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About 13 million customers per month contact the West Coast customer service call center of the Bank of America, as reported by CIO Magazine's cover story on data mining in May 1998 [1]. In the past, each caller would have listened to the same marketing advertisement, whether or not it was relevant to the caller's interests. However, "rather than pitch the product of the week, we want to be as relevant as possible to each customer," states Chris Kelly, vice president and director of database marketing at Bank of America in San Francisco. Thus, Bank of America's customer service representatives have access to individual customer profiles, so that the customer can be informed of new products or services that may be of greatest

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Meta-analysis refers to quantitative methods to combine results from independent studies so as to draw overall conclusions. Frequently, results from dissimilar studies are inappropriately combined, resulting in suspect inferential synthesis. We present a straightforward method to identify and address this problem through the development of grouped random effect models for meta-analysis. We examine 15 comparative studies that investigate the efficacy of a new anti-epileptic drug, progabide. The flexibility of this modelling scheme is exemplified by the result that the open studies support the efficacy of progabide while the closed studies support the reverse hypothesis. Bayesian approaches for meta-analysis are preferable because of the small number of studies prevalent in meta-analysis. We specify diffuse proper prior and hyperprior distributions to assure posterior propriety. We investigate sensitivity of the posterior to choice of prior. We use Gibbs sampling and the Metropolis algorithm to generate samples from the relevant posteriors. We analyse posterior summaries and plots of model parameters to suggest solutions to questions of interest.