Sentences with phrase «multivariable logistic»

In order to assess the unique contribution of the level of relationship satisfaction, multivariable logistic regression analyses were performed with the following independent control variables: stressful life events, maternal age, level of education, income, marital status, social support, breastfeeding, smoking during pregnancy, maternal depression and the sex of the offspring.

In the final analysis, two multivariable logistic models were fitted based on the study hypotheses.

The predictors that were moderately associated (crude associations) with AD (p < 0.25) in step one and, as recommended [86], those that have previously shown to be associated with AD in other studies were selected and included in the multivariable logistic regression model.

The contribution of adversity variables over the life - course in mediating excess risks of common mental disorders and poorer self - rated health at midlife was assessed.31 To assess mediation, three criteria needed to be fulfilled.31 First, the association of parental migration history with putative mediator was assessed using multivariable logistic regression.31 Second, the association of the putative mediator with the outcome variable (poorer self - rated health and common mental disorders at midlife) was assessed using multivariable logistic regression.31 Finally, the association of parental migration history with outcome --(either midlife common mental disorders or poorer self - rated health at midlife) was assessed in the presence of the putative mediator.31 If the coefficient for the association between parental migration history and outcome was reduced in the presence of the putative mediator, then it was presumed that the data were consistent with mediation.31

Adverse behavioral outcomes were defined by top 10th percentiles on Strengths and Difficulties Questionnaire total and subscales, at 4 and 7 years, in multivariable logistic regression models.

aChild Behavior Checklist for 4 - 18 years; bChildren who are currently visiting their father who used to perpetrate intimate partner violence and already separated from their mothers; cInternalizing problems = Withdrawn + Somatic complaints + Anxious / depressed; dExternalizing problems = Delinquent behavior + Aggressive behavior; Total problems = the sum of the scores of all the nine subscales of the CBCL; eAdjusted odds ratios calculated by multivariable logistic regression analysis; fThe dependent variable: 0 = non - clinical, 1 = clinical; gp values calculated by multivariable logistic regression analysis; hStandardized regression coefficients calculated by multivariable regression analysis; ip values calculated by multivariable regression analysis; jVariance Inflation Factor; k0 = non-visiting, 1 = visiting; lThe score of the subscale (anxiety) of the Hospital Anxiety and Depression Scale; mThe score of the subscale (depression) of the Hospital Anxiety and Depression Scale; nThe number of years the child lived with the father in the past; oAdjusted R2 calculated by multivariable regression analysis.

Other risk factors were assessed and adjusted for by multivariable logistic regression analyses.

Multivariable logistic regression was used to develop a model for assessment of risk factors.

The longitudinal relation between dietary variables and incident depression 3 y later was examined by using multivariable logistic regression to calculate ORs adjusted using the energy partition (22)(model 1).

A multivariable logistic regression analysis was conducted to evaluate factors independently associated with the prescription of other psychotropic drug classes among patients already using antipsychotics.

We examined independent associations by using multivariable logistic regression analysis.

In the analysis, we considered multiple covariates that may confound the association between early breastfeeding experience and postpartum depression based on the published literature and included these covariates in our multivariable logistic regression models.

These covariates were included as confounders in the multivariable logistic regression models.

We used multivariable logistic - regression models to adjust for potential confounders, including maternal race or ethnic group (non-Hispanic white vs. other), parity (nulliparous vs. multiparous), insurance status (public or none vs. other), extent of prenatal care (≥ 5 visits vs. < 5 visits), advanced maternal age (≥ 35 years vs. < 35 years), maternal education (> 12 years vs. ≤ 12 years), history or no history of cesarean delivery, and a composite marker of conditions that confer increased medical risk.

Not exact matches

A multivariable random effects logistic regression model was used to identify risk factors significantly associated with seropositivity while accounting for clinic - to - clinic (or shelter) variability.

To examine associations between child self - regulation (independent variable) and media use (dependent variable), we built multivariable linear and logistic regression models weighted to account for the complex sampling design of the ECLS - B.

Univariate and multivariable ordinal logistic regression (using the proportional odds model) were used to assess associations between the parenting variables and the odds of higher BMI status in the child (nonoverweight, overweight, or obese).

Binary logistic regression was employed for multivariable analysis, as the dependent variable was dichotomous.