title: Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models.
abstract: Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intra-class correlation coefficient (I2C2) is generalized and the graphical intra-class correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probit-linear mixed effect models. A Markov Chain Monte Carlo EM (mcm-cEM) algorithm is used for estimating the GICC. Simulation results with varied settings are demonstrated and our method is applied to the KIRBY21 test-retest dataset.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA, 21205.
authors.forename: Chen
authors.initials: C
authors.lastname: Yue
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA, 21205.
authors.forename: Shaojie
authors.initials: S
authors.lastname: Chen
authors.affiliation: Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21205.
authors.forename: Haris I
authors.initials: HI
authors.lastname: Sair
authors.affiliation: Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21205.
authors.forename: Raag
authors.initials: R
authors.lastname: Airan
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA, 21205.
authors.forename: Brian S
authors.initials: BS
authors.lastname: Caffo
keywords: MCMCEM; graphical intra class correlation coefficient; multivariate probit-linear mixed model