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As stated by Yekutieli (2009), under a random effect model, Bayesian inference is unaffected by selection.
Plasma homocysteine levels in VPA-treated patients with epilepsy were significantly higher than in healthy controls under a random effect model.
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Under a random effects logistic regression model, we compare two experimental treatments with a placebo in dichotomous data under an incomplete block crossover trial.
Under a random effects logistic regression model, asymptotic and exact test procedures in closed form for testing equality of binary responses are developed for comparing three treatments in a three-period crossover trial.
To address between-study heterogeneity we derived a pooled odds ratio under a random effects model [16].
Therefore pooled results were obtained under a random effects model.
To address heterogeneity between case control series, we derived pooled ORs under a random effects model.
When warranted, pooling was made under a random effects model, in view of anticipated differences in clinical design [ 6].
A heuristic explanation for this finding is that multiple estimates can better estimate the distribution of effects assumed under a random effects model.
Under a random effects model, we found a meta-KL (× 100) of 0.13 [95% confidence interval (CI): 0.04, 0.22] with an intercept of 1.47.
Mantel-Haenszel and inverse variance estimates were calculated and pooled under a random effects model expressing data as relative risks and 95% confidence intervals.
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