Sentences with phrase «regression models were used»

In the second part of the analyses, multinomial logistic regression models were used to examine which variables2 would discriminate between trajectories of social anxiety (Duchesne et al. 2010).

Logistic regression models were used to estimate moderation effects predicting school dropout six

To examine our first aim, separate regression models were used to evaluate each frontal EEG measure (dependent variable; frontal EEG power: FL, FR, and FA; functional connectivity: FL, FR, and FA), and each behavioral score (dependent variable; internalizing and externalizing score) in relation with pre - or post-natal maternal EPDS score (independent variable).

Multi-variable regression models were used to examine the impact of individual factors on exclusion from school or psychological distress.

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.

Latent growth modeling and hierarchical logistic regression models were used to adjust for variations at baseline.

Logistic regression models were used to assess whether particular variables were associated with each other.

Zero - inflated Poisson regression models were used to test for intervention impact on numbers of tardies, absences, and discipline referrals.

Linear and logistic regression models were used to examine the associations between psychiatric disorder from age 18 to 25 and workforce participation, income and living standards, and educational achievement at age 30, before and after adjustment for confounding factors.

Logistic regression models were used for controlling eight confounding variables such as maternal age, maternal education, employment status, parity, maternal BMI, hypertension, diabetes and medically assisted conception.

Two - part regression models were used to estimate the impact of child maltreatment on expenditures.

Multivariate logistic regression models were used to estimate odds ratios (ORs) while adjusting for factors associated with obesity risk using the SAS PROC LOGISTIC procedure (SAS Institute, Inc, Cary, North Carolina).

Multivariate logistic regression models were used to determine the association between IPV and childhood obesity.

Multiple - linear mixed - effect regression models were used to evaluate the association of sex hormones and sex hormone — binding globulin (SHBG) with % DBV, ADBV, and ANDBV.

Multivariable generalized linear regression models were used to examine the independent association between change in weight (lb) over 4 y and change in intake of fruits and vegetables (servings / day) over the same 4 - y time interval, as described in a previous publication [2].

Logistic regression models were used to estimate the association between presence of FMc and disease status, while adjusting for age and other confounding factors.

Kaplan - Meier and Cox proportional hazards survival analyses were used in unadjusted and adjusted analyses of the effect of pacifier use on breastfeeding duration.19 Logistic regression modeling was used to evaluate the effect of pacifier timing on breastfeeding duration.20 Significance levels were not adjusted for multiple comparisons.

To test for a relationship between fMRI response similarity and social distance, a dyad - level regression model was used.

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.

Design A cross-sectional study; the logistic regression model was use to find the risk factors of allergic rhinitis.

Linear regression modelling was used

Logistic regression models are used to assess whether there is reliable evidence that particular variables are associated with each other.

Not exact matches

Using these variables, a fixed effects regression model is employed.

Comparisons between intervention and control groups were tested with the use of ANCOVA regression models.

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.

We used multiple regression to estimate the differences in total cost between the settings for birth and to adjust for potential confounders, including maternal age, parity, ethnicity, understanding of English, marital status, BMI, index of multiple deprivation score, parity, and gestational age at birth, which could each be associated with planned place of birth and with adverse outcomes.12 For the generalised linear model on costs, we selected a γ distribution and identity link function in preference to alternative distributional forms and link functions on the basis of its low Akaike's information criterion (AIC) statistic.

First, a linear regression model was constructed using the latest postnatal weight measurement in grams as the dependent variable and using the breastfeeding medication group (fluoxetine: yes / no) as the independent variable of interest.

The analysis was carried out using a logistic binary regression model, with PPH as the outcome variable and built using manual forward selection (with p < 0.05 as the cut - off).

It is an observational study involving secondary analysis of maternity records, using binary logistic regression modelling.

The effect of the timing of pacifier introduction (≤ 2 weeks and ≤ 6 weeks) on breastfeeding duration at 2 and 3 months was evaluated using logistic regression modeling.

The timing of pacifier introduction on breastfeeding was evaluated by using logistic regression modeling to predict breastfeeding duration to 2 and 3 months» postpartum.

The ~ 6 - million - km2 Amazonian lowlands were divided into 1 ° cells, and mean tree density was estimated for each cell by using a loess regression model that included no environmental data but had its basis exclusively in the geographic location of tree plots.

Although the same protocol was used for the mouse cohorts, a linear regression model with indicators for genotype and cohort was used to adjust for possible training differences.

Multilevel logistic regression was used to estimate the odds ratios (ORs) for conversion to laparotomy, CRM +, intraoperative complications, and postoperative complications between treatment groups, adjusting for the stratification factors, where operating surgeon was modeled as a random effect.

Individual clinical phenotypes were measured using regression models in PLINK [36].

Then, we generated proxies with this fitted model, using as predictors the SST time series where the components linearly related to the MEI, PDO and AMO were removed (by linear regression, as above).

The model was validated using the Monte Carlo simulation based on the fitted model and computing an empirical semi-variogram with the residuals, obtained by fitting a standard logistic regression with simulated data and predictors (see Supplementary File 1).

To investigate to what extent these effects can be controlled for, we used a linear regression model that allows incorporating additional covariates.

Using linear regression, we demonstrate that the multivariate pattern of gray matter density within these brain regions significantly predicts individual intelligence scores in the remaining, i.e., independent sample used for model testing (N = 108; correlation between predicted and actual intelligence scores: r =.36).

Multiple linear regression models (in PROC GLM) were used to control for potential confounding factors.

Relations between serum CRP and dietary fiber were assessed by using linear mixed models and logistic regression, adjusted for covariates.

Tests for trend with the use of simple linear regression analysis were performed by modeling the median values of each fiber category as a continuous variable.

Hazard ratios and 95 % confidence intervals for mortality associated with coffee consumption were estimated with the use of Cox proportional - hazards regression models, with person - years as the underlying time metric; results calculated with age as the underlying time metric were similar.

To compare changes in FFM between groups, a general linear regression model was fitted with FFM as the dependent variable, and treatment, time, and the interaction of treatment with time were used as independent variables.

The relationship between an athlete personal best in competition and back squat, bench press and power clean 1RM was determined via general linear model polynomial contrast analysis and regression for a group of 53 collegiate elite level throwers (24 males and 29 females); data analysis showed significant linear and quadratic trends for distance and 1RM power clean for both male (linear: p ≤ 0.001, quadratic: p ≤ 0.003) and female (linear: p ≤ 0.001, quadratic: p = 0.001) suggesting how the use of Olympic - style weightlifting movements — the clean, in this particular case, but more in general explosive, fast, athletic - like movements — can be a much better alternative for sport - specific testing for shot putters (Judge, et al, 2013).

To examine the prospective association of sugar intake from sweet food and beverages, a random effects logistic regression model (REM) was performed using the STATA command xtlogit 48, with exposures at phases 3, 5, 7 and 9 for GHQ caseness, and at phases 7 and 9 for CES - D caseness.

The analyses were first conducted in each cohort separately, and because no appreciable difference was detected by cohort (eTable 1 in the Supplement), we then conducted the pooled analysis using the sex - stratified Cox proportional hazards regression model in the combined data set.

The relationships between cardiovascular fitness at age 18 y and subsequent academic and educational achievements were determined using Cox proportional - hazards regression models.

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 test for linear trend of effects across coffee consumption categories was performed by regressing each log RR on the ordered categorical variable for coffee in 5 levels using a random - effect meta - regression model.