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Integral controllability (IC) is a desired property of multivariable models used in robust controller design.
Multivariable models used logistic regression and were run in Stata's "svy" mode to account for weights.
Other studies have shown that a low eGFR and end-stage renal disease are associated with diabetes-related LEA [ 10, 46], but albuminuria was not available for inclusion as an independent variable in the multivariable models used.
More male participants were seropositive compared to females (in all three age classes) (OR = 1.96; p LRT) < 0.001) (Table 3).> -wrap-foot> The uni- and multivariable models used a random effect on village level.
Multivariable models used a backward stepwise model-building technique to arrive at a parsimonious model, in which all variables were included in the complete model with backward stepwise elimination of the least significant variable, using p < 0.05 as the cut point.
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We developed multivariable models using a difference-in-difference parameterisation.
Variables with a p-value <0.10 from the univariable analysis were then tested in multivariable models using forward stepwise procedures.
Nonetheless, similar results were obtained in multivariable models using the OGTT measurements in the subgroup from the 2005 2006 NHANES.
To estimate how much each additional level of data contributed to the model, we developed a succession of multivariable models using increasing numbers of variables.
For all multivariable models using ordinary least squares (OLS) linear regression, all models were assessed for violation of the fundamental assumptions.
We evaluated the resulting multivariable models using the area under the receiver operating characteristic curves (AUC) and evaluated the goodness-of-fit using the Hosmer-Lemeshow test.
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