![]() tau) is the estimated standard deviation of underlying effects across studies. In common with other meta-analysis software, RevMan presents an estimate of the between-study variance in a random-effects meta-analysis (known as tau-squared (τ 2 or Tau 2)). For example, when there are many studies in a meta-analysis, one may obtain a tight confidence interval around the random-effects estimate of the mean effect even when there is a large amount of heterogeneity. It does not describe the degree of heterogeneity among studies as may be commonly believed. The confidence interval from a random-effects meta-analysis describes uncertainty in the location of the mean of systematically different effects in the different studies. Often the pooled estimate and its confidence interval are quoted in isolation as an alternative estimate of the quantity evaluated in a fixed-effect meta-analysis, which is inappropriate. It is always advisable to explore possible causes of heterogeneity, although there may be too few studies to do this adequately (see Section 9.6).įor random-effects analyses in RevMan, the pooled estimate and confidence interval refer to the centre of the distribution of intervention effects, but do not describe the width of the distribution. Note that a random-effects model does not ‘take account’ of the heterogeneity, in the sense that it is no longer an issue. The importance of the particular assumed shape for this distribution is not known. It is difficult to establish the validity of any distributional assumption, and this is a common criticism of random-effects meta-analyses. The conventional choice of distribution is a normal distribution. The centre of this distribution describes the average of the effects, while its width describes the degree of heterogeneity. The model represents our lack of knowledge about why real, or apparent, intervention effects differ by considering the differences as if they were random. A random-effects meta-analysis model involves an assumption that the effects being estimated in the different studies are not identical, but follow some distribution. When there is heterogeneity that cannot readily be explained, one analytical approach is to incorporate it into a random-effects model. that there is no statistical heterogeneity. This assumption implies that the observed differences among study results are due solely to the play of chance, i.e. ![]() In order to calculate a confidence interval for a fixed-effect meta-analysis the assumption is made that the true effect of intervention (in both magnitude and direction) is the same value in every study (that is, fixed across studies). 9.5.4 Incorporating heterogeneity into random-effects modelsĪ fixed-effect meta-analysis provides a result that may be viewed as a ‘typical intervention effect’ from the studies included in the analysis. For the current version, please go to /handbook/current or search for this chapter here. This is an archived version of the Handbook. ![]()
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