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Table 1 details multivariable models using the principle of backward selection for biomarkers at baseline and when assessed longitudinally using PFS and OS as outcomes.
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.
Univariate analyses will be performed to determine which variables to include in the multivariable models using the criterion of alpha = 0.20.
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.
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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.
Cost estimates for intent, firearm type, and payer source were obtained from the multivariable model using the BYLEVEL option in conjunction with the LSMEANS statement.
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