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Publication Detail

Title: cit: hypothesis testing software for mediation analysis in genomic applications.

Authors: Millstein, Joshua; Chen, Gary K; Breton, Carrie V

Published In Bioinformatics, (2016 08 01)

Abstract: The challenges of successfully applying causal inference methods include: (i) satisfying underlying assumptions, (ii) limitations in data/models accommodated by the software and (iii) low power of common multiple testing approaches.The causal inference test (CIT) is based on hypothesis testing rather than estimation, allowing the testable assumptions to be evaluated in the determination of statistical significance. A user-friendly software package provides P-values and optionally permutation-based FDR estimates (q-values) for potential mediators. It can handle single and multiple binary and continuous instrumental variables, binary or continuous outcome variables and adjustment covariates. Also, the permutation-based FDR option provides a non-parametric implementation.Simulation studies demonstrate the validity of the cit package and show a substantial advantage of permutation-based FDR over other common multiple testing strategies.The cit open-source R package is freely available from the CRAN website (https://cran.r-project.org/web/packages/cit/index.html) with embedded C ++ code that utilizes the GNU Scientific Library, also freely available (http://www.gnu.org/software/gsl/).joshua.millstein@usc.eduSupplementary data are available at Bioinformatics online.

PubMed ID: 27153715 Exiting the NIEHS site

MeSH Terms: Gene Library; Genome; Genomics*; Models, Theoretical; Software*

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