Functional Linear Regression Models for Nonignorable Missing Scalar Responses.

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 st

SUBMITTER: Li T 

PROVIDER: S-EPMC6191855 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature