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In all models the predictors we will consider include age at diagnosis, sex, geographical remoteness and area of socieconomic disadvantage, cancer type, disease spread at diagnosis and comorbidities.
In these models, the predictors (features) are the matching scores of promoter sequences to putative binding motifs, and the predictions (responses) can be continuous or discrete gene expression levels or categorical cluster labels.
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To analyze the goodness-of-fit of the species-specific volume models, the predictor variables were inserted systematically based on the earlier analyses (e.g., Fig. 2).
In all models, the predictor variable was the level of MPs or mpICs.
Prior to the modeling, the predictors based on the ALS data were evaluated with respect to their relationships with the species-specific volumes in a similar way than described in the previous section.
In each model the predictors were d2 at a single locus and a measure of d2 calculated across all remaining loci, omitting the locus under consideration.
To model the predictors of the time to first acute hospitalization for nursing home residents, and accounting for previous hospitalizations, model the predictors of time between subsequent hospitalizations.
Within the best fitting model, the predictors were examined by means of t-tests.
Univariate analysis and multivariable logistic regression were used for modeling the predictors of condom use.
Multivariable linear regression was used to model the predictors of 25(OH D concentration.
In a logistic regression analysis, we modelled the predictors of delayed treatment.
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