With the accrual of high quality databases, both within our national archives and individual laboratories, and the economic pressures of big science research to do more with less, the scientific community is looking for innovative methods that leverage existing resources to answer novel questions. Responsive to this call, methodologists from different fields are developing multiple approaches for pooled data analysis that combine information collected across multiple studies into a single analytic design. These methods have been used to examine the efficacy of medications versus cognitive behavior therapy for severe depression (DeRubeis et al., 1999), the relation between fat-intake and breast cancer (Hunter et al., 1996), the pharmacogenetics of tardive dyskinesia (Lerer et al., 2002), the relation of height, weight and breast cancer risk (van den Brandt et al., 2000), and the mediators of Fluoxetine effects on youth suicidal ideation (Gibbons et al., 2012). Not surprisingly, these methods may share little in their analytics beyond the common goal of data pooling. Together, however, they form a toolkit for researchers interested in analyzing pooled data. We offer to this toolkit an approach
