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Literature reviews designed to summarize large volumes of information are frequently published. When a review is done systematically, following certain criteria, and the results are pooled and analyzed quantitatively, it is called a meta-analysis. A well-designed and – interpreted meta-analysis can provide valuable information for decision making. However, there are several critical caveats in performing and interpreting them.

The information generated in medical research has tremendously increased in recent years. New studies are constantly being published, and clinicians, researchers, and policy makers may find it nearly impossible to stay current. More and more review articles that pool the results of multiple studies are seen.

Combining available information seems reasonable and can save considerable time, effort, and money. Nowadays, meta-analyses are used to design future research, to provide evidence in the regulatory process, and especially to modify clinical decision making. A meta-analysis is powerful but also controversial because several conditions are critical, and small violations of those conditions can lead to misleading results. Under scrutiny, some meta-analyses have been inappropriate and their conclusions not fully warranted. This entry covers basic concepts of meta-analysis and discusses its caveats.

Main Aims of a Meta-Analysis

The main aims of a meta-analysis are as follows:

  • To summarize results from several individual studies
  • To evaluate differences in the results among studies
  • To overcome small sample sizes of individual studies
  • To increase precision in estimating effects
  • To evaluate effects in subsets of patients
  • To determine if new studies are needed to further investigate a topic

Critical Issues in Performing a Meta-Analysis

Identification and Selection of Studies

Two phases need to be followed when selecting studies for a meta-analysis: (1) the literature search, in which potential studies are identified; and (2) the clear definition of inclusion criteria. Three problems affect these phases: publication bias and search bias in the former, and selection bias in the latter.

Publication Bias

Searches of databases can yield many studies. However, these databases include only published studies. Such searches are unlikely to yield a representative sample because studies that show a positive result (usually in favor of a new or standard treatment) are more likely to be published. This selective publication is called publication bias. Consider the case of the publication status of studies on antidepressants. Based on studies registered with the FDA, 97% of the positive studies are published versus only 12% of the negative ones. Furthermore, when the nonpublished studies are not included, the positive effects of individual drugs increase between 11% and 69%.

One reason for publication bias is that drug manufacturers are not generally interested in publishing negative studies. Also, journal editors favor positive studies because these are the ones that make the headlines. To ameliorate the effect of publication bias, a serious effort should be made to identify unpublished studies. This is much easier now due to improved communication between researchers and by registries in which all the studies of a certain disease or treatment are reported. In some medical areas, the exclusion of studies conducted in non-English-speaking countries can increase publication bias.

The National Institutes of Health maintains a registry of all the studies it supports, and the U.S. Food and Drug Administration keeps a registry and database in which drug companies must register all trials they sponsor. Registries of published and unpublished trials supported by pharmaceutical companies are also available (e.g., GlaxoSmithKline's Clinical Study Register). The Cochrane collaboration keeps records of systematic reviews and meta-analyses of many diseases and interventions.

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