Title | Functional Linear Regression Models for Nonignorable Missing Scalar Responses. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Li, Tengfei, Fengchang Xie, Xiangnan Feng, Joseph G. Ibrahim, and Hongtu Zhu |
Journal | Stat Sin |
Volume | 28 |
Issue | 4 |
Pagination | 1867-1886 |
Date Published | 2018 Oct |
ISSN | 1017-0405 |
Abstract | 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. |
DOI | 10.5705/ss.202016.0350 |
Alternate Journal | Stat Sin |
Original Publication | Functional linear regression models for nonignorable missing scalar responses. |
PubMed ID | 30344426 |
PubMed Central ID | PMC6191855 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States R21 AG033387 / AG / NIA NIH HHS / United States |