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Moreover, in order to detect possible dependencies, a COX proportional hazard model with factors gender, age, compliance and drug formulation was calculated for the parameter 'time to resolution of symptoms'.
We used a multiple logistic regression model with factors that showed p<0.2 by univariate analysis.
To compare patients and controls, we used a mixed-effects linear model, with factors disease and reward.
a Estimated from ANCOVA model with factors for treatment and centre and a covariate for baseline throat soreness.
These data were used to fit a linear model with factors Gene, Array, Array × Gene, Dye, Dye × Gene, and Sample × Gene.
P-values were determined from univariate logistic regression or from the interaction term of a logistic regression model with factors treatment, subgroup and treatment times subgroup as appropriate.
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Fig 2 The Model (with factor loadings and beta coefficients).
We assume an exponential path-loss model with factor 3, but no shadow-fading.
In both samples, we simulated data according to a 1-factor model with factor loadings equal to.5.
We therefore decided to carry out an EFA in order to determine whether there is an optimal model with factor structure that better fits the Arabic data.
Gender equivalence of factor loadings was then tested by comparing the nested model (i.e., the model with factor loadings constrained across genders) to the base model (i.e., the model with factor loadings freely estimated across genders) using the nested-model chi-square difference test.
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