Sentence examples for between study variation when from inspiring English sources

Similarly, the proportion of culture-positive TB in the population remained insignificant but explained 2.6% of the between study variation when excluding studies with the highest risk of bias (p=0.278 and adjusted R=2.62).

The proportion of culture-positive TB in the population remained insignificant but explained 2.6% of the between study variation when excluding 0% clustering (p=0.278 and adjusted R=2.62).

The number of loci typed became non-significant, but explained 9.6% and 10.5% of the between study variation when excluding studies using the 'old 12' loci and the highest risk of bias, respectively (p=0.106, adjusted R=9.63; p=0.111, adjusted R=10.51, respectively).

Since there is no information about the between-study variation when we have just a single study, it is appropriate that the estimation should fail in such instances.

Data on study design included analysis type, length of follow-up, classification of long- or short-stay NH admissions, and analytic unit of predictors (e.g., individual-level, community-level, state-level, etc).. Results were integrated with random effects models, as these models consider both within- and between-study variation when deriving pooled empirical associations [ 17].

The univariable metaregression shows evidence for the proportion of clustering to decrease as the number of MIRU-VNTR loci typed increased from 15 to 24 (p=0.04; table 3), accounting for 14% of the between study variation, and to increase when the study participants consented to being included in the study (p=0.03), accounting for 14% of the between study variation.

The summary LR for random effects is usually recommended for the analysis of accuracy studies when an estimate of between study variation can be incorporated.

Furthermore, the between-study variation was markedly lower when taking tumor size into account, and somewhat lower within subgroups of grade.

When there is a high level of between-study variation (study heterogeneity), it is important to look for explanatory variables (covariates) to explain this variation.

Both measures were very close despite substantial between-study variation (I2 = 81%9595% CI: 68 89%); Fig. 2a).

FLAME 1 implements a random-effects meta-analysis with iterative estimation of between-study variation via maximum likelihood70,71.