Sentences with phrase «logistic regression analyses of»

Adjusted ORs (95 % CIs) from logistic regression analyses of depression prevalence according to quintiles of energy partition — adjusted GI and glycemic load1

Adjusted ORs (95 % CIs) from logistic regression analyses of depression incidence according to quintiles of specific measures of energy partition — adjusted carbohydrate consumption1

Adjusted ORs (95 % CIs) from logistic regression analyses of depression incidence according to quintiles of energy partition — adjusted GI and glycemic load1

I have used these numbers as part of a logistic regression analysis of the positions of Conservative MPs on Brexit in order to try to explain why they have chosen their stance on the referendum.

In addition, to assess whether there was an independent study effect on pregnancy rates by time period of recruitment into the study (before and after December 31, 2001), we included a time period variable in the multiple logistic regression analysis of the full study sample and found no effect.

Not exact matches

Our logistic regression analysis with NHIS data suggests that diabetes is associated with a 2.4 percentage point increase in the likelihood of leaving the workforce for disability.

Logistic regression analysis was used to calculate the OR of not meeting a specified nutrient reference values for Australia and New Zealand per unit in % EAS or % ETS.

• In another Australian study, in multivariate logistic regression analyses «feeling close to the unborn baby» and a «high level of knowledge about the effects of passive smoking on baby» were associated with early quit attempts by fathers Moffatt & Stanton (2005).

Second, the associations between duration of breastfeeding and upper and lower respiratory and gastrointestinal tract infections in infants aged 6 and 12 months were analyzed by using multiple logistic regression analysis.

A logistic regression analysis was conducted to adjust for the effects of variables identified through the bivariate analysis to be associated with either type of feeding or the presence of infection or sepsis / meningitis.

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

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.

The above classification of MPs then formed the basis of the ordinal logistic regression analyses [5] used to determine common traits among the out camp.

«Machine learning offers new way of designing chiral crystals: Logistic regression analysis model predicts ideal chiral crystal.»

Bioinformatic approaches to the analysis of genetic variability and complex genotype - phenotype relationships will moreover include gene sequence and database analyses, measures of association of haplotypes / genotypes with phenotype, clustering procedures, neuronal networks, fuzzy and other techniques in pattern recognition, similarity measures for discrete patterns (e.g., gene sequences, structures, functions), logistic regression methods, and a spectrum of other techniques.

As described in the main text, ordered logistic regression analyses were carried out for each brain region in which social network distances were modeled as a function of local neural response similarities and dyadic dissimilarities in control variables (gender, ethnicity, nationality, age, and handedness).

To gain insight into what brain regions may be driving the relationship between social distance and overall neural similarity, we performed ordered logistic regression analyses analogous to those described above independently for each of the 80 ROIs, again using cluster - robust standard errors to account for dyadic dependencies in the data.

A 2 - step logistic regression analysis was used to control for the method of culture in which the independent significance of RFLP type was assessed with regard to blood culture positivity.

Andrews, P. L., Loftsgaarden, D. O. & Bradshaw, L. S. Evaluation of fire danger rating indexes using logistic regression and percentile analysis.

In multivariate analysis, stepwise logistic regression analysis demonstrated that FAB (odds ratio [OR] = 0.79, 95 % confidence interval [CI] = 0.66 — 0.94) and the use of CCB (OR = 2.72, 95 % CI = 1.09 — 6.77) were significantly associated with UI at 1 year.

A multivariable logistic regression analysis was conducted to evaluate factors independently associated with the prescription of other psychotropic drug classes among patients already using antipsychotics.

Logistic regression analysis revealed that the presence of IgM to CMV proteins was not associated with a specific subject group but was associated with age (P less than.0001), gender (P less than.005), and IgG titer (P less than.03).

Odds ratios (ORs) and 95 % CIs of the likelihood of elevated C - reactive protein concentrations (> 3.0 mg / L) from logistic regression using cross-sectional analyses in the Seasonal Variation of Blood Cholesterol Study, Worcester, MA (1994 — 1998)

Of other indexes of exposure, working in the quality - control room at the plant was significantly associated with airway obstruction in a logistic - regression analysis, after adjustment for age and smoking status: five of six persons were affected (odds ratio for the comparison with all the other workers, 41.7; 95 percent confidence interval, 3.5 to 494Of other indexes of exposure, working in the quality - control room at the plant was significantly associated with airway obstruction in a logistic - regression analysis, after adjustment for age and smoking status: five of six persons were affected (odds ratio for the comparison with all the other workers, 41.7; 95 percent confidence interval, 3.5 to 494of exposure, working in the quality - control room at the plant was significantly associated with airway obstruction in a logistic - regression analysis, after adjustment for age and smoking status: five of six persons were affected (odds ratio for the comparison with all the other workers, 41.7; 95 percent confidence interval, 3.5 to 494of six persons were affected (odds ratio for the comparison with all the other workers, 41.7; 95 percent confidence interval, 3.5 to 494).

In February of 2011, CUNY's Office of Institutional Research and Assessment, headed by University Dean David Crook, released critical data (obtained by Director of Policy Analysis Colin Chellman using linear probability models and logistic regression) demonstrating that, all else being equal (i.e., taking into account all measurable demographic and performance characteristics), CUNY's transfer students were at a disadvantage in terms of graduation compared to native students.

Specific statistical areas of expertise include factor and cluster analysis, basic bivariate analyses, repeated measures analyses, linear and hierarchical / mixed models, structural equation modeling, and nonparametric analyses including logistic regression techniques.

Table 5 provides an overview of the results of logistic regression analyses predicting attrition through Year 2 of the intervention.

To probe these findings further, we conducted a set of logistic regression analyses using teacher retention through Year 2 as our binary outcome.

I also taught myself a fair bit of statistics along the way including logistic regressions and discriminant analysis in order to backtest different models for identifying outperformers, dividend growth / cuts etc..

For this reason they ran a series of complex statistical analyses which are called logistic regressions, to tease apart the variables.

Performed advanced statistical analysis (univariate and multivariate analysis of variance, cluster and path analysis, principle component and factor analysis, analysis of covariance, survival & longitudinal analysis, logistic and linear regression modeling), created customized reports and presentation quality data summary tables and figures.

Multiple logistic regression analysis was used to estimate the odds of pregnancy during the study by treatment arm (odds ratios [ORs] are presented with 95 % confidence intervals).

aChild Behavior Checklist for 4 - 18 years; bChildren who are currently visiting their father who used to perpetrate intimate partner violence and already separated from their mothers; cInternalizing problems = Withdrawn + Somatic complaints + Anxious / depressed; dExternalizing problems = Delinquent behavior + Aggressive behavior; Total problems = the sum of the scores of all the nine subscales of the CBCL; eAdjusted odds ratios calculated by multivariable logistic regression analysis; fThe dependent variable: 0 = non - clinical, 1 = clinical; gp values calculated by multivariable logistic regression analysis; hStandardized regression coefficients calculated by multivariable regression analysis; ip values calculated by multivariable regression analysis; jVariance Inflation Factor; k0 = non-visiting, 1 = visiting; lThe score of the subscale (anxiety) of the Hospital Anxiety and Depression Scale; mThe score of the subscale (depression) of the Hospital Anxiety and Depression Scale; nThe number of years the child lived with the father in the past; oAdjusted R2 calculated by multivariable regression analysis.

Baseline differences among conditions were tested using analyses of variance and logistic regression.

Multiple logistic regression analyses were used to determine the association between panic attacks during adolescence in 1983 and the risk of personality disorders during young adulthood in 1993, adjusting for differences in sociodemographic characteristics, adolescent personality disorders, and co-morbid depressive and substance use disorders.

Associations between each of the baseline residential - environmental factors and five year survival were examined by multiple logistic regression analysis.

Analyses used an analysis of covariance (continuous measure) or logistic regression (categorical measure) with the posttreatment measure as a covariate.

Topics Include Exploratory Data Analysis, Multiple Regression, Logistic Regression, Correlation, Multivariate Analysis Of Variance (manova), Factorial Analysis Of Variance (anova), Factor Analysis And Principal Components, Discriminant Analysis, Structural Equation Modeling, And Emerging Data Analysis Techniques.

Logistic regression analyses were conducted to estimate the effect of maternal IPV on asthma diagnosed by age 36 months while adjusting for potential confounders (child's sex, age, race / ethnicity, low birth weight, maternal education, economic hardship, and tobacco exposure).

We will compare the proportion of patients meeting guidelines for gestational weight gain and for weight retention at 1 year postpartum between the two groups using logistic regression analyses.

Multivariate logistic regression analyses * assessing impact of unawareness of the health consequences of tobacco use on attitudes and behaviour towards tobacco control programmes among school personnel from 29 African countries, 2006 — 2011 (n = 17 929)

In the logistic regression analysis (Table 2), all at least marginally significant social and family obesity variables (ie, age of index patient, presence versus absence of obese siblings, and maternal obesity) were introduced first to control for their influences.

The study involved administering all 3 sets of scales to a general population sample who were then interviewed by clinical interviewers blinded to screening scales scores and classified as having or not having SMI based on 12 - month prevalences of DSM - IV disorders, as assessed by the Structured Clinical Interview (SCID) for DSM - IV16 and scores on the GAF.1 Logistic regression analyses were then carried out to estimate the strength of associations between the screening scales and SMI using linear and nonlinear prediction equations that assumed either additive or multiplicative associations among the different screening scales.

The adjusted odds ratios and 95 % confidence intervals reported for these logistic regression analyses represent the increased likelihood of the outcome of interest, compared with having experienced no adversities.

Similar to the ACE Study analyses, for each outcome variable, a binary logistic regression was applied to test the relationship of the adversity index score (0, 1, 2, 3, or ≥ 4) to the outcome, after entering the control variables (child's sex, child's race / ethnicity, caregiver's marital status, and family income).

Logistic regression analyses associating family exposures of anxiety and depression symptoms in adolescence with receipt of medical benefits from age 20 to 29, imputed data

Further logistic regression analyses indicated that the effect of family type on health outcomes was, in most cases, significant after controlling for the 3 social class indicators and child sex.

For each outcome, the first row presents the percent of affected children in each family type; this is complemented in the second row with the OR from logistic regression analysis using children living with 2 biological parents as the index (control) group.

Multiple mediation analyses utilising linear and logistic regression models as appropriate were used to further investigate the extent to which the association between sports club membership and each of the three SES indicators was mediated by the three potential mediators.

The effects of relationship dissatisfaction, life events, emotional distress, and demographic variables on the risk of relationship dissolution were examined using logistic regression analyses.