Meta-Analysis of ‘Sparse’ Data: Perspectives from the Avandia Cases by Michael Finkelstein and Bruce Levin.
Combining the results of multiple small trials to increase accuracy and statistical power, a technique called meta-analysis has become well established and increasingly important in medical studies, particularly in connection with new drugs. When the data are sparse, as they are in many such cases, certain accepted practices, applied reflexively by researchers, may be misleading because they are biased and for other reasons. We illustrate some of the problems by examining a meta-analysis of the connection between the diabetes drug Avandia (rosiglitazone) and myocardial infarction that was strongly criticized as misleading, but led to thousands of lawsuits being filed against the manufacturer and the FDA acting to restrict access to the drug. Our scrutiny of the Avandia meta-analysis is particularly appropriate because it plays an important role in ongoing litigation, has been sharply criticized, and has been subject to a more searching review in court than meta-analyses of other drugs.
A good introduction to the issues of meta-analysis, where the stakes for drug companies, can be quite high.
All clinical trials vary in some respects, the question with meta-analysis being is the variance enough (enough heterogeneity) to make meta-analysis invalid?
How would you measure heterogeneity or perhaps an experts claim of heterogeneity?