Sentences with phrase «with covariates»

Changes in likelihood are based on comparison with a covariates - only model (− 2 log likelihood = 8,037.2).

Results are shown for the models with covariates.

ANOVA of IH score by the relative frequency of condom use with non-steady partners, with covariates, the number of non-steady partners that they had unprotected anal intercourse within the past year and perceived control over sexual risk - taking, demonstrated a small - to - moderate effect size (η2 = 0.03).

With these covariates in the final model, behavior problems were independently associated with concurrent child overweight (adjusted odds ratio: 2.95; 95 % confidence interval: 1.34 — 6.49).

Thus, what are purported to be temperature reconstructions are contaminated with covariates that reflect temperature indirectly at best and not at all at worst.

Figure 3 shows the same coefficients from the race / ethnicity variables, with covariates included, when predicting participation in special education (these were in the second bar for each group in Figure 2 and now are in the first bar for each group in Figure 3).

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.

Not exact matches

Greater intakes of total sugars, added sugars and sugar - sweetened beverages, but not of sugar - sweetened solid foods, were significantly associated with lower MMSE score, after adjusting for covariates.

We investigated substantial heterogeneity for all four outcomes with subgroup analyses for the following covariates: who delivered care, type of support, timing of support, background breastfeeding rate and number of postnatal contacts.

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.

Some potential covariates were excluded from the modelling despite being associated with PPH: mode of delivery, type of health professional attending delivery, type of pain relief used in labour and augmentation of labour.

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.

SMMIS allowed us to control for nearly all of the known risk factors for PPH, many of which were also associated with intended place of birth and would therefore almost certainly have caused problems of confounding had they not been included as model covariates.

Multivariate analyses were performed with logistic regression for outcome variables with paternal depression and other covariates as predictors.

All sociodemographic covariates were ascertained from the national opinion panel data with the exception of educational level and marital status, which were ascertained from the prenatal questionnaire.

Labour took place in a comprehensive room with constant care, with early skin - to - skin contact of at least 1 h and encouragement of early initiation of breastfeeding (positive covariates for EBF).

Compared with women who breastfed their first child for ≥ 12 months, women who did not breastfeed were more likely to develop hypertension (hazard ratio (HR) = 1.27, 95 % confidence interval (CI): 1.18, 1.36), adjusting for family history and lifestyle covariates.

After adjustment for relevant covariates and markers of inflammation, findings persisted in those with RA and not in the controls.

With the full set of covariates in Model 5, the award probabilities for applications from blacks were 10.4 percentage points lower, and for Asians were 4.2 percentage points lower, than for whites (P <.001).

Statistical group analyses were based on a factorial repeated ANOVA SPM8 model with two groups (marijuana abusers and controls) and two conditions (placebo and MP) and a covariate [parts per million levels of carbon monoxide (CO), a marker for tobacco smoking (40)-RSB-.

The fixed effects of the proportion of rainbow trout admixture, sex, fish length (covariates) and spawning year (random effect) on reproductive success (response variable) were evaluated using generalized linear mixed models (GLMMs) using a natural log - link function with a quasi-Poisson error distribution (see the electronic supplementary material).

On average we found only 54 genes per tissue (0.2 %), which showed significant correlation of gene expression with PMI (FDR < 1 %)(Fig. 3a, Supplementary Table 6), compared to 6919 genes per tissue (39.3 %), if using the same model without covariates.

The most parsimonious geostatistical model was fitted with the seven covariates and then used to predict podoconiosis prevalence for unsurveyed areas (Figure 2).

When the adiposity categories were adjusted for the same set of covariates (Table 6), individuals with abdominal obesity had a higher mortality risk (HR, 1.25; 95 % CI, 1.00 - 1.56; P =.05), although this relationship did not persist after further adjustment for fitness (HR, 0.99; 95 % CI, 0.79 - 1.25; P =.95).

After controlling for maternal and child covariates, we found that maternal influenza infection anytime during pregnancy was not associated with increased ASD risk (adjusted HR [AHR], 1.04 [95 % CI, 0.68 - 1.58]-RRB-.

After adjusting for covariates, we found that maternal influenza infection (adjusted hazard ratio, 1.04; 95 % CI, 0.68 - 1.58) or influenza vaccination (adjusted hazard ratio, 1.10; 95 % CI, 1.00 - 1.21) anytime during pregnancy was not associated with increased ASD risk.

For studies that reported incidence in each age category, we fitted log - linear model that contained incidence (dependent variable) and consumption (independent variable) with age as a covariate (median age in each age category), and we estimated the relative risk by using an interaction term between age and consumption.

After adjustment for covariates, maternal influenza vaccination anytime during pregnancy was not significantly associated with increased ASD risk (AHR, 1.10 [95 % CI, 1.00 - 1.21]-RRB-(Table 3).

After adjustment for covariates, first trimester influenza vaccination was associated with an increased risk of ASD (AHR, 1.20 [95 % CI, 1.04 - 1.39], P =.01); however, adjusting for the multiplicity of hypotheses tested (n = 8) using the Bonferroni correction suggests that this association could be due to chance (P =.10).

Additional covariate information about age, smoking habit, socioeconomic status, family history of diabetes and stroke, menopausal status, medical history, and current use of medications was obtained with the use of pretested questionnaires.

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.

Voxel-wise regressions identified focal areas within the prefrontal cortex where volume was positively correlated with Factor 2 scores (covariates: age, race, substance abuse severity, brain volume, and Factor 1 score)(Figure 2).

To address the possibility of residual confounding, we further adjusted for a propensity score that reflected associations of protein consumption with potential confounding covariates.17 Details about covariate assessment and propensity score analysis are provided in the eMethods in the Supplement.

In the analysis including all NHANES III participants with nonmissing covariates (n = 14338), the association was moderately attenuated (Supplement [eTable 4]-RRB-.

Participation in any strength training was associated with a 30 % rate reduction of type 2 diabetes (HR = 0.70, 95 % CI = 0.61 — 0.80, P < 0.001) compared with no participation, adjusting for time spent in lower - intensity and aerobic activities and model 1 covariates (age, smoking status, alcohol consumption, vegetable and fruit intake, saturated fat intake, total caloric intake, parental history of myocardial infarction, postmenopausal status, hormone therapy, and randomization arm during the trial period).

We calculated the covariates - adjusted number needed to harm associated with each quintile of added sugar intake at 15 - years of follow - up (15 years represents the median follow - up), 39 and the 95 % CI of the number needed to harm was based on 2.5 th and 97.5 th percentile values of 500 rescaled bootstrap weights.40, 41

We also excluded 279 participants with a body mass index (BMI) less than 18.5 (calculated as weight in kilograms divided by height in meters squared) and 905 with missing values on covariates, leaving 11733 adults for analysis.

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).

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

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).

Results were attenuated with the inclusion of the variables in model 2 and further so with the inclusion of covariates that directly affect average GI in model 3, yet participants in the fourth and fifth quintiles for dietary GI remained significantly more likely to have depression 3 y later in fully adjusted multivariable models.

The inclusion of the covariates that directly affect average GI in model 3 further attenuated the results, with the fifth quintile no longer being significant.

An analysis of covariance on students» spring comprehension scores across grades 2 - 6, with fall scores used as the covariate, revealed a significant effect for the school: F (2,336) = 10.18, p <.001; a significant effect for year in study (Year 1 or Year 2): F (1,336) = 4.18, p =.04; and a significant school by year - in - study interaction: F (2,336) = 10.30, p <.001.

Each panel compares the rating of teachers using a value - added model with prior test scores and student covariates to placements from another model: (1) a value - added model that includes only a prior test score (Panels A and D); (2) the SGP model (Panels B and E); and (3) a value - added model that makes within - school comparisons (Panels C and F).

Strikingly, about 6 percent of teachers who are placed in the top quintile in reading by the value - added model with student covariates are placed in the bottom quintile by the value - added model that makes within - school comparisons, and vice versa.

The movement becomes more pronounced as the correlations decrease, both as we compare the value - added model with student covariates to SGP models and within - school models, and as we make the same comparisons in reading.

We built a generalized estimating equation (GEE) general linear model (GLM) with outcome as the dependent variable; time in the nursing box, licking / grooming per puppy, vertical nursing per puppy, and ventral nursing per puppy were entered as predictors with breed, maternal parity, sex of puppy, and age at return entered as covariates.

A nested negative binomial regression model was created with intervention or control as the main effect, and type of pet (Cat or Dog) and age of pet (under two years, 2 - 7 years, or over 7 years) as covariates.

A nested logistic regression model was created with group (intervention or control) as the main effect, and type of pet (Cat or Dog) and age of pet (under two years, 2 - 7 years, or over 7 years) as the 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).