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Nonlinear P-Splines were used to check for linearity of Lp(a) in both linear and logistic regression analyses.
LASSO is a shrinkage method proposed by Tibshirani [31], which is applicable to both linear and logistic regression models; ENet is a widely used variation of LASSO proposed by Zou and Hastie [32].
Weighted regression analysis, both linear and logistic was performed using the same Horvitz-Thompson weights.
Change over time was analysed using a multilevel mixed-effects model for both linear and logistic regressions.
Both linear and logistic regression models provided similar results.
We examined the association of neighborhood deprivation and metabolic syndrome using both linear and logistic regression.
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Climatic and environmental data obtained from RS and GIS and individual animal characteristics and farm management data obtained from a questionnaire were analyzed in relation to N. caninum seropositivity and antibody titres both by linear and logistic regression models.
Results were robust in that the same patient variables emerged as independent correlates in both linear and multinomial logistic regression models.
BMI was also log-transformed (base 1.1) to pull in the long right tail, and hereby obtain a better model fit in regard to the underlying assumptions of linearity in both the linear and logistic regression models [23].
Both the likelihood ratio test and the Akaike information criterion (AIC) showed that an additive model of FTO rs9939609 gave the best model fit in both the linear and logistic regression models as compared to dominant and recessive models.
Both multiple linear and logistic models were preceded by univariate and bivariate analyses.
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