A robust permutation test for the concordance correlation coefficient

Hutson, Alan D.; Yu, Han

doi:10.1002/pst.2101

Cited by 9 publications

(8 citation statements)

References 24 publications

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“…Noting that the pairwise global inter-CCC can be written as that is, the functional correlation coefficient 9 ρ j j ′ scaled by κ j j ′, one can conduct a permutation test, similar in spirit to that for the correlation, to test this hypothesis. 22 However, performing a permutation test using normalGCCC ^ inter , j j ′ may result in an inflated Type I error rate as permutation and sampling distributions converge to different distributions. 23 One remedy is to perform the test on the studentized statistic 22 S ^ j j ′ = n normalGCCC ^ inter , j j ′ / τ ^ j j ′, where τ ^ j j ′ = ∫ falsefalse{ σ ^ B j 2 false( t false) + σ ^ B j ′ 2 false( t false) + 2 σ ^ j j ′ 2 false( …”

Section: Methodsmentioning

confidence: 99%

“…22 However, performing a permutation test using normalGCCC ^ inter , j j ′ may result in an inflated Type I error rate as permutation and sampling distributions converge to different distributions. 23 One remedy is to perform the test on the studentized statistic 22 S ^ j j ′ = n normalGCCC ^ inter , j j ′ / τ ^ j j ′, where τ ^ j j ′ = ∫ falsefalse{ σ ^ B j 2 false( t false) + σ ^ B j ′ 2 false( t false) + 2 σ ^ j j ′ 2 false( t false) falsefalse} d t / falsefalse{ ∫ σ ^ B j 2 false( t false) d t ⋅ ∫ σ ^ B j ′ 2 false( t false) d t …”

Section: Methodsmentioning

confidence: 99%

“…that is, the functional correlation coefficient 9 𝜌 jj ′ scaled by 𝜅 jj ′ , one can conduct a permutation test, similar in spirit to that for the correlation, to test this hypothesis. 22 However, performing a permutation test using ĜCCC inter,jj ′ may result in an inflated Type I error rate as permutation and sampling distributions converge to different distributions. 23 One remedy is to perform the test on the studentized statistic 22…”

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confidence: 99%

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Assessing intra- and inter-method agreement of functional data

Yue,

Jang,

Manatunga

2023

Stat Methods Med Res

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Modern medical devices are increasingly producing complex data that could offer deeper insights into physiological mechanisms of underlying diseases. One type of complex data that arises frequently in medical imaging studies is functional data, whose sampling unit is a smooth continuous function. In this work, with the goal of establishing the scientific validity of experiments involving modern medical imaging devices, we focus on the problem of evaluating reliability and reproducibility of multiple functional data that are measured on the same subjects by different methods (i.e. different technologies or raters). Specifically, we develop a series of intraclass correlation coefficient and concordance correlation coefficient indices that can assess intra-method, inter-method, and total (intra + inter) agreement based on multivariate multilevel functional data consisting of replicated functional data measurements produced by each of the different methods. For efficient estimation, the proposed indices are expressed using variance components of a multivariate multilevel functional mixed effect model, which can be smoothly estimated by functional principal component analysis. Extensive simulation studies are performed to assess the finite-sample properties of the estimators. The proposed method is applied to evaluate the reliability and reproducibility of renogram curves produced by a high-tech radionuclide image scan used to non-invasively detect kidney obstruction.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Assessing intra- and inter-method agreement of functional data

Yue,

Jang,

Manatunga

2023

Stat Methods Med Res

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“…To avoid the influence of extreme values, we eliminated the FC value that exceeded two standard deviations. Moreover, to avoid the impact of sample size, a permutation test (36) was performed to investigate the altered FC of networks in ASD compared to TDC. And we also tried to detect the differences of FC between ASD and TD in two age groups, respectively.…”

Section: Functional Connectivity Analysismentioning

confidence: 99%

Self-Brain Networks in Toddlers with Autism Spectrum Disorder

Zuo,

Zhang,

Zhou

et al. 2024

Preprint

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As early as 18-24 months, self-processing has been reported abnormal in autism spectrum disorder (ASD), echoing a key period for the emergence of self-awareness. However, the neural mechanisms underlying this phenomenon remains unclear. The self-processing was supposed integrating three nested levels: the interoceptive-self which represents bodily internal state, the exteroceptive-self which links the internal and external environment and marks a key step in self-awareness development, the mental-self which represents the mental world built on the abstract relationship between the internal and external environment. Therefore, we assumed that ASD may show abnormal exteroceptive-self from 18-24 months and then develop the disorders of mental-self with their growing. To test this hypothesis, resting-state functional magnetic resonance imaging data from both ASD and typically developing controls (TDC) were collected and divided into two age groups: 18-24 months and 25-48 months. In ASD, functional connectivity revealed hyperconnectivity within exteroceptive-self network; graph theory analysis revealed atypical nodal topological properties in both exteroceptive- and mental-self network. Based on these findings, a support vector machine (SVM) model was trained to distinguish ASD from TDC for each age groups, and then validated with independent datasets. The SVM models achieved accuracy rates of 88.2% (training) and 75% (testing) for the 18-24 months group, and 86.7% (training) and 84.6% (testing) for the 25-48 months group while SVM models based on the non-self-related brain regions cannot achieve similar accuracy. These findings disclose the abnormal information processing within self-network in brains of toddlers with ASD, and provide potential neuroimaging markers for its early detection and future translation.

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“…In this case, the asymptotic test based large sample approximation will be a natural option, though it may have an inflated type I error rate when n is small. Alternatively, a permutation test about non-zero correlations under the null can be achieved by using a de-correlated sample, as introduced by [ 13 ]. This approach ensures type I error control for testing the non-zero concordance correlation coefficient even when n is as small as 10.…”

Section: Permutation Testing Aboutmentioning

confidence: 99%

Inferential procedures based on the weighted Pearson correlation coefficient test statistic

Hutson

2022

Journal of Applied Statistics

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In this note, we evaluated the type I error control of the commonly used t -test found in most statistical software packages for testing the hypothesis on vs. based on the sample weighted Pearson correlation coefficient. We found the type I error rate is severely inflated in general cases, even under bivariate normality. To address this issue, we derived the large sample variance of the weighted Pearson correlation. Based on this result, we proposed an asymptotic test and a set of studentized permutation tests. A comprehensive set of simulation studies with a range of sample sizes and a variety of underlying distributions were conducted. The studentized permutation test based on Fisher's Z statistic was shown to robustly control the type I error even in the small sample and non-normality settings. The method was demonstrated with an example data of country-level preterm birth rates.

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A robust permutation test for the concordance correlation coefficient

Cited by 9 publications

References 24 publications

Assessing intra- and inter-method agreement of functional data

Assessing intra- and inter-method agreement of functional data

Self-Brain Networks in Toddlers with Autism Spectrum Disorder

Inferential procedures based on the weighted Pearson correlation coefficient test statistic

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