This is an archived version. For the current version, please go to training.cochrane.org/handbook/current.

Two methods of analysis are available in RevMan for meta-analysis of continuous data: the inverse-variance fixed-effect method and the inverse-variance random-effects method. The methods will give exactly the same answers when there is no heterogeneity. Where there is heterogeneity, confidence intervals for the average intervention effect will be wider if the random-effects method is used rather than a fixed-effect method, and corresponding P values will be less significant. It is also possible that the central estimate of the intervention effect will change if there are relationships between observed intervention effects and sample sizes. See Section 9.5.4 for further discussion of these issues.

Authors should be aware that an assumption underlying methods for meta-analysis of continuous data is that the outcomes have a normal distribution in each intervention arm in each study. This assumption may not always be met, although it is unimportant in very large studies. It is useful to consider the possibility of skewed data (see Section 9.4.5.3).