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Multivariate logistic regression analyses were applied to control for probable confounders.
Bivariate analysis was used as a preliminary step in screening for the probable confounders.
After assessment of effect measure modification, probable confounders identified during crude analysis were also assessed.
Smoking status, age, and sex were included in all models as they were deemed a priori to be probable confounders.
The full contains the outcome variable (breastfeeding duration/initiation), the exposure variable (pregnancy intentions), all probable confounders and interaction terms.
To be more conservative in the of possible confounders, those with a p-value less than 0.25 in at least one category were considered as probable confounders [ 11].
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However, Bairati et al. suggested that socio-economic status was a probable confounder [ 10].
The probable confounder with the largest p-value was the first variable to be removed from the full model.
The difference in response to treatment protocols across the groups was assessed using a multivariable logistic regression model considering baseline data as probable independent confounders.
The possibility of residual confounding, however, cannot be ruled out and it is probable that these unknown confounders to a considerable degree have a biological basis [ 22, 26].
Finally, longitudinal analysis was completed using a generalized estimating equation model with maternal-infant bonding as a repeated outcome measure, adjusting for potential confounders maternal age, probable postpartum depression, and fertility treatment or advice.
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