Heterogeneity refers to the variability or differences that exist between studies in a meta-analysis. It indicates how much the results of individual studies differ from each other. If there is significant heterogeneity between the studies being combined, it can indicate that the studies are not measuring the same underlying effect, which can complicate the interpretation of the overall results.
There are three principal types of heterogeneity:
Statistical Heterogeneity:This type of heterogeneity involves differences in the observed treatment effects across studies. It can be quantified by statistical measures like the I² statistic or Cochran’s Q test, which assess the degree of inconsistency in the results.
Clinical Heterogeneity: This occurs when the studies being analysed differ in terms of their patient populations, interventions, or settings. For example, one study may investigate a drug in a population of older adults, while another study investigates the same drug in younger adults with different comorbidities.
Methodological Heterogeneity: This refers to differences in how the studies were conducted, such as study design, methods of measurement, and statistical analysis. For example, one study might use a randomised controlled trial (RCT) design, while another study uses a cohort design, which can lead to variability in the results.