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Therefore, joint analysis for multiple potential predictors might be a potentially useful method for diabetes screening.
Consequently, many studies end up evaluating multiple potential predictors using data sets with relatively small numbers of cases.
Different analysis methods are available when analyzing data with multiple potential predictors, these include tree analysis, neural networks, and the more traditional approach of regression modeling.
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Serum concentrations for six major PBDE congeners, BDE-47, 99, 100, 153, 154 and 209, were evaluated in mixed-effects multiple regression models to identify potential predictors.
Third, all factors shown in tables 2 and 3 with a p-value < 0.1 (sexual risk behavior, sexual trauma, multiple sex partners and early maturity and drug use disorder, suicidality, and age) were incorporated as potential predictors in the multiple logistic regression analysis with pregnancy as the dependent variable.
To be able to study these potential predictors simultaneously a multiple regression model was adopted.
The differences between countries were confirmed by multiple regression while adjusting for other potential predictors.
Another approach has been to include gestational age in a multiple regression equation along with other potential predictors of birth weight.
Multiple logistic regressions were employed to identify potential predictors.
Multiple linear regression was used to evaluate potential predictors of age of diabetes diagnosis in subjects who had their first autoantibody measurement before 1.5 years (n = 38).
Multivariate logistic regression with the backward-elimination method was used for adjusting multiple selected covariates to assess the association between potential predictors and outcomes.
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