Sentences with phrase «effects logistic»

The analysis was performed with random effects logistic models that accounted for the two - wave panel structure of the data.

The three - way interaction term and its implementation in the random effects logistic regression model is specified in the following equation:

We used a random - effects logistic model to examine changes in smoking status among women over time, incorporating age, education and community (urban v remote) as covariates.

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.

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

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 results from logistic regression analyses were presented as OR, with the OR from the fixed - effect logistic regression (sibling comparison) having a cluster - specific interpretation.22 All the analyses were reported with 95 % CI.

Not exact matches

• 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).

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.

When categories of human milk were entered into the logistic regression models, a dose - response effect was not observed.

Conditional logistic regression was employed to estimate adjusted odds ratios for infant feeding and method of sterilisation on diarrhoeal disease, and to assess whether the effect of breast feeding persisted after breast feeding had ceased.

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.

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.

Because it was not possible to examine the independent effects of BMI and edema in the same logistic regression model, these variables were examined in separate models.

They then conducted a series of logistic regression models that included fixed effects for year and state, and adjusted for demographic characteristics, school characteristics, and other state alcohol policies.

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.

A logistic regression model was fit to evaluate the effects of each sociodemographic variable level on odds of screening positive for depression controlling for each of the other sociodemographic variables.

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.

But at the very moment when one considers joined piecewise linear approximations [fig 4], without giving the reasons why the system would change its characteristics at that very time, perhaps it is more sensible to look for mechanisms (or effects, if we are unsure of the mechanisms) that describe the changing rate of change (leading to quadratic description) or perhaps even other functional forms: periodic, logistic, cubic.

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.

Because poverty predicts risk for school adjustment problems, low achievement, crime, and other problem behaviors, the effects of the full intervention on children from poor families were investigated using logistic and linear regression methods as appropriate, with terms for intervention and free lunch eligibility as main effects and an interaction term for intervention by participation in the free lunch program.

Data Extraction Logistic regression was used to estimate the effects of the number of short alleles at 5 - HTTLPR, the number of stressful life events, and their interaction on depression.

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).

Maternal age at delivery, ethnicity, smoking during pregnancy, parity, paternal diabetes status at follow - up, family social class, sex, offspring physical activity, and offspring smoking habits were not found to be confounders and had no effect on offspring risk of type 2 diabetes / pre-diabetes when entered in multiple logistic regression models.

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.

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

To put the effect sizes for the hypothesized associations on wave 6 reckless driving into perspective, we re-ran the final model using logistic regressions (for the connections between the wave 6 indicators and the wave 6 latent variables) to obtain odds ratios (OR) for the indirect effects of wave 1 predictors on the individual wave 6 reckless driving items.

We used ordinal logistic - regression analysis to test the independent effects of each variable, adjusting for demographics, child personality, and parenting style.

Generalized regression models (logistic regression for dichotomous outcomes, linear regression for continuous outcomes) were used to estimate the overall adjusted effects of Healthy Steps.26, 27 These models included site variables to account for the fact that families within sites tend to respond more similarly than those at different sites.

Using publicly available community - level AEDI data, 62, 63 we ran a two - level multilevel logistic regression model for one aggregate developmental outcome measure (ie, risk of developmental vulnerability; figure 3A) and an example simulation (figure 3B) using a total sample of 181 500, with the proportion of Aboriginal children in each LGA derived from ABS estimates.64, 65 Binomial outcome data were simulated assuming a baseline risk of being vulnerable of 21 % and a community - level random effect based on the actual variation in the published data (figure 3A).

Intervention effects will be assessed by conducting linear and logistic random effects models incorporating a time by group interaction or latent growth curve modelling to determine whether trends across the three data points within the course of the patients» treatment differ between the carer groups.39 The models will adjust for confounders and effect modifiers as necessary.

Logistic regressions were used to predict the likelihood of recovery at 18 months, and mixed - effects regression analysis was applied to examine the association of severity and rates of improvement across time in the two treatment groups.

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

Logistic regression was used to assess the association of child mental health conditions and parent mental health status, while examining socioeconomic, parent, family, and community factors as potential effect modifiers and confounders of the association.

Multivariate logistic regression analyses indicated that past adolescent conduct disorder, being younger and male, symptoms of Akathisia (movement disorder, most often develops as a side effect of antipsychotic medications), and particularly drug abuse increase the risk for CJS involvement.

No gender differences were found with respect to attachment to mother (χ 2 (1) =.003, p >.05) or father (χ 2 (1) =.26, p >.05), nor were there any effects of child age (entered in a logistic regression with dichotomous attachment classification as outcome variable) for mother B =.02, p =.67 and father B = −.03, p =.49.