Meta-analysis: Beyond the grand mean?

In the previous two articles1 2 we outlined the potentials and principles of meta-analysis and the practical steps in performing a meta-analysis. Now we will examine how to use meta-analysis to do more than simply combine the results from all the individual trials into a single effect estimate. Firstly, we discuss the advantages and disadvantages of performing subgroup analyses. Secondly, we consider the situation in which the differences in effects between individual trials are related in a graded way to an underlying phenomenon, such as the degree of mortality risk of the trial participants. #### Summary points Meta-analysis can be used to examine differences in treatment effects across trials; however, the fact that randomised trials are included in meta-analyses does not mean that comparisons between trials are also randomised comparisons Meta-analytic subgroup analyses, like subgroup analyses within trials, are prone to bias and need to be interpreted with caution A more reliable way of assessing differences in treatment effects is to relate outcome to some underlying patient characteristic on a continuous, or ordered, scale The underlying level of risk is a key variable which is often related to a given treatment effect, with patients at higher risk receiving more benefit then low risk patients Individual patient data, rather than published summary statistics, are often required for meaningful subgroup analyses The main aim of a meta-analysis is to produce an estimate of the average effect seen in trials of a particular treatment. The direction and magnitude of this average effect is intended to guide decisions about clinical practice for a wide range of patients. Clinicians are thus being asked to treat their patients as though each one is well represented by the patients in the clinical trials included in the meta-analysis. This runs against doctors' concerns to use the specific characteristics of a …