Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81924
Title: Robust score tests with missing data in genomics studies
Authors: Wong, KY 
Zeng, D
Lin, DY
Issue Date: 2019
Source: Journal of the American Statistical Association, 2019, v. 114, no. 528, p. 1778-1786
Abstract: Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this article, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms, even when the imputation model is misspecified. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic with a consistent variance estimator using sieve approximation theory and empirical process theory. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study. Supplementary materials for this article are available online.
Keywords: Association tests
Imputation
Integrative analysis
Multiple genomics platforms
Semiparametric models
Sieve estimation
Publisher: Taylor & Francis
Journal: Journal of the American Statistical Association 
ISSN: 0162-1459
EISSN: 1537-274X
DOI: 10.1080/01621459.2018.1514304
Rights: © 2018 American Statistical Association
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 26 Feb 2019, available online: http://www.tandfonline.com/10.1080/01621459.2018.1514304.
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