Meta-analysis is a class of techniques used to combine data from multiple, often disparate, studies on a given topic. Essentially the methodology involves reverse-engineering published literature or data from a website and then statistically combining the results. Of course, as with all statistical analyses, there are several ways of doing a meta-analysis, and within each way there are lots of smaller assumptions that affect the way a meta-analysis should be interpreted. Bias, especially publication bias, are primary worries.
In the article linked above, FDA reviewers are calling for restraint in the use of this tool, and for good reason. In the drive toward transparency and open data (or at least open results in our industry), coupled with the wide availability of statistical software, anybody can easily create a meta-analysis. The Vioxx and Avandia examples show that a meta-analysis can kick off a process of scrutiny that will eventually cause a drug to be pulled from the market or relegated to a "last resort" status. The ugly downside of this, of course, is that some drugs may be inappropriately targeted and its use inappropriately reduced due to market withdrawal, patient fears, or refusal of reimbursement. The reviewers note that Triotropium should not follow the path of Vioxx and Avandia despite a negative meta-analysis.
My comment is that they are absolutely right in that the meta-analysis is only one aspect of the whole picture. In the cases of Vioxx and Avandia, further investigations were made into the data, and these further investigations supported the original meta-analysis. It is not automatic, however, that a drug should be targeted for removal or usage reduction in light of a negative meta-analysis, but rather a more detailed analysis that includes the original approval data and any subsequent post-marketing data.