How do you interpret I2 statistics?
Researchers often use the I2 index to quantify the dispersion of effect sizes in a meta-analysis. Some suggest that I2 values of 25%, 50%, and 75%, correspond to small, moderate, and large amounts of heterogeneity. In fact though, I2 is a not a measure of absolute heterogeneity.
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.
How do you interpret heterogeneity I2?
A rough guide to interpretation is as follows:
- 0% to 40%: might not be important;
- 30% to 60%: may represent moderate heterogeneity*;
- 50% to 90%: may represent substantial heterogeneity*;
- 75% to 100%: considerable heterogeneity*.
What is a good heterogeneity score?
A rough guide to interpretation is as follows: 0% to 40%: might not be important. 30% to 60%: moderate heterogeneity. 50% to 90%: substantial heterogeneity.
Is heterogeneity good or bad?
The presence of substantial heterogeneity in a meta-analysis is always of interest. On the one hand, it may indicate that there is excessive clinical diversity in the studies included, and that it is inappropriate to derive an estimate of overall effect from that particular set of studies.
What does it mean if there is no heterogeneity?
There are several sources of heterogeneity, including differences in the treatment, the treated population, the study design, or the data analysis method. When there is no heterogeneity, estimates are said to be homogeneous and differ only because of random sampling error. Heterogeneity is very important.
How much heterogeneity is too much?
The variation in the true effects is called heterogeneity. Its impact on meta-analyses can be assessed by I2 that describes the percentage of the variability that is due to heterogeneity [1, 2]. Values greater than 50% are – rather arbitrarily – considered substantial heterogeneity [1].
Why is heterogeneity bad?
How is I2 heterogeneity calculated?
I2 can be readily calculated from basic results obtained from a typical meta-analysis as I2 = 100%×(Q – df)/Q, where Q is Cochran’s heterogeneity statistic and df the degrees of freedom. Negative values of I2 are put equal to zero so that I2 lies between 0% and 100%.
How is I2 calculated?
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 I2 in meta-analysis?
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.
What is the significance of the observed I2 value?
75% to 100%: considerable heterogeneity*. *The importance of the observed value of I2 depends on (i) magnitude and direction of effects and (ii) strength of evidence for heterogeneity (e.g. P value from the chi-squared test, or a confidence interval for I2).
What is the I^2 value of a homogeneous study?
The I^2 indicates the level of of heterogeneity. It can take values from 0% to 100%. If I^2 ≤ 50%, studies are considered homogeneous, and a fixed effect model of meta-analysis can be used.
How do you interpret I2 > 75%?
• I2 > 75% suggests high heterogeneity. For the data in Figure 1, I2 comes out as 0 and for the data in Figure 2 it is 94%. To deal with moderate heterogeneity in the data, it is recommended to use a random effects model for combining the estimates.
Is there a threshold for the interpretation of I2?
Thresholds for the interpretation of I2 can be misleading, since the importance of inconsistency depends on several factors. A rough guide to interpretation is as follows: