Sentences with phrase «continuous outcomes»

We used mean difference (MD) with 95 % confidence interval (CI), employing a random - effects model for continuous outcomes measured on the same scales.

The estimated effects of continuous outcomes are presented as a standardised effect size and dichotomous variables as OR.

If the same continuous outcome is measured differently across studies, we may calculate an overall standardised mean difference (SMD) and 95 % CI (Deeks 2011).

If continuous outcomes are measured identically across studies, we may calculate an overall mean difference (MD) and 95 % CI.

As outlined above, the stratification factors will be included as factors in the ANCOVA models analysing the primary and secondary continuous outcomes and, depending on sample size, may also be included in a Mantel - Haenszel χ2 analysis of the post-treatment categorical outcomes.

For continuous outcomes measured on different scales, we pooled standardised mean differences (SMDs) and associated 95 % CIs.

Hierarchical multiple regressions were performed for nonemergency services, ER visits, ear infections, and acute respiratory illnesses, as they were continuous outcome variables.

Efficacy (as a continuous outcome), measured by the overall mean change scores on depressive symptom scales (self - rated or assessor - rated), for example, Children's Depression Rating Scale (CDRS - R) 32 and Hamilton Depression Rating Scale (HAMD) 33 from baseline to endpoint.

We used xtreg in Stata for continuous outcomes and xtlogit for dichotomous outcomes.

Mean effect sizes for continuous outcomes were pooled using the generic inverse variance methods to give the standardised mean difference (SMD).

PROCESS estimates unstandardized model coefficients, standard error, t - and p - values, and confidence intervals using OLS regressions for continuous outcomes.

We considered a completely cluster randomized design with a continuous outcome (normally distributed) measured at a single time point.

The size of effect is described by the standardized difference (Cohen's «d») between means or proportions, and the association between treatment group and outcomes is described by the odds ratio for dichotomous outcomes and the correlation ratio (η) for continuous outcomes.

Primary and secondary outcomes will be analyzed using multiple regression for continuous outcomes, and logistic regression for binary outcomes controlling for baseline scores where possible.

Applying multi-group confirmatory factor models for continuous outcomes to Likert scale data complicates meaningful group comparisons

Evaluation of model fit indices for latent variable models with categorical and continuous outcomes

Analyses were conducted using linear regression for continuous outcomes and binary logistic regression for categorical outcomes.

Robust inference using weighted - least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes