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Data with homogeneity among studies were analyzed using a fixed-effect meta-analysis model; otherwise, a random-effect model was applied to data with heterogeneity.
The UMD and odds ratio (OR) were estimated by indirect meta-analysis using a random effects model, otherwise a fixed effects model was applied.
If heterogeneity was present as determined by a statistically significant Q-statistic or by I 2 > 25 %, the UMD was estimated using a random effects model; otherwise a fixed effects model was applied.
When there was no statistically significant heterogeneity, a pooled effect was calculated using a fixed-effect model; otherwise, a random-effect model was employed.
Without the heterogeneity (test for inconsistency not significant), pooled estimates of odds ratios (ORs) or effect sizes and 95% CIs for the estimates were derived using a fixed-effects model; otherwise, a random-effects model was used (16).
In the case where assumptions for normality and homogeneity were not violated, our hypotheses were tested using an ANOVA model, otherwise a nonparametric Kruskall-Wallis ANOVA test was used.
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We solved this by fitting each individual BMI SDS trajectory by a piecewise linear model, otherwise known as a broken stick-model [26], with the knots set equal to the break ages.
In a causality graph between root causes C and symptoms S, if I(s j |c i )=0 or 1 for all (i,j), we call such causality model a deterministic model; otherwise, we call it a likelihood model.
If P > 0.1 and I 2 < 50%, there was determined to be little heterogeneity between studies, then we used a fixed effect model, otherwise we should use a random effect model.
In view of the unstable relationship, we further included one year lagged deposit growth as a regressor in a model otherwise similar to that in column (8) of Table 4.
In a model otherwise identical to Model 1, Model 2 includes a random effect on the slope of adolescent violence.
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