Contents
How is I2 heterogeneity calculated?
The I² statistic describes the percentage of variation across studies that is due to heterogeneity rather than chance (Higgins and Thompson, 2002; Higgins et al., 2003). I² = 100% x (Q-df)/Q. I² is an intuitive and simple expression of the inconsistency of studies’ results.
How do you calculate I2 in statistics?
I2 can be calculated from Cochran’s Q (the most commonly used heterogeneity statistic) according to the formula: I2 = 100% X (Cochran’s Q – degrees of freedom). Any negative values of I2 are considered equal to 0, so that the range of I2 values is between 0-100%.
What is a good I2 statistic?
While determining what constitutes a large I2 value is subjective, the following rule-of thumb can be used: < 40% may be low. 30-60% may be moderate. 50-90% may be substantial. 75-100% may be considerable.
What is heterogeneity of variance and why does it matter?
Heterogeneity of variance is a special instance of what is known as heteroscedasticity in the context of regression, the only difference being due to the nature of the predictors—categorical in procedures of group means comparisons and continuous in regression-based procedures.
Do you need to consider heterogeneity in a Cochrane review?
Variation across studies (heterogeneity) must be considered, although most Cochrane Reviews do not have enough studies to allow for the reliable investigation of its causes. Random-effects meta-analyses allow for heterogeneity by assuming that underlying effects follow a normal distribution, but they must be interpreted carefully.
Which is the correct statistic for heterogeneity across studies?
The I² statistic describes the percentage of variation across studies that is due to heterogeneity rather than chance ( Higgins and Thompson, 2002; Higgins et al., 2003 ). I² = 100% x (Q-df)/Q. I² is an intuitive and simple expression of the inconsistency of studies’ results.
When to use the chi-squared test for heterogeneity?
This will happen if the I2 statistic is greater than zero, even if the heterogeneity is not detected by the chi-squared test for heterogeneity (Higgins 2003) (see Section 9.5.2 ). The choice between a fixed-effect and a random-effects meta-analysis should never be made on the basis of a statistical test for heterogeneity.
How is heterogeneity measured in a meta-analysis?
Methods have been developed for quantifying inconsistency across studies that move the focus away from testing whether heterogeneity is present to assessing its impact on the meta-analysis. A useful statistic for quantifying inconsistency is where Q is the chi-squared statistic and df is its degrees of freedom (Higgins 2002, Higgins 2003).