Sentences with phrase «multinominal logistic regression analysis»

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).

Descriptive statistics and multiple logistic regression analysis relating sleep position at each follow - up age to symptoms in the prior week (fever, cough, wheezing, stuffy nose, trouble breathing or sleeping, diarrhea, vomiting, or spitting up) and outpatient visits in the prior month (ear infection, breathing problem, vomiting, spitting up, colic, seizure, accident, or injury).

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.

Main Outcome Measures Descriptive statistics and multiple logistic regression analysis relating sleep position at each follow - up age to symptoms in the prior week (fever, cough, wheezing, stuffy nose, trouble breathing or sleeping, diarrhea, vomiting, or spitting up) and outpatient visits in the prior month (ear infection, breathing problem, vomiting, spitting up, colic, seizure, accident, or injury).

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.

Logistic regression analysis was used to adjust for potential confounding variables.

We also applied logistic regression analysis to calculate adjusted odds ratios, 28 in order to control for possible confounding factors.

We examined independent associations by using multivariable logistic regression analysis.

After examining the unadjusted, bivariate associations with delayed OL, we used logistic regression analysis to estimate the adjusted odds ratio (OR) and 95 % CI in multiple variable models.

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.

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.

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

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.

the sample size and to provide age - and education - adjusted normative data using a logistic regression 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).

The participants were categorized as CI or BRI and referents, and binary logistic regression analysis was applied.

Logistic regression analyses showed that whole plant foods intake was associated with a significant reduction in risk for nonvasomotor symptoms only.

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

We assessed the association between onlineoffline partner dating and UAI, using random - effects logistic regression analysis..

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

To investigate the relationship between learning styles, teacher demographics, and teacher retention, bivariate logistic regression analyses were conducted, and interactions between variables were tested for their predictive relationship with the binary outcome (retention).

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

Logistic regression analysis was used to evaluate risk factors for surgical DV.

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).

Individual weights were estimated by logistic regression analysis and trimming strategy.

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.

Using logistic regression analysis, odds ratios, 95 % confidence intervals and significance p values were estimated for association between each outcome and each childhood measure individually and in models including all childhood measures, each adjusted for cohort and gender.

Other risk factors were assessed and adjusted for by multivariable logistic regression analyses.

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.

Finally, multivariate logistic regression analyses and multivariate regression analyses were conducted to identify the factors associated with the scores and rates on the CBCL (i.e., scores for internalizing, externalizing, and total problems).

Factors associated with nicotine dependence among regular smokers according to the multivariate logistic regression analysis (ORs with corresponding 95 % CI)

Logistic regression analyses find that mothers with a varying work schedule, those who work more than 40 hours per week, those with more education, and those in families with the father as main child care provider are more likely to use multiple care arrangements.

To examine the average association between, on the one hand, the categorical variables mentioned above, and on the other, being SGA, we simply calculated ORs and 95 % CIs obtained from logistic regression analyses.

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.

Risk factors for DSH and SA were first analysed using univariate logistic regression analysis, with one variable at a time.

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).

After adjustment, the logistic regression analyses showed that mutations in rs1044429, rs2284191 and rs2856997 were associated with treatment response.

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)

Results from logistic regression analysis indicate that marital status differentially affects mortality, but not in a social vacuum.

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.