"From the standpoint of general effectiveness in searching,... the searcher with the widest range of search strategies available is the searcher with the greatest retrieval power."
Marcia J. Bates,THE DESIGN OF BROWSING AND BERRYPICKING TECHNIQUES FOR THE ONLINE SEARCH INTERFACE, Graduate School of Library and Information Science, University of California at Los Angeles
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A systematic review attempts to collate all empirical evidence that fits pre-specified eligibility criteria in order to answer a specific research question. It uses explicit, systematic methods that are selected with a view to minimizing bias, thus providing more reliable findings from which conclusions can be drawn and decisions made (Antman 1992, Oxman 1993). The key characteristics of a systematic review are:
a clearly stated set of objectives with pre-defined eligibility criteria for studies;
an explicit, reproducible methodology;
a systematic search that attempts to identify all studies that would meet the eligibility criteria;
an assessment of the validity of the findings of the included studies, for example through the assessment of risk of bias; and
a systematic presentation, and synthesis, of the characteristics and findings of the included studies.
Many systematic reviews contain meta-analyses. Meta-analysis is the use of statistical methods to summarize the results of independent studies (Glass 1976). By combining information from all relevant studies, meta-analyses can provide more precise estimates of the effects of health care than those derived from the individual studies included within a review (see Chapter 9, Section 9.1.3). They also facilitate investigations of the consistency of evidence across studies, and the exploration of differences across studies.
The value a meta-analysis can add to a review depends on the context in which it is used, as described in Section 9.1.2. The following are reasons for considering including a meta-analysis in a review.
To increase power. Power is the chance of detecting a real effect as statistically significant if it exists. Many individual studies are too small to detect small effects, but when several are combined there is a higher chance of detecting an effect.
To improve precision. The estimation of an intervention effect can be improved when it is based on more information.
To answer questions not posed by the individual studies. Primary studies often involve a specific type of patient and explicitly defined interventions. A selection of studies in which these characteristics differ can allow investigation of the consistency of effect and, if relevant, allow reasons for differences in effect estimates to be investigated.
To settle controversies arising from apparently conflicting studies or to generate new hypotheses. Statistical analysis of findings allows the degree of conflict to be formally assessed, and reasons for different results to be explored and quantified.
Of course, the use of statistical methods does not guarantee that the results of a review are valid, any more than it does for a primary study.Moreover, like any tool, statistical methods can be misused.