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Heterogeneity between studies was evaluated using homogeneity statistic (Q), which follows a Chi-square distribution with a degree of freedom of (n-1) where n is the total number of studies included43.
Heterogeneity between studies was evaluated with the I test and the Q statistic.
Statistical heterogeneity between studies was evaluated with Cochran's Q test and the I2 statistic.
Bias was removed using Hedges' g method [ 19], and the homogeneity between studies was evaluated by the Q test [ 20].
The heterogeneity of sensitivity and specificity between studies was evaluated using Cochran's Q and chi-square-test, followed by the calculation of inconsistency (I).
The heterogeneity that exists in a meta-analysis due to the variation in outcomes between studies was evaluated by Chi-square, according to Peto's method [ 21].
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The advantage of this statistical approach, which uses a Gaussian distribution to represent the experimental error associated with each study, is that the relative error between studies is evaluated and that all available information is used to determine the best estimates of the parameters common to all of the included studies.
Differences between studies were evaluated using chi-squared tests of associations at the 5, 1 and 0.1 % levels of significance.
The heterogeneity between the studies was evaluated using Cochran's test.
Statistical heterogeneity between the studies was evaluated using Cochran's Q test and the I statistic.
The heterogeneity between the studies was evaluated by the Chi square-test based Q-statistic.
More suggestions(17)
between studies was investigated
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between differences was evaluated
between techniques was evaluated
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between proportions was evaluated
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between populations was evaluated
between studies was judged
between correlations was evaluated
between studies was taken
between treatments was evaluated
between slopes was evaluated
between readers was evaluated
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