Combining studies

Estimated intervention effects for different study designs can be expected to be influenced to varying degrees by different sources of bias (see Section 13.5). Results from different study designs should be expected to differ systematically, resulting in increased heterogeneity. Therefore, we recommend that NRS which used different study designs (or which have different design features), or randomized trials and NRS, should not be combined in a meta-analysis.


Because of the need to control for confounding as best as possible, the estimated intervention effect and its standard error (or confidence interval) are key pieces of information which should be used for pooling NRS in a meta-analysis. (Simple numerators and denominators, or means and standard errors, for intervention and control groups cannot control for confounding unless the groups have been matched at the design stage.) Consequently, meta-analysis methods based on estimates and standard errors, and in particular the generic inverse-variance method, will be suitable for NRS (see Chapter 9, Section 9.4.3).


It is straightforward to extract an adjusted effect estimate and its standard error for a meta-analysis if a single adjusted estimate is reported for a particular outcome in a primary NRS. However, many NRS report both unadjusted and adjusted effect estimates, and some NRS report multiple adjusted estimates from analyses including different sets of covariates. Review authors should record both unadjusted and adjusted effect estimates but it can be difficult to choose between alternative adjusted estimates. No general recommendation can be made for the selection of which adjusted estimate is preferable. Possible selection rules are:

Sensitivity analyses could be performed by pooling separately the most optimistic and pessimistic results from each included study.


There is a subtle statistical point regarding the different interpretation of adjusted and unadjusted effects when expressed as odds or hazard ratios. The unadjusted effect estimate is known as the population average effect, and if the estimate were unbiased would be the effect of intervention observed in a population with an average mixture of prognostic characteristics. When estimates are adjusted for prognostic characteristics, the estimated effects are known as conditional estimates and are the intervention effects that would be observed in groups with particular combinations of the adjusted covariates. Mathematical research has shown that conditional estimates are usually larger (further from an OR or HR of 1) than population average estimates.  This phenomenon may not be observed in systematic reviews due to heterogeneity in the estimates of the studies.