Medical and Social Science & Practice
The SBU newsletter presents and disseminates the results of the SBU reports, describes ongoing projects at the agency, informs about assessment projects at sister organisations, and promotes interest in scientific assessments and critical reviews of methods in health care and social services.
Syntheses of research findings occur in many disciplines  and have even become a separate field of research. Systematic reviews can provide valuable knowledge, such as in cases where individual studies are too small to provide reliable results. An overall picture is often more accurate.
An important statistical tool for conducting such work is meta-analysis (see sidebar). One advantage of combining several observations through meta-analysis is to increase statistical power, which makes it possible to demonstrate even minor differences in effect with acceptable statistical confidence – for example, a small but important difference in treatment efficacy between two methods.
But the purpose of meta-analysis is not always to mathematically synthesise the results. Sometimes the purpose is to investigate how the results of different studies vary.  In cases where this is the main reason, or when researchers focus on broad generalisations involving many different groups, the analysis may intentionally include studies from completely different categories of participants. 
In any event, meta-analysis is a tool that must be correctly and knowledgeably applied. And along with its rapid rise in popularity, a growing number of researchers are sounding the alarm regarding its careless misuse. [3,4] The overall picture will be misleading if aggregation and analysis of the findings of the studies are incorrectly handled. Moreover, because the methodology is so complex, there is also a risk of intentional manipulation. [3,4]
Consequently, systematic reviews using meta-analysis must be subjected to at least as careful scrutiny as other types of research – possibly even more, given that claims of validity are often greater.
For starters, not all compilations that are called meta-analyses truly meet the necessary criteria. For example, simply counting the number of studies that are “for” or “against” an intervention is not a meta-analytical method and may be directly misleading. Nevertheless, this type of “vote counting” is found in reviews.3 For example, some authors may try to substantiate their assumptions by counting the number of studies with statistically significant and non-significant results. But the finding that significant results outweigh non-significant results hardly constitutes evidentiary support.
One challenge in meta-analysis is to select a suitable model – fixed or random effects. The choice depends on the purpose of the analysis and how similar the participants in the various studies are deemed to be. If the participants are sufficiently similar, each study´s group of subjects can be thought of as a random sample of the larger population under investigation. In such cases, a synthesis of results contributes to achieving a clearer picture of the population at large, and the fixed effect model is used. However, should the studies differ to the point that participants can be considered to represent different populations, a random effects model should be used instead. In the latter case, the analysis results correspond to an average effect across all populations, which of course may deviate from the actual effect in a single population.
Meta-analysis also requires a review and ranking of data before they are synthesised. Well-established statistical methodology must be used when calculating effect size, weighting results from different studies and addressing any heterogeneity in the data.
Results are often weighted based on the width of the confidence intervals. The purpose is to be able to distinguish the uncertainty in individual studies from the uncertainty associated with the collective results.  Without weighting, it becomes difficult to assess how “robust” the aggregate results of the meta-analysis are as a whole, and how dependent they may be on certain included studies. Weighting also prevents small studies from having too much influence on the collective results (in the fixed effect model), which can otherwise be a problem – for two reasons.
One is that small studies are inherently more sensitive to random errors. The fewer observations made in a study, the greater the latitude for randomness. Studies with few participants are more sensitive to random effects – the results will vary more than in larger studies. 
Secondly, it is known that publication of small clinical trials with negative outcomes tend to be delayed or, in the worst-case scenario, fail to be published at all, in which case the findings remain unknown. This skews the overall picture of treatment efficacy, resulting in publication bias.  In fields of research dominated by small treatment studies, the overall picture of the beneficial effects of treatment therefore tends to be exaggerated.
Formerly a scarcity in the research literature from the 1990s, scientific journals are now veritably flooded with results from meta-analyses, many of which have been criticised as redundant, erroneous, or both. [4,5] The tendency for researchers to be opinionated regarding substantive issues may bias results, but this is hardly unique to meta-analysis. As with other approaches, researchers must make choices which may affect results. [4 ] Researchers must decide what types of studies to cover, how old they may be and what languages to include. The quality criteria used to cull studies may also vary in regard to both stringency and application.
For this reason, the scientific community must remain vigilant that researchers disclose their choices and explain their process. Authors must openly and clearly explain and motivate their decisions (transparency in reporting) in order for a meta-analysis to be considered reliable.
Technological developments in the field, such as machine learning and artificial intelligence, pose both opportunities and challenges. Broad access to advanced statistical analytical tools allows an ever-growing number of researchers to carry out increasingly complex calculations – without necessarily themselves possessing the knowledge or statistical expertise to do so. The more convoluted the analyses, the more difficult it becomes for researchers, reviewers and others to discover errors and detect bias.
One example is network meta-analysis – an advanced analytical method that is becoming increasingly common and which can easily yield erroneous findings.  This type of meta-analysis compares three or more treatments by combining both direct and indirect comparison results from various trials. While traditional meta-analysis only makes direct comparisons between interventions, network meta-analysis also makes indirect comparisons – including interventions that were never tested side by side within one and the same trial. In order to also compare interventions that were never tested directly head to head, effect estimates from trials that share a common comparator are used. For example, when A vs B is the comparison of interest, randomised trials on A vs C and on B vs C are used as indirect evidence. A large network meta-analysis may include more than 20 comparisons.
The extent to which use of network meta-analysis can at all be considered appropriate once again depends on how similar the studies are. Such an assessment requires knowledge of the subject and affects choice of statistical methodology – where the options are many. Various draft review templates for network meta-analysis have been published. [7-10]
An array of pitfalls must be avoided when conducting and interpreting meta-analyses, ranging from simple to highly complex. While meta-analysis has proven valuable as a statistical tool, it is often used incorrectly. A large proportion of published analyses have been deemed substandard. 
It is paramount to remember that meta-analytic tools in themselves are by no means a guarantee of quality. RL
1. Gough D, et al. Syst Rev. 2020;9:155.
2. Gurevitch J, et al. Nature 2018;555:175-82.
3. de Vrieze J. Science 2018;361:1184-8.
4. Ioannidis JPA. Milbank Q, 2016;94:485-514.
5. Leclercq V, et al. BMJ Open 2020;10:e036349.
6. Anttila S. SBU, Vetenskap & praxis, 2018:(1-2):12-3.
7. Nikolakopoulou A, et al. PLoS Med 2020;17:e1003082.
8. Puhan MA, et al. BMJ 2014;349:g5630.
9. Jansen J, et al. Value Health 2014;17:157-73.
10. Brignardello-Petersen R, et al. BMJ 2020;371:m3907