Concordance correlation coefficients estimated by variance components for longitudinal normal and Poisson data

Miao-Yu Tsai, Chao Chun Lin

Research output: Contribution to journalArticle

Abstract

The concordance correlation coefficient (CCC) is widely used to assess agreement between two observers for continuous responses. Further, the CCC is extended for measuring agreement with discrete data. This paper proposes a variance components (VC) approach that allows dependency between repeated measurements over time to assess intra-agreement for each observer and inter- and total agreement among multiple observers simultaneously under extended three-way generalized linear mixed-effects models (GLMMs) for longitudinal normal and Poisson data. Furthermore, we propose a weight matrix to compare with existing weight matrices. Simulation studies are conducted to compare the performance of the VC, generalized estimating equations and U-statistics approaches with different weight matrices for repeated measurements from longitudinal normal and Poisson data. Two applications, of myopia twin and of corticospinal diffusion tensor tractography studies, are used for illustration. In conclusion, the VC approach with consideration of the correlation structure of longitudinal repeated measurements gives satisfactory results with small mean square errors and nominal 95% coverage rates for all sample sizes.

Original languageEnglish
Pages (from-to)57-70
Number of pages14
JournalComputational Statistics and Data Analysis
Volume121
DOIs
Publication statusPublished - 2018 May 1

Fingerprint

Repeated Measurements
Variance Components
Concordance
Correlation coefficient
Observer
Siméon Denis Poisson
Linear Mixed Effects Model
Generalized Estimating Equations
Discrete Data
U-statistics
Correlation Structure
Mean square error
Tensors
Categorical or nominal
Sample Size
Coverage
Tensor
Statistics
Simulation Study

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

@article{06cd9eed8a594eca94e06383c3b964ba,
title = "Concordance correlation coefficients estimated by variance components for longitudinal normal and Poisson data",
abstract = "The concordance correlation coefficient (CCC) is widely used to assess agreement between two observers for continuous responses. Further, the CCC is extended for measuring agreement with discrete data. This paper proposes a variance components (VC) approach that allows dependency between repeated measurements over time to assess intra-agreement for each observer and inter- and total agreement among multiple observers simultaneously under extended three-way generalized linear mixed-effects models (GLMMs) for longitudinal normal and Poisson data. Furthermore, we propose a weight matrix to compare with existing weight matrices. Simulation studies are conducted to compare the performance of the VC, generalized estimating equations and U-statistics approaches with different weight matrices for repeated measurements from longitudinal normal and Poisson data. Two applications, of myopia twin and of corticospinal diffusion tensor tractography studies, are used for illustration. In conclusion, the VC approach with consideration of the correlation structure of longitudinal repeated measurements gives satisfactory results with small mean square errors and nominal 95{\%} coverage rates for all sample sizes.",
author = "Miao-Yu Tsai and Lin, {Chao Chun}",
year = "2018",
month = "5",
day = "1",
doi = "10.1016/j.csda.2017.12.003",
language = "English",
volume = "121",
pages = "57--70",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",

}

TY - JOUR

T1 - Concordance correlation coefficients estimated by variance components for longitudinal normal and Poisson data

AU - Tsai, Miao-Yu

AU - Lin, Chao Chun

PY - 2018/5/1

Y1 - 2018/5/1

N2 - The concordance correlation coefficient (CCC) is widely used to assess agreement between two observers for continuous responses. Further, the CCC is extended for measuring agreement with discrete data. This paper proposes a variance components (VC) approach that allows dependency between repeated measurements over time to assess intra-agreement for each observer and inter- and total agreement among multiple observers simultaneously under extended three-way generalized linear mixed-effects models (GLMMs) for longitudinal normal and Poisson data. Furthermore, we propose a weight matrix to compare with existing weight matrices. Simulation studies are conducted to compare the performance of the VC, generalized estimating equations and U-statistics approaches with different weight matrices for repeated measurements from longitudinal normal and Poisson data. Two applications, of myopia twin and of corticospinal diffusion tensor tractography studies, are used for illustration. In conclusion, the VC approach with consideration of the correlation structure of longitudinal repeated measurements gives satisfactory results with small mean square errors and nominal 95% coverage rates for all sample sizes.

AB - The concordance correlation coefficient (CCC) is widely used to assess agreement between two observers for continuous responses. Further, the CCC is extended for measuring agreement with discrete data. This paper proposes a variance components (VC) approach that allows dependency between repeated measurements over time to assess intra-agreement for each observer and inter- and total agreement among multiple observers simultaneously under extended three-way generalized linear mixed-effects models (GLMMs) for longitudinal normal and Poisson data. Furthermore, we propose a weight matrix to compare with existing weight matrices. Simulation studies are conducted to compare the performance of the VC, generalized estimating equations and U-statistics approaches with different weight matrices for repeated measurements from longitudinal normal and Poisson data. Two applications, of myopia twin and of corticospinal diffusion tensor tractography studies, are used for illustration. In conclusion, the VC approach with consideration of the correlation structure of longitudinal repeated measurements gives satisfactory results with small mean square errors and nominal 95% coverage rates for all sample sizes.

UR - http://www.scopus.com/inward/record.url?scp=85039859334&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85039859334&partnerID=8YFLogxK

U2 - 10.1016/j.csda.2017.12.003

DO - 10.1016/j.csda.2017.12.003

M3 - Article

AN - SCOPUS:85039859334

VL - 121

SP - 57

EP - 70

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

ER -