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Journal articles on the topic 'Intraclass correlation coefficient'

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Published: 4 June 2021
Last updated: 6 February 2022

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1

Sedgwick, P. "Intraclass correlation coefficient." BMJ 346, mar22 1 (March 22, 2013): f1816. http://dx.doi.org/10.1136/bmj.f1816.

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2

Bhandary, Madhusudin K. "Bayes estimation of intraclass correlation coefficient." Communications in Statistics - Theory and Methods 29, no. 1 (January 2000): 79–93. http://dx.doi.org/10.1080/03610920008832470.

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3

SRIVASTAVA, M. S. "Estimation of the intraclass correlation coefficient." Annals of Human Genetics 57, no. 2 (May 1993): 159–65. http://dx.doi.org/10.1111/j.1469-1809.1993.tb00897.x.

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4

Kovačić, Jelena, and Veda Marija Varnai. "Intraclass Correlation Coefficient for Grouped Data." Epidemiology 25, no. 5 (September 2014): 769–70. http://dx.doi.org/10.1097/ede.0000000000000139.

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5

Müller, Reinhold, and Petra Büttner. "Intraclass correlation coefficient—methodology and applications." Controlled Clinical Trials 12, no. 5 (October 1991): 690. http://dx.doi.org/10.1016/0197-2456(91)90262-k.

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6

Looney, Marilyn A. "When Is the Intraclass Correlation Coefficