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As the Q tests of heterogeneity are significant for flexible work schedules (p < 0.001) and telecommuting (p < 0.01), the assumption of a random effects model is appropriate.
To assess how different imputation methods used to account for missing variance data in primary studies influence tests of heterogeneity and pooled results from a meta-analysis with continuous outcomes.
Tests of heterogeneity for prostatitis/STDs were conducted for severity of disease and race/ethnicity, respectively.
In all three meta-analyses the between-study heterogeneity was substantial, with I2 values >90% and P from tests of heterogeneity <0.0001 (Figure 3).
Results were combined in a meta-analysis under a fixed effect model in R. Tests of heterogeneity were also evaluated using the I2 statistic [34].
Tests of heterogeneity were also conducted.
Tests of heterogeneity, sensitivity, and publication bias were performed.
For both locations, tests of heterogeneity suggested no difference across ethnic groups (Table 2).
Statistical tests of heterogeneity are very popular in meta-analyses, as heterogeneity might indicate subgroup effects.
The power of each of the two tests of heterogeneity is indistinguishable (HETEROGENEITY compared with SP1vSP2).
We performed tests of heterogeneity using the Breslow-Day test in PLINK.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com