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Estimation and testing for multiple regulation of multivariate mixed outcomes

Author

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  • Denis Agniel
  • Katherine P. Liao
  • Tianxi Cai

Abstract

Considerable interest has recently been focused on studying multiple phenotypes simultaneously in both epidemiological and genomic studies, either to capture the multidimensionality of complex disorders or to understand shared etiology of related disorders. We seek to identify multiple regulators or predictors that are associated with multiple outcomes when these outcomes may be measured on very different scales or composed of a mixture of continuous, binary, and not‐fully observed elements. We first propose an estimation technique to put all effects on similar scales, and we induce sparsity on the estimated effects. We provide standard asymptotic results for this estimator and show that resampling can be used to quantify uncertainty in finite samples. We finally provide a multiple testing procedure which can be geared specifically to the types of multiple regulators of interest, and we establish that, under standard regularity conditions, the familywise error rate will approach 0 as sample size diverges. Simulation results indicate that our approach can improve over unregularized methods both in reducing bias in estimation and improving power for testing.

Suggested Citation

  • Denis Agniel & Katherine P. Liao & Tianxi Cai, 2016. "Estimation and testing for multiple regulation of multivariate mixed outcomes," Biometrics, The International Biometric Society, vol. 72(4), pages 1194-1205, December.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1194-1205
    DOI: 10.1111/biom.12495
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    References listed on IDEAS

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    Cited by:

    1. Denis Agniel & Tianxi Cai, 2017. "Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis," Biometrics, The International Biometric Society, vol. 73(4), pages 1254-1265, December.

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