Sentences with phrase «multiple covariates»

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.

Not exact matches

Covariates included mother's age, education, smoking during pregnancy, and participation in the federal nutritional support program for Women, Infants, and Children; and infant's gender, race, birth weight, congenital malformation reported at birth, live birth order, and single or multiple birth.

We therefore adjusted for covariates and confounding variables with a backward multiple logistic regression - model as described in the Methods section (model 2).

A reduced multiple linear regression model was fit including both covariates.

Although latitude was also a strong predictor of N2O flux (p < 0.001, R2 = 0.47, n = 55), latitude and NO3 — were weak covariates -LRB--- 0.29 Pearson correlation), and latitude was not a significant predictor of N2O (p = 0.10) in a multiple linear regression model with NO3 (p = 0.01).

We used multiple imputation with the method of chained equations to account for missing maternal data for children with a mother in the household.24 In addition to the mother's BMI status (missing for 1085 [22 %] of the children), imputation was conducted for 4 maternal covariates with few (< 1 %) missing cases (education [n = 13], warmth [n = 47], control [n = 49], and irritability [n = 48]-RRB-.

In general, multiple imputation imputes missing scale values with regression - based maximal likelihood procedures but also incorporates random error into the estimates.26 To improve estimates, key demographic factors and the important covariates were used in the imputation strategy, and the procedures described by Allison27 for intervention studies were used.

All significant single environmental attributes and interaction terms were entered in multiple environmental attribute GAMMs adjusted for all covariates.

Controlling for endogenous covariates (including school quality) thus has the net result of denying the possibility that there are multiple pathways by which the neighborhood may influence developmental outcomes among children (22).

Because of substantial missing data on 2 direct parenting measures (29 %), multiple imputation via chained equations was used to handle missing covariate data.30 This approach uses regression models to predict missing data from available variables with 20 imputation iterations selected.

Table 4 displays the results of the parallel multiple mediation analyses using antipathy as the independent variable and with depressive symptoms included as a covariate.

Multiple - classification analysis and analysis of covariance were employed to examine the relationship between widowhood and social ties after adjusting for the other covariates.

Adolescent sex, age, time since diagnosis, and diabetes regimen (pump vs. multiple daily injections) were also included as covariates in all regression analyses because each was correlated with at least one outcome variable.

Although this effect may appear small, the finding is rather robust given that the effect occurs over a 4 - year time lag and the model controlled for Wave 1 negative marital quality as well as multiple demographic covariates (education, years married, race, and age).