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1 The results were essentially unchanged after statistical adjustment for potential confounding factors .
2 Information on smoking and potential confounding factors was collected by standardized interviews.
3 Multivariable linear regression analysis was performed to account for potential confounding factors .
4 All of the above associations remained significant after adjustment for confounding factors .
5 Multiple logistic regression analyses were performed to determine the potential confounding factors .
6 However, when confounding factors were controlled, only the negative factor remained lower significantly.
7 It contains three updated data sets on tobacco consumption and potential confounding factors .
8 We used Cox proportional hazard models adjusted for possible confounding factors .
9 Age, smoking status, physical activity, and sleeping hours were treated as confounding factors .
10 Propensity models were developed according to discharge destination, accounting for likely confounding factors .
11 The outcome of this treatment option depends on many confounding factors .
12 We used adjustment by propensity score to control for bias by confounding factors .
13 The difference remained statistically significant after correction for confounding factors by multivariate analysis.
14 Multivariable logistic models were used after adjusting for potentially confounding factors .
15 When correcting for potential confounding factors and multiple testing, these associations remained significant.
16 Maternal depression, breastfeeding rates and socio-economic factors were evaluated as potentially confounding factors .
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