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Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints.

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  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, USA.

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Summary
This summary is machine-generated.

This study introduces a novel data shielding integrative large-scale testing (DSILT) method for high-dimensional regression. DSILT effectively detects signals across studies with heterogeneity, even without sharing individual data, improving association analysis power.

Keywords:
DebiasingDistributed learningFalse discovery rateHigh dimensional inferenceIntegrative analysisMultiple testing

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • High-dimensional regression requires identifying informative predictors, but signal detection is often limited by small sample sizes.
  • Meta-analysis of multiple studies can improve power but faces challenges with between-study heterogeneity and data sharing constraints.
  • Existing methods struggle with integrative analysis of high-dimensional data when only summary data is available.

Purpose of the Study:

  • To propose a novel data shielding integrative large-scale testing (DSILT) approach for signal detection in high-dimensional regression.
  • To address challenges of between-study heterogeneity and data sharing constraints in multi-site analyses.
  • To develop a robust method for identifying significant covariate effects while controlling false discovery rates.

Main Methods:

  • Proposed a data shielding integrative large-scale testing (DSILT) approach designed for high-dimensional regression with between-study heterogeneity.
  • Developed integrative estimation and debiasing procedures to construct test statistics for overall covariate effects without individual data sharing.
  • Implemented a multiple testing procedure to control the false discovery rate (FDR) and false discovery proportion (FDP).

Main Results:

  • DSILT allows for between-study heterogeneity and does not require individual-level data sharing.
  • The method successfully constructs test statistics for overall effects of covariates, assuming shared support across studies.
  • Simulation studies confirmed the procedure's effectiveness in controlling false discovery and achieving high power.

Conclusions:

  • The DSILT approach offers a powerful solution for signal detection in high-dimensional regression meta-analysis under data sharing constraints.
  • The method performs well in controlling false discoveries and maintaining statistical power, outperforming other distributed inference methods.
  • Applied to a real-world example, DSILT effectively detected interaction effects of genetic variants on type II diabetes risk.