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Abstract:
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As advanced platforms produce data in a higher and higher resolution, a large collection of datasets are made available from different studies with possibly various resolutions. These multi-resolution datasets are incompatible in the sense that a predictor in a low-resolution dataset corresponds to a group of predictors in a high-resolution dataset. This incompatible but nested data structure poses new challenges to the existent statistical methods for data integration. This paper proposes a new statistical regularization approach that can integratively analyze multiple datasets with different resolutions. This approach not only enables joint estimation of model parameters but also ensures consistent findings across multiple studies. Simulation studies illustrate the advantage of the proposed integrative analysis in terms of its consistent findings and its enhanced statistical power compared to separate analyses. Meanwhile, an integrative analysis of multi-resolution genetic datasets shows the applicability and efficacy of the proposed method when applied to genetic association studies.
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