Sentences with phrase «of other covariates»

With the effects of other covariates and the classification effects subsumed in the intercept of the equations, the vertical distance between the regression lines represents the estimated mean difference at a given covariate value on the abscissa.

Not exact matches

Had the aim of this analysis been to identify characteristics associated with PPH, clearly these covariates would have been included (as would many of the maternities excluded from the analysis as described earlier), so it would not be appropriate to use these results to draw conclusions about the association between PPH and covariates other than intended place of birth.

While standardisation was performed for six principal covariates (gender, gestational age < 37 weeks, smoking in pregnancy, older siblings, maternal education, and maternal age at time of birth), there were several other potential covariates.

Confounding by other known dietary factors was unlikely because the risk estimates were influenced only slightly by adjustments for covariates, eg, the amount of gluten consumed.

The question of binge drinking patterns and mortality is far from solved, and there may be genetic differences or other covariates not yet discovered, which play a role and could explain the different empirical findings.»

These associations were significant after adjustment for a number of important covariates known to be associated with vascular health, including lifestyle factors, medication use, and other nutrients.

Third, although we adjusted for a comprehensive set of covariates in our multivariable models, the associations reported in our study may partially result from other unobserved confounding variables, from residual confounding, or by other dietary variables.55 However, use of the HEI provided a comprehensive assessment of overall dietary pattern and should have significantly reduced the confounding effect of other dietary variables.

The covariates included race / ethnicity (non-Hispanic white, non-Hispanic black, Mexican American, or other), educational attainment (< 12, 12, or > 12 years of education), smoking status (never, former, or current), alcohol consumption (0, < 3, or ≥ 3 drinks / wk), physical activity (0, < 5, or ≥ 5 times / wk of moderate - intensity to vigorous activities), BMI (< 25, 25 to < 30, or ≥ 30), family history of CVD (yes / no), and antihypertensive medication use (yes / no).

After controlling for caffeinated coffee and other covariates, compared with women with the lowest consumption of decaffeinated coffee (≤ 1 cup per week), the risk of depression was increased for higher consumption, with the exception of the very highest consumption category (≥ 2 cups per day).

After exclusion of participants with missing information on dietary data (n = 117; 70 case subjects, 47 subcohort) or other missing covariates, i.e., physical activity, educational, and smoking status (n = 790; 357 case subjects, 433 subcohort), and participants who fell in the top or bottom 1 % of the «energy intake / energy requirement ratio» (n = 619; 339 case subjects, 280 subcohort), our analysis included 26,253 participants (10,901 incident type 2 diabetes case subjects and a subcohort of 15,352 participants including 736 cases of incident type 2 diabetes).

We tested a baseline model with no covariates (i.e., only the five policy attributes and no other predictors), and then a full model, which included novice teacher and percentage of high - achieving students, low - achieving students, students with IEPs, and percentage of ELL students.

Leaving them within the analysis, however, does improve the precision of estimates for other covariates, which results in a more precise estimate of the treatment effect.

We propose a general method of moments technique to identify measurement error in self - reported and transcript - reported schooling using differences in wages, test scores, and other covariates to

It should be noted, however, that whether the use of students» pretest scores and other covariates can account or control for such inter - and intra-classroom variations is still being debated and remains highly uncertain (Ballou, Sanders, & Wright, 2004; Capitol Hill Briefing, 2011; Koedel & Betts, 2010; Kupermintz, 2003; McCaffrey, Lockwood, Koretz, Louis, & Hamilton, 2004; J. Rothstein, 2009; Tekwe et al., 2004).

The general pattern showing large jurisdictional differences after controlling for the covariates is consistent across each of the five developmental domains with children in Queensland and the ACT showing higher vulnerability compared to children living in the other jurisdictions.

Other strengths of our analysis include its large nationally representative and diverse sample, as well as the rich availability of covariates for inclusion in multivariable models.

A covariate was included in the multivariate analyses if theoretical or empirical evidence supported its role as a risk factor for obesity, if it was a significant predictor of obesity in univariate regression models, or if including it in the full multivariate model led to a 5 % or greater change in the OR.48 Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h / d).

Covariates were those identified in previous analyses of this sample to independently contribute to child BMI status.16 Parent - reported child variables were gender (male or female), number of siblings in the household (0, 1, 2, or ≥ 3), and language other than English spoken at home by the child (yes or no).

After adjustment for the covariates and for each other, there was no evidence for an association among any of the 3 maternal parenting dimensions and child BMI status (all P values were ≥.69).

