Sentences with phrase «potential covariates»

Finally, the current state of the literature would benefit from examining cohort differences and the length of widowhood as potential covariates in the relationship between widowhood and late life health.

At step 1, we controlled for the respective T1 eating behaviour, and for potential covariates of maternal age, BMI and education, and child age and gender (1 = male; 2 = female).

We focused on whether maternal problems in reciprocal social behavior increase the likelihood of infantile aggression and whether the association was mediated by maternal PDS after controlling for potential covariates.

Child interest in food at T2 was not significantly predicted by the model combining potential covariates and prior child interest in food (step 1).

Physical activity recall and 1 - week food frequency questionnaire were obtained at baseline and 3, 6, and 12 months to examine physical activity and dietary confounders as potential covariates.

While standardisation was performed for six principal covariates (gender, gestational age < 37 weeks, smoking in pregnancy, older siblings, maternal education, and maternal age at time of birth), there were several other potential covariates.

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.

Because SMMIS contained over 200 items of information for each pregnancy, the list of potential covariates was a long one.

Not exact matches

• Discuss how covariates (genetic, environmental, and clinical) can affect the levels of potential biomarkers

• Explore the use of covariate modeling to establish personally normalized plasma protein profiles (PNPPP) to reduce non-disease-related variation and maximize clinical potential

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.

It has been shown that inferences resulting from this analysis are virtually identical no matter which of these outcome measures is used.30 In addition to the covariates previously noted, the regression analysis was repeated to include annual household income, mother's treatment setting (primary vs psychiatric outpatient care), and treatment status of child during the 3 - month follow - up period in order to investigate the further potential confounding effects of these variables.

Age, sex, and ancestral proportion scores were entered in all of the models as covariates and were retained in the models regardless of their significance given the relevance of these potential confounding variables in interpreting the study results.

As in previous reports, the baseline covariates served as adjustments for potential differences between intervention and control families that resulted from nonrandom assignment at quasi-experimental sites or selective reporting of outcome data.

Many of the covariates in typical regression models represent potential causal pathways by which the neighborhood may influence the outcome.

The baseline covariates serve as adjustment for potential differences between intervention and control families that resulted from nonrandom assignment at quasi-experimental sites or selective reporting of outcome data.29 Results of these adjusted analyses are reported as ORs for dichotomous variables and as differences in means for continuous outcomes.

To account for potential confounders associated with diabetes self - care, we conducted a logistic regression to determine whether the percentage of patients with HbA1c levels ≥ 8 % were different between attachment style categories after adjusting for covariates that were specifically different between attachment groups, such as demographics, medical comorbidity, diabetes complications, diabetes knowledge, and depression.

Research also needs to adequately control for covariates that may confound the effects of PAE, such as family processes (eg, problematic parenting or family conflict) and parental characteristics, especially maternal substance use.1, 12 Researchers also need to account for genetic liabilities that are shared by parents and offspring.13, 14 A woman's genetic risk of substance use could be passed down to her children and subsequently affect their behavior.15 Research on the consequences of PAE, therefore, needs studies with large samples, with sufficient statistical power to detect small effects, using analytical methods and designs that can account for potential confounds, including factors that are not measured.

However, we took steps to reduce potential confounds by including a range of covariates in our models and controlled for individual differences in earlier verbal ability, general cognitive ability and EF (as well as parental education, child age, and formal schooling) in each of our models.

But it's important to note that potential tenants do not decide on which property they are going to rent by plugging the amenities and specs into a spreadsheet and running a logarithmic, covariate algorithm that takes the least - squares regression of the hypotenuse to determine the best value.