Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions

• URL: http://arxiv.org/abs/1912.11928v2
• Date: Thu, 30 Jun 2022 15:21:24 GMT
• Title: Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions
• Authors: Subha Maity, Yuekai Sun, and Moulinath Banerjee
• Abstract summary: We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical. We introduce a global parameter that emphasizes interpretability and statistical efficiency in the presence of heterogeneity. We demonstrate the benefits of our approach on a large-scale drug treatment dataset involving several different cancer cell-lines.
• Score: 21.162280861396205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical. To borrow strength across such heterogeneous datasets, we introduce a global parameter that emphasizes interpretability and statistical efficiency in the presence of heterogeneity. We also propose a one-shot estimator of the global parameter that preserves the anonymity of the data sources and converges at a rate that depends on the size of the combined dataset. For high-dimensional linear model settings, we demonstrate the superiority of our identification restrictions in adapting to a previously seen data distribution as well as predicting for a new/unseen data distribution. Finally, we demonstrate the benefits of our approach on a large-scale drug treatment dataset involving several different cancer cell-lines.

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