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Table 2 presents bivariable associations between regression model predictors using Spearman's correlation coefficients, rank biserial correlation coefficients and phi coefficients.
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Two independent reviewers (GL and MJ) extracted the following article details using a data abstraction spreadsheet: purpose for making the prediction, study population, population size, methods used for building predictive models, predictors used, and the models' performance.
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).
Multivariable analysis was conducted using Cox's regression model with backward stepwise model selection of predictors using the Akaike Information Criterion [ 26].
Based on a logistic regression model, predictors for using the ELP were examined.
In the next example we illustrate how to fit models with various sets of predictors using the mice data set.
The LR model used all the predictors used in the BL model and six additional ones.
The effects of model predictors were measured using odds ratios (95% CI) and P values.
This type of ab initio gene predictors uses mathematical models to identify genes.
ab initio gene predictors use mathematical models to identify genes and their structures [ 10], without external evidences.
For each of the 2 outcomes, we developed a pretreatment and a post-treatment predictor model using the variables identified above.
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