The Annals of Applied Statistics

Two-stage empirical likelihood for longitudinal neuroimaging data

Xiaoyan Shi, Joseph G. Ibrahim, Jeffrey Lieberman, Martin Styner, Yimei Li, and Hongtu Zhu

Full-text: Open access

Abstract

Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders, substance use disorders, and the normal brain. The main objective of this paper is to develop a two-stage adjusted exponentially tilted empirical likelihood (TETEL) for the spatial analysis of neuroimaging data from longitudinal studies. The TETEL method as a frequentist approach allows us to efficiently analyze longitudinal data without modeling temporal correlation and to classify different time-dependent covariate types. To account for spatial dependence, the TETEL method developed here specifically combines all the data in the closest neighborhood of each voxel (or pixel) on a 3-dimensional (3D) volume (or 2D surface) with appropriate weights to calculate adaptive parameter estimates and adaptive test statistics. Simulation studies are used to examine the finite sample performance of the adjusted exponential tilted likelihood ratio statistic and TETEL. We demonstrate the application of our statistical methods to the detection of the difference in the morphological changes of the hippocampus across time between schizophrenia patients and healthy subjects in a longitudinal schizophrenia study.

Article information

Source
Ann. Appl. Stat., Volume 5, Number 2B (2011), 1132-1158.

Dates
First available in Project Euclid: 13 July 2011

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1310562716

Digital Object Identifier
doi:10.1214/11-AOAS480

Mathematical Reviews number (MathSciNet)
MR2849769

Zentralblatt MATH identifier
1223.62024

Keywords
Hippocampus shape longitudinal data time-dependent covariate two-stage adjusted exponentially tilted empirical likelihood

Citation

Shi, Xiaoyan; Ibrahim, Joseph G.; Lieberman, Jeffrey; Styner, Martin; Li, Yimei; Zhu, Hongtu. Two-stage empirical likelihood for longitudinal neuroimaging data. Ann. Appl. Stat. 5 (2011), no. 2B, 1132--1158. doi:10.1214/11-AOAS480. https://projecteuclid.org/euclid.aoas/1310562716


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