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META-ANALYSIS: COMBINING EVIDENCE FROM MULTIPLE STUDIES

Scientific evidence relevant to causal relationships between exposure and disease comes from different types of investigation, including randomized clinical trials (RCTs) on humans, epidemiologic studies, animal experiments, and cell studies, and also from fundamental biological knowledge. We use the term human studies to refer to RCTs or observational studies involving people. Although an evidence-based approach must combine all forms of scientific evidence, in this section we limit our discussion to the problem of synthesizing the information from multiple human studies.

The idea of pooling information from multiple studies has a long tradition in statistics that goes back at least to Karl Pearson in 1904. A meta-analysis involves gathering all studies with evidence related to a particular question, and statistically combining the results of these studies. In many contexts, health researchers have mathematically combined the results from multiple, yet comparable RCTs to derive a summary estimate of the effect of some substance on health; the estimate appropriately combines the results of all the individual studies. Such summaries are often carried out, for example, to determine if there is a benefit of a drug or perhaps an excess occurrence of an unwanted side effect. One approach for combining evidence, random effects meta-analysis, allows for heterogeneity between studies; with this technique, a meta-analysis is not strictly limited to studies involving similar populations.

In observational studies, there may be more variability in findings from study to study because study variables are not under the investigator’s control. The populations studied may vary considerably in their characteristics, and the variables measured as covariates for statistical adjustment may also differ. Nevertheless, meta-analysis is applied to observational study results as well as to RCT data. Meta-regression (Greenland and O’Rourke, 2001) allows pooling of data across observational studies with some unexplained heterogeneity, and recent work by E. Kaizar (2005) improves on meta-regression for situations with data available from both RCTs and observational studies.

Although the development of meta-analytic methods has generated extensive methodological discussion (see, for example, Berlin and Antman, 1994; Berlin and Chalmers, 1988; Dickersin and Berlin, 1992; Greenland, 1994a,b; Stram, 1996; Stroup et al., 2000), it is a technique that can be quite useful when there are multiple studies on the same question. For example, for each of a number of different cancers, the 2006 IOM Committee on Asbestos and Selected Cancers (IOM, 2006a) did a quantitative meta-analysis on studies that combined the effect of asbestos exposure on risk based on multiple studies for each of a set of cancers. The report pre-



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