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Also, based on binary logistic regression analysis, predictors for hospital treatment were determined.
Multivariate analyses were based on binary logistic regression.
Multivariable analyses were based on binary logistic regression (forward conditional procedure).
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These simulations were based on binary outcome data.
In addition to the cost analysis, based on a binary logistic regression model, predictors for probability of inpatient hospital treatment were identified.
In this form, they have been used to derive a comparative statistical profile of the teachers groups and to further enhance an analysis of their differences based on multivariate binary logistic regression models.
Based on a binary logistic regression model [18], we assigned probability scores to all interactions using a training set of positive (interologs in DIP and IntAct) and negative Treponema interactions (see Discussion S1 for more details on the training data and scoring procedure).
Variables having P-value of less than 0.05 on binary logistic regression were the candidate for multiple logistic regressions.
Variables with a p-value of ≤ 0.2 on binary logistic regression analysis were entered and further computed on the multi-variate logistic regression model.
Multivariable analyses will be based on logistic regression for binary outcomes and linear regression for continuous outcomes.
In order to detect this difference with 80% power and 0.05 significance level, based on a logistic regression model of a binary outcome variable (skilled attendance at birth) on a binary independent variable (SES), a minimum sample size of 880 women will be necessary.
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