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Furthermore, in order to validate the performance of the non linear model, a multiple regression model with all two-way interactions between variables significantly correlated with EWL, was computed.
To address the problem of missing data in the BWHHS covariates that were adjusted for in the Cox regression model, a multiple imputation procedure provided unbiased estimates of the parameters and their standard errors in the model.
In a similar manner to the per subject random-effect model, a multiple groups comparison model can be created by adding a fourth level to the model and so on.
To assess the importance of different factors in an adjusted model, a multiple linear regression was performed to predict NFL from sex, age, and AD biomarker profile (dichotomized as positive or negative).
III) Final model: A multiple binary logistic regression analysis was computed with the success criterion as the dependent variable and the predictor variables that were not associated with each other more than r = .500 and that at least showed a trend toward being significantly associated with therapy success in the univariate analysis.
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In [14], a two-dimensional discrete-time Markov chain is used to model a multiple-channel CR system with imperfect sensing.
In all those models a multiple hidden layer architecture has been used.
In all such models, a multiple hidden-layer architecture has been used.
For both models, a multiple regression approach was taken to predict Hm from age, Site Index, environmental data and RapidEye spectra and indices.
2) Multiple logistic regression models A multiple logistic regression model was formulated stratifying by sex and assuming the presence or absence of dementia as a dependent variable.
To describe such relationship, we modeled a multiple linear regression equation where the SSI was associated to the different topological metrics of the initial networks.
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