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Definitions for high-risk variables were determined based on a combination of one or more factors, including validated International Classification of Diseases, 9th Revision (ICD-9) codes (8), laboratory data, and clinical information, with detailed criteria for each high-risk factor listed previously (6).
The ranges of variables were determined based on pre-tests.
Probability distribution functions of input variables were determined based on field-measured data obtained under alternative tillage treatments.
Cut-points of variables were determined based on receiver-operating characteristic (ROC) curves.
Acceptable levels of quality-related variables were determined based on empirical observation of samples with relationship and DNA quality issues.
These independent variables were determined based on the results of the Pearson product moment coefficient analyses (i.e., alpha level ≤ 0.05) and assumed biological relevance, such as MMSE and waist girth were entered into the model regardless of the results of the correlation analyses.
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In this work, under the constraint that the total experimental cost does not exceed a pre-specified budget, the optimal decision variables are determined based on C/D/A-optimality criteria.
The perceived relative contribution of each variable was determined based on ADA diagnostic criteria, [ 5] existing diabetes case-finding algorithms, [ 10] and expert opinion.
Variable levels were determined based on both stoichiometric studies and literature reports to ensure that the design points fell within the design space.
Candidate variables for multiple regression analysis were determined based on significance of factors related to GH peak determined by univariate analysis as shown in Table 2. Fasting glucose, fasting insulin, BMI, triglycerides and sex explained 54% (R = 0·5379) of the variation in GH peak (Table 3).
The variables that best defined the estimated model were determined based on the coefficient of determination (R2), adjusted R2, chi-square value, the direction of influence of the independent variables, as well as the number of significant variables in the model.
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