A value of 0% indicates no observed heterogeneity, and larger values show increasing heterogeneity.” Measuring inconsistency in meta-analyses. Julian P T Higgins, Simon G Thompson, Jonathan J Deeks, Douglas G Altman. Metan: fixed- and random-effects meta-analysis.

The I-square is able to quantify the degree of heterogeneity among studies and, in general, it is calculated in addition to the chi-square test.

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.

Quantifying heterogeneity: a better approach 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%.

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.

Egger’s test is commonly used to assess potential publication bias in a meta-analysis via funnel plot asymmetry (Egger’s test is a linear regression of the intervention effect estimates on their standard errors weighted by their inverse variance).

“Small-study effects” is a generic term for the phenomenon that smaller studies sometimes show different, often larger, treatment effects than large ones. This notion was coined by Sterne et al. [55]. One possible, probably the most well-known, reason is publication bias.

A funnel plot is a scatterplot of treatment effect against a measure of study precision. It is used primarily as a visual aid for detecting bias or systematic heterogeneity. A symmetric inverted funnel shape arises from a ‘well-behaved’ data set, in which publication bias is unlikely.

Meta-regression is an extension to subgroup analyses that allows the effect of continuous, as well as categorical, characteristics to be investigated, and in principle allows the effects of multiple factors to be investigated simultaneously (although this is rarely possible due to inadequate numbers of studies) ( …

Meta-regression constitutes an effort to explain statistical heterogeneity in terms of study-level variables, thus summarizing the information not as a single value but as function.

• Linear regression. One of the most basic types of regression in machine learning, linear regression comprises a predictor variable and a dependent variable related to each other in a linear fashion.
• Logistic regression.
• Ridge regression.
• Lasso regression.
• Polynomial regression.

Heterogeneity and its opposite, homogeneity, refer to how consistent or stable a particular data set or variable relationship are. Having statistical heterogeneity is not a good or bad thing in and of itself for the analysis; however, it’s useful to know to design, choose and interpret statistical analyses.

Heterogeneity is not something to be afraid of, it just means that there is variability in your data. So, if one brings together different studies for analysing them or doing a meta-analysis, it is clear that there will be differences found.

Strategies for addressing heterogeneity in systematic reviews include checking that the data extracted from the trial reports are correct, which may often not be the case [3]; omitting meta-analysis; conducting subgroup analysis or meta-regression; choosing a fixed effect or a random effects model [2]; changing the …

Meta-analysis seeks to combine the results of several experiments in order to improve the accuracy of decisions. It is common to use a test for homogeneity to determine if the results of the several experiments are sufficiently similar to warrant their combination into an overall result.

The classical measure of heterogeneity is Cochran’s Q, which is calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies, with the weights being those used in the pooling method. …

To determine whether significant heterogeneity exists, look for the P value for the χ2 test of heterogeneity. A high P value is good news because it suggests that the heterogeneity is insignificant and that one can go ahead and summarise the results.

We have broken the process of meta-analysis into six steps: (1) do a literature search; (2) decide on some inclusion criteria and apply them; (3) calculate effect sizes for each study to be included; (4) do the basic meta-analysis; (5) consider doing some more advanced analysis such as publication bias analysis and …

Meta-analyses are conducted to assess the strength of evidence present on a disease and treatment. One aim is to determine whether an effect exists; another aim is to determine whether the effect is positive or negative and, ideally, to obtain a single summary estimate of the effect.

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies.