![]() Our goals for this paper are to define the IDA framework for pooled data analysis describe the advantages and limitations of this approach discuss sampling, measurement and hypothesis testing within the IDA framework and provide future directions for IDA applications and methodological development. IDA is a framework for conducting the simultaneous analysis of raw data pooled from multiple studies. We offer to this toolkit an approach which we call Integrative Data Analysis (IDA). Together, however, they form a toolkit for researchers interested in analyzing pooled data. Not surprisingly, these methods may share little in their analytics beyond the common goal of data pooling. 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). 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. 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. In this review, we outline potential solutions to these challenges and describe future avenues for developing IDA as a framework for studies in clinical psychology. There are also methodological challenges associated with IDA, including the need to account for sampling heterogeneity across studies, to develop commensurate measures across studies, and to account for multiple sources of study differences as they impact hypothesis testing. ![]() ![]() Integrative Data Analysis (IDA), a novel framework for conducting the simultaneous analysis of raw data pooled from multiple studies, offers many advantages including economy (i.e., reuse of extant data), power (i.e., large combined sample sizes), the potential to address new questions not answerable by a single contributing study (e.g., combining longitudinal studies to cover a broader swath of the lifespan), and the opportunity to build a more cumulative science (i.e., examining the similarity of effects across studies and potential reasons for dissimilarities).
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