Sentences with phrase «random effects model»

Standardised mean differences were derived to take account of the variety of behavioural outcome measures included and random effect models adopted in view of variability of the intervention and target populations across studies.

A multilevel random effects model accounts for the hierarchical structure of the data, in which the effect sizes or study results (the lowest level) are nested within studies (the highest level).

To deal with dependency of study results, we used a multilevel random effects model for the calculation of combined effect sizes and moderator - analyses (Hox 2002; Van den Noortgate and Onghena 2003).

Data were analyzed using a propensity score stratification method and a cross-classified random effects model, adjusting for socio - demographic characteristics.

Growth trajectories of maternal parenting practices (including family routines, firm - responsive parenting, and corporal punishment) were modeled using linear random effects models.

However, these differences were accounted for in part through the use of the random effects model.

Based on the random effects model, the final pooled effect size in the form of relative risk was 0.71 (95 % CI: 0.54 to 0.87) for health facility delivery as compared to home delivery.

A random effects model was used and statistical heterogeneity was quantified using the I2 and τ2 statistics.

Results from individual studies were combined and number needed to treat and relative risk of response (primary outcome) were calculated using a random effects model.

Cochrane odds ratio plot (random effects model)

All analyses used the random effects model, as it is assumed that there is non-random variation between studies [9].

Using the random effects model in CMA, weighting is done using the inverse of the study's variance, where the variance is made up of the within - study variance and the between - study variance [7].

Overall effect sizes were pooled using a random effects model.

Where appropriate the results have been combined in a meta - analysis using a random effects model.

The authors assumed a random effects model across the studies.

We used linear mixed regression models with random intercept and slope (random effects models) to examine the extent to which the predictor variables considered influenced changes in continuous CBCL total, internalising, and externalising T scores from ages 2 to 14.

Explaining the relationship between temperament and symptoms of psychiatric disorders from preschool to middle childhood: hybrid fixed and random effects models of Norwegian and Spanish children.