Functional Linear Regression Models for Nonignorable Missing Scalar Responses.

TitleFunctional Linear Regression Models for Nonignorable Missing Scalar Responses.
Publication TypeJournal Article
Year of Publication2018
AuthorsLi, Tengfei, Fengchang Xie, Xiangnan Feng, Joseph G. Ibrahim, and Hongtu Zhu
JournalStat Sin
Date Published2018 Oct

As an important part of modern health care, medical imaging data, which can be regarded as densely sampled functional data, have been widely used for diagnosis, screening, treatment, and prognosis, such as finding breast cancer through mammograms. The aim of this paper is to propose a functional linear regression model for using functional (or imaging) predictors to predict clinical outcomes (e.g., disease status), while addressing missing clinical outcomes. We introduce an exponential tilting semiparametric model to account for the nonignorable missing data mechanism. We develop a set of estimating equations and its associated computational methods for both parameter estimation and the selection of the tuning parameters. We also propose a bootstrap resampling procedure for carrying out statistical inference. Under some regularity conditions, we systematically establish the asymptotic properties (e.g., consistency and convergence rate) of the estimates calculated from the proposed estimating equations. Simulation studies and a real data analysis are used to illustrate the finite sample performance of the proposed methods.

Alternate JournalStat Sin
Original PublicationFunctional linear regression models for nonignorable missing scalar responses.
PubMed ID30344426
PubMed Central IDPMC6191855
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
R21 AG033387 / AG / NIA NIH HHS / United States