for test retest reliability for the relational thinking measure we created and used.
As a heuristic measure, we created facial size ratios by pairing the smallest value for a given facial variable with the smallest value for a given postcranial variable, and by pairing the largest facial dimension with the largest postcranial dimension for each variable (see Supplementary Data S4).
As the primary outcome measure, we created a dummy variable indicating whether an employee enrolled in the subsequent weight loss program or not.
§Because of skewed nature of this outcome measure, we created a dichotomous variable of those who were very satisfied (score 4) and those who were less than very satisfied (scores 0 3).
‡Because of skewed nature of this outcome measure, we created a dichotomous variable of those who displayed no enablement (score 0) and those who displayed some enablement (scores 1 6).
In addition, in order to form a baseline measure, we created random gene sets as surrogate pathways by keeping KEGG pathway designations but doing a full permutation all genes (i.e. replacing each gene in a pathway by a randomly selected gene).
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