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Statistically significant heterogeneity among sites was not detected, probably because of the large within versus between site variance of effects.
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Accordingly, the false-positive rate of the QTLST also depends on the variance of effect sizes.
Between-center heterogeneity was quantified using the between-center variance of effect estimates, τ.
In fact, for QTL data sets with low variance of effect sizes, the power to detect selection asymptotes to zero.
The QTLST-EE showed little dependence on variance of effect sizes for s < 5 × 10−7 but detected selection less often in higher variance samples for s ≥ 5 × 10−7.
Single imputation methods do not account for the uncertainty about the predictions of the unknown missing values and may lead to underestimation of the variance of effect estimates.
Differences in patient selection may have attributed to the observed variance of effect of adjuvant (H IPEC within the different studies.
Moreover, for cluster randomized trials, there is a higher risk that the variance of effect estimate will be underestimated with single imputation methods.
A fixed-effect model assumes that all the studies using a particular protocol have the same true effect size and that the observed variance of effect sizes across the studies is due entirely to random error within the studies.
Genomic prediction requires estimation of variances of effects of single nucleotide polymorphisms (SNPs), which is computationally demanding, and uses these variances for prediction.
Using this model we calculated the following quantities: the baseline subdistribution hazard and corresponding cumulative incidence of NB, ICU effects, variance of ICU effects and the subdistribution hazard ratios for multilevel risk factors at the patient and ICU levels.
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