Functional Linear Regression Models for Nonignorable Missing Scalar Responses.

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.

Single-nucleotide polymorphisms are associated with cognitive decline at Alzheimer's disease conversion within mild cognitive impairment patients.

INTRODUCTION: The growing public threat of Alzheimer's disease (AD) has raised the urgency to quantify the degree of cognitive decline during the conversion process of mild cognitive impairment (MCI) to AD and its underlying genetic pathway. The aim of this article was to test genetic common variants associated with accelerated cognitive decline after the conversion of MCI to AD. METHODS: In 583 subjects with MCI enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI; ADNI-1, ADNI-Go, and ADNI-2), 245 MCI participants converted to AD at follow-up. We tested the interaction effects between individual single-nucleotide polymorphisms and AD diagnosis trajectory on the longitudinal Alzheimer's Disease Assessment Scale-Cognition scores. RESULTS: Our findings reveal six genes, including BDH1, ST6GAL1, RAB20, PDS5B, ADARB2, and SPSB1, which are directly or indirectly related to MCI conversion to AD. DISCUSSION: This genome-wide association study sheds light on a genetic mechanism of longitudinal cognitive changes during the transition period from MCI to AD.

Assessing influence for pharmaceutical data in zero-inflated generalized Poisson mixed models.

For clustered count data with excess zeros where the observations are either over-dispersed or under-dispersed, the zero-inflated generalized Poisson mixed (ZIGPM) regression model may be appropriate, in which the baseline discrete distribution is a generalized Poisson distribution, which is a natural extension of standard Poisson distribution. Motivated by one data set drawn from a pharmaceutical study, influence diagnostics for ZIGPM models based on case-deletion and local influence analysis are developed in this work. The one-step approximations of the estimates under case-deletion model and some case-deletion measures are given. Meanwhile, local influence measures are obtained under various perturbations of the observed data or model assumptions. Results from a pharmaceutical study illustrate the usefulness of the diagnostic statistics.