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9.5.3  Strategies for addressing heterogeneity

A number of options are available if (statistical) heterogeneity is identified among a group of studies that would otherwise be considered suitable for a meta-analysis.


1. Check again that the data are correct

Severe heterogeneity can indicate that data have been incorrectly extracted or entered into RevMan. For example, if standard errors have mistakenly been entered as standard deviations for continuous outcomes, this could manifest itself in overly narrow confidence intervals with poor overlap and hence substantial heterogeneity. Unit-of-analysis errors may also be causes of heterogeneity (see Section 9.3).


2. Do not do a meta-analysis

A systematic review need not contain any meta-analyses (O'Rourke 1989). If there is considerable variation in results, and particularly if there is inconsistency in the direction of effect, it may be misleading to quote an average value for the intervention effect.


3. Explore heterogeneity

It is clearly of interest to determine the causes of heterogeneity among results of studies. This process is problematic since there are often many characteristics that vary across studies from which one may choose. Heterogeneity may be explored by conducting subgroup analyses (see Section 9.6.3) or meta-regression (see Section 9.6.4), though this latter method is not implemented in RevMan. Ideally, investigations of characteristics of studies that may be associated with heterogeneity should be pre-specified in the protocol of a review (see Section 9.1.7). Reliable conclusions can only be drawn from analyses that are truly pre-specified before inspecting the studies’ results, and even these conclusions should be interpreted with caution. In practice, authors will often be familiar with some study results when writing the protocol, so true pre-specification is not possible. Explorations of heterogeneity that are devised after heterogeneity is identified can at best lead to the generation of hypotheses. They should be interpreted with even more caution and should generally not be listed among the conclusions of a review. Also, investigations of heterogeneity when there are very few studies are of questionable value.


4. Ignore heterogeneity

Fixed-effect meta-analyses ignore heterogeneity. The pooled effect estimate from a fixed-effect meta-analysis is normally interpreted as being the best estimate of the intervention effect. However, the existence of heterogeneity suggests that there may not be a single intervention effect but a distribution of intervention effects. Thus the pooled fixed-effect estimate may be an intervention effect that does not actually exist in any population, and therefore have a confidence interval that is meaningless as well as being too narrow, (see Section 9.5.4). The P value obtained from a fixed-effect meta-analysis does however provide a meaningful test of the null hypothesis that there is no effect in every study.


5. Perform a random-effects meta-analysis

A random-effects meta-analysis may be used to incorporate heterogeneity among studies. This is not a substitute for a thorough investigation of heterogeneity. It is intended primarily for heterogeneity that cannot be explained. An extended discussion of this option appears in Section 9.5.4.


6. Change the effect measure

Heterogeneity may be an artificial consequence of an inappropriate choice of effect measure. For example, when studies collect continuous outcome data using different scales or different units, extreme heterogeneity may be apparent when using the mean difference but not when the more appropriate standardized mean difference is used. Furthermore, choice of effect measure for dichotomous outcomes (odds ratio, relative risk, or risk difference) may affect the degree of heterogeneity among results. In particular, when control group risks vary, homogeneous odds ratios or risk ratios will necessarily lead to heterogeneous risk differences, and vice versa. However, it remains unclear whether homogeneity of intervention effect in a particular meta-analysis is a suitable criterion for choosing between these measures (see also Section


7. Exclude studies

Heterogeneity may be due to the presence of one or two outlying studies with results that conflict with the rest of the studies. In general it is unwise to exclude studies from a meta-analysis on the basis of their results as this may introduce bias. However, if an obvious reason for the outlying result is apparent, the study might be removed with more confidence. Since usually at least one characteristic can be found for any study in any meta-analysis which makes it different from the others, this criterion is unreliable because it is all too easy to fulfil. It is advisable to perform analyses both with and without outlying studies as part of a sensitivity analysis (see Section 9.7). Whenever possible, potential sources of clinical diversity that might lead to such situations should be specified in the protocol.