Even after controlling for parental monitoring of nonmedia - related behaviors and other covariates, children were at lower risk of smoking and drinking if their parents prohibited them from watching R - rated movies.

In multivariate analyses, after adjusting for other covariates, the odds of poorer school performance increased with increasing weekday television screen time and cable movie channel availability and decreased with parental restriction of television content restriction.

However, the association remained significant after controlling for these behaviors as well as a history of psychological problems, use of psychotropic medications, current depressive symptoms, and other covariates.

The analyses also included age, race / ethnicity (three binary variables for Black, Hispanic and other ethnicity, coded with Whites as the reference group), gender, household income and parental education, media - viewing habits — hours watching television on a school day and how often the participant viewed movies together with his / her parents — and receptivity to alcohol marketing (based on whether or not the adolescent owned alcohol - branded merchandise at waves 2 — 4).31 Family predictors included perceived inhome availability of alcohol, subject - reported parental alcohol use (assessed at the 16 M survey and assumed to be invariant) and perceptions of authoritative parenting (α = 0.80).32 Other covariates included school performance, extracurricular participation, number of friends who used alcohol, weekly spending money, sensation seeking (4 - wave Cronbach's α range = 0.57 — 0.62) 33 and rebelliousness (0.71 — 0.76).34 All survey items are listed in tablother ethnicity, coded with Whites as the reference group), gender, household income and parental education, media - viewing habits — hours watching television on a school day and how often the participant viewed movies together with his / her parents — and receptivity to alcohol marketing (based on whether or not the adolescent owned alcohol - branded merchandise at waves 2 — 4).31 Family predictors included perceived inhome availability of alcohol, subject - reported parental alcohol use (assessed at the 16 M survey and assumed to be invariant) and perceptions of authoritative parenting (α = 0.80).32 Other covariates included school performance, extracurricular participation, number of friends who used alcohol, weekly spending money, sensation seeking (4 - wave Cronbach's α range = 0.57 — 0.62) 33 and rebelliousness (0.71 — 0.76).34 All survey items are listed in tablOther covariates included school performance, extracurricular participation, number of friends who used alcohol, weekly spending money, sensation seeking (4 - wave Cronbach's α range = 0.57 — 0.62) 33 and rebelliousness (0.71 — 0.76).34 All survey items are listed in table S1.

Thus, varying levels of child or adult responsibility for the drawings did not confound other effects tested in this study and did not need to be included as a covariate in further analyses.

Others have also consistently reported that breastfed children score slightly higher than those bottlefed on the Bayley Scales of Infant Development or later tests of IQ, such as the McCarthy Scales, after controlling for standard covariates including socioeconomic status (SES), maternal age and education, maternal smoking and drinking, 16, 17 and in one study maternal psychological state.18 Longitudinal studies indicate that these differences persist to 5 years and into school age.

bMultiple linear regression adjusted for other covariates (age, number of children, education, equivalent household income IADL disability, number of chronic disease, economic activity, and number of social activities).

aMultinomial logistic regression adjusted for other covariates (age, education, equivalent household income IADL disability, number of chronic disease, number of children, economic activity, and number of social activities).

aPercentage of social ties was adjusted for other covariates (age, education, equivalent household income IADL disability, number of chronic disease, number of children, economic activity, and number of social activities).

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

In these analyses, the CU raw score was considered as the independent variable and covariates were also SES, children's sex and ethnicity, comorbidities other than ODD and the number of DSM - IV CD symptoms.

Linear and logistic regression models were used to determine if 6 types of adverse experiences including physical abuse, sexual abuse by family and / or other persons, witnessing abuse, and household dysfunction caused by family alcohol and / or drug use were significantly associated with risk of adolescent violence perpetration after adjustment for demographic covariates.

Table 2 contains the GLM and logistic regressions assessing the contribution of the independent variables, CU levels, and the presence / absence of ODD on the children's psychological measures for the total sample (n = 622), adjusted by the covariates family SES, children's ethnicity and sex, other comorbid disorder different from ODD and the number of DSM - IV CD symptoms.

The current study examines the effects of PMQ on health net of NMQ and these other covariates.

For this modeling, the measures of CU (ICU - total raw score) and ODD (binary diagnosis present / absent) were considered as the independent variables and the analyses were adjusted by the covariates family SES, children's sex and ethnicity, presence of comorbidities other than ODD and the number of DSM - IV CD symptoms.