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We ran multiple variable linear regression models with robust standard errors (α = 0.05).
Multiple variable linear regressions will be undertaken to determine statistically independent predictors of the threshold of delay time.
Multiple variable linear regression will be undertaken to explore determinants of the VTE threshold, as determined in the probability trade-off exercise.
Finally, we ran multiple variable linear regression models to generate a final explanatory model on the factors related to WBVs for Medicaid-enrolled infants.
Finally, we will perform a multiple variable linear regression with the threshold as the dependent variable and with utilities as independent variables.
Multiple variable linear regression was used to explore the relative strength and independence of individual question items as predictors of the various external measures of health state and to select items to include in subscores.
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To quantify these trends we applied a multiple-variable, linear regression model to LIP activity over a sliding temporal window as described in Methods.
The baseline association between mO-3FA intake and weight, BMI, waist circumference, A1C, fasting glucose, and lipids was examined using multiple-variable linear regression.
Multiple-variable linear regression will be used to compare the alternative mechanism TNFi and abatacept to rituximab with the dependent variable, the change in DAS28 at 6 months (24 weeks), and the minimisation factors (centre, disease duration (<5 years, ≥5 years), rheumatoid factor status, primary/secondary non-response) and the baseline value of DAS28 included as independent variables.
Multiple variable logistic and linear regression models examining the associations between key measures, adjusted for confounders identified a priori.
The thinking is linear, where multiple variables contribute differentially in measurable ways to determine a measurable outcome.
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