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A logistic regression model was built using a backward selection algorithm and SNPs nominally associated with nephropathy in our population.
The main effect model was built using a backward stepwise elimination technique.
Multivariate models were built using a backward stepwise selection of variables to minimize the Akaike Information Criterion.
An initial multivariable model included all potential confounders, and final multivariable model was built using a backward selection method with 10% change-in-estimate criteria.
Three predictive models were built using a backward stepwise elimination approach and all correlated variables were built into the models as interaction terms.
The models were built using a backward manual procedure performed on a maximal model including the type of units (forced variable) and all factors that were associated with mortality with p < 0.05 in univariate logistic regression.
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A multivariate Poisson regression model, with robust standard errors [ 31], was built using a stepwise backward selection procedure with a threshold p-value of 0.15 to explore the relationships between potential explanatory variables and life satisfaction three months after injury.
The final adjusted models were built using a stepwise backward reduction algorithm that retained all significant variables (P < 0.10) (Table 1) while always keeping age, sex, race/ethnicity, education, and BMI in the model.
The final multivariate model was built using a manual backward-elimination approach.
Multivariate logistic regression models were built using backward selection with a p value of 0.05 to stay in the model.
Models were built using manual backward elimination.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com