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Biometrics. 2016 Dec;72(4):1194-1205. doi: 10.1111/biom.12495. Epub 2016 Feb 22.

Estimation and testing for multiple regulation of multivariate mixed outcomes.

Author information

1
Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, U.S.A. 02115.
2
Brigham and Women's Hospital, Boston, Massachusetts, U.S.A. 02115.
3
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, U.S.A. 02115.

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.

KEYWORDS:

Familywise error control; Hierarchical lasso; Multiple phenotypes; Multiple regulation; Multiple testing; Resampling; Semiparametric models; Stepdown testing

PMID:
26910481
PMCID:
PMC5459434
DOI:
10.1111/biom.12495
[Indexed for MEDLINE]
Free PMC Article

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