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In a logistic regression analysis, we modelled the predictors of delayed treatment.
To allow for a non-linear relationship between the predictors and the log hazard, we modelled the predictors using restricted cubic splines with knots at the 10th, 50th and 90th percentiles (Harrell, 2001).
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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.
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.
When using AMMs to estimate differences for the population mean, we modeled the same predictors as for AQMMs.
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.
Within the best fitting model, the predictors were examined by means of t-tests.
In a separate hierarchical model, the predictors of non-participation will be studied in each program.
In this model, the predictors are the inferred activities of 4 PWMs that are most associated with patient survival.
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