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Based on the test, the model was re-run by excluding each predictor, then a model was created using each variable in isolation.
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Sensitivity analyses were also performed by excluding each study individually.
Because of the concern about endogeneity when including reported prenatal care adherence as a predictor, we tested the model for sensitivity by excluding this predictor, and did not find significant changes in the odds for other predictors.
Ridge regression tends to achieve better prediction performance through a bias-variance tradeoff among all the variables, while LASSO specifically enforces sparseness by excluding poor predictor variables, and elastic net combines the two techniques.
Following guidelines on validation of prediction models, we wanted to refrain from simplification of the models by excluding some predictors and then refitting the new model on our data, as that would lead to the development of even more models.
The model will be reduced by excluding predictors from the model with a p-value of > 0.05.
Following univariate screening all predictors that were significant at p < 0.25 were entered into a multivariable model which was subsequently reduced by excluding the least significant predictors in turn until only those which were significant at p < 0.05 remained.
Plot of sensitivity analysis by excluding one study each time and the pooling estimate for the rest of the studies.
These two possibilities do by no means exclude each other.
The stepwise regression procedure aimed to produce a parsimonious model, explaining the dependent variable by including or excluding predictor variables stepwise.
To assess the effect of each predictor variable on model outputs, we excluded each variable in turn during calculations.
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