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Table 1 Subgroup analyses in Cochrane reviews

From: Age-treatment subgroup analyses in Cochrane intervention reviews: a meta-epidemiological study

In meta-analyses, it is possible to formally test whether an intervention has different effects across subgroups based on patient, intervention, or study characteristics. When investigating subgroup differences in individual trials, subgroup analyses are based on within-trial comparisons. In other words, the subgroups of patients being compared come from the same study and population [35]. In meta-analyses, comparisons can be either within or between-trials. Within-trial comparisons are only possible if meta-analyses have access to individual participant level data and some of the trials contribute patients from every subgroup level considered [35]. However, subgroup analyses in meta-analyses are traditionally based on between-study comparisons, where certain trials only contribute data to one subgroup level [35]. When conducting a subgroup analyses for a meta-analyses, the two main steps are as follows: (1) calculating the effects within each subgroup level and (2) comparing the summary effects across the subgroup levels, using either fixed or random models within and between subgroups (e.g., a study can have random-effects within, and fixed effects between) [36]. In RevMan, the official Cochrane review software, a formal test for interaction is conducted using Cochran’s Q test, which tests the null hypothesis that the subgroup effects are the same and that any variation is no more than what would be expected by chance alone. The results of tests for interaction are typically present at the bottom of forest plots, with the text “Test for subgroup differences” [37]. If subgroup analyses are based on between-trial comparisons, the trials contributing data to different subgroup levels can have different patient, intervention, or study characteristics, which can complicate the conclusions from an interaction test [35].