Assessing covariates of adolescent delinquency trajectories: A latent growth mixture.
Not exact matches
To
assess the robustness of the results of our regression analysis, we performed
covariate adjustment with derived propensity scores to calculate the absolute risk difference (details are provided in the Supplementary Appendix, available with the full text of this article at NEJM.org).14, 15 To calculate the adjusted absolute risk difference, we used predictive margins and G - computation (i.e., regression - model — based outcome prediction in both exposure settings: planned in - hospital and planned out - of - hospital birth).16, 17 Finally, we conducted post hoc analyses to
assess associations between planned out - of - hospital birth and outcomes (cesarean delivery and a composite of perinatal morbidity and mortality), which were stratified according to parity, maternal age, maternal education, and risk level.
In order to
assess the impact of PMI on gene expression we took into account a set of fourteen
covariates (Supplementary Table 5).
Relations between serum CRP and dietary fiber were
assessed by using linear mixed models and logistic regression, adjusted for
covariates.
This study uses within - study comparisons to
assess the relative importance of
covariate choice, unreliability in the measurement of these
covariates, and whether regression or various forms of
The authors
assess how different
covariates contribute to improving the statistical power of a randomization design and examine differences between math and reading tests; differences between test types (curriculum - referenced tests versus norm - referenced tests); and differences between elementary school and secondary school, to see if the test subject, test type, or grade level makes a large difference in the crucial design parameters.
When data on
covariates had been collected at both time points (eg, SES or household adults), we used
covariates assessed at 9 months for the 9 - month ITSC predictor variable, and
covariates assessed at 2 years for the combined 9 - month + 2 - year ITSC variable.
Previous studies have indicated that the following
covariates, had been
assessed in the HBC Study, are associated with early infantile aggression (Alink et al., 2006; Hay, Mundy et al., 2011; Tremblay et al., 2004): (a) maternal age, (b) paternal age, (c) maternal years of education, (d) paternal years of education, (e) marital status of the mother during early pregnancy, (f) annual household income, (g) maternal history of depression and / or anxiety disorders, and (h) infant gender.
For all analyses examining hippocampus or amygdala volumes, children's total cortical brain volume (total white + total cortical gray) was included as a
covariate to
assess specificity.
Health - related behaviours, prior mental health / self - rated health and
covariates assessed from age 33 onwards did not attenuate associations of being second - generation Irish with poorer midlife health.
The site variables and all
covariates were simultaneously included in all models
assessing HS effects.
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 table S1.
To
assess the influence of widowhood within each sex, we considered the possible
covariates of depressive symptoms identified by the results of previous population - based studies, including age, education, income, experience with chronic diseases, disability, number of children, and social participation.
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.
Included in analyses as a
covariate, NMQ was
assessed by averaging responses to three questions: «How much do they criticize you?»
Analyses were conducted to
assess whether any demographic variables were related to the dependent variables (DVs), and hence should be included as
covariates in the mediation analysis.
Maternal depressive symptomatology, and the
covariates of family SES and teacher - rated peer preference were all
assessed during children's kindergarten year.