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Identifying informative predictors in a high dimensional regression model isa critical step for association analysis and predictive modeling. Signaldetection in the high dimensional setting often fails due to the limited samplesize. One approach to improve power is through meta-analyzing multiple studieson the same scientific question. However, integrative analysis of highdimensional data from multiple studies is challenging in the presence ofbetween study heterogeneity. The challenge is even more pronounced withadditional data sharing constraints under which only summary data but notindividual level data can be shared across different sites. In this paper, wepropose a novel data shielding integrative large-scale testing (DSILT) approachto signal detection by allowing between study heterogeneity and not requiringsharing of individual level data. Assuming the underlying high dimensionalregression models of the data differ across studies yet share similar support,the DSILT approach incorporates proper integrative estimation and debiasingprocedures to construct test statistics for the overall effects of specificcovariates. We also develop a multiple testing procedure to identifysignificant effects while controlling for false discovery rate (FDR) and falsediscovery proportion (FDP). Theoretical comparisons of the DSILT procedure withthe ideal individual--level meta--analysis (ILMA) approach and otherdistributed inference methods are investigated. Simulation studies demonstratethat the DSILT procedure performs well in both false discovery control andattaining power. The proposed method is applied to a real example on detectinginteraction effect of the genetic variants for statins and obesity on the riskfor Type 2 Diabetes.

Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints