Robust Score Tests With Missing Data in Genomics Studies.

TitleRobust Score Tests With Missing Data in Genomics Studies.
Publication TypeJournal Article
Year of Publication2019
AuthorsWong, Kin Yau, Donglin Zeng, and D Y. Lin
JournalJ Am Stat Assoc
Date Published2019

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 paper, 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.

Alternate JournalJ Am Stat Assoc
Original PublicationRobust score tests with missing data in genomics studies
PubMed ID31920211
PubMed Central IDPMC6951249
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
R01 HG009974 / HG / NHGRI NIH HHS / United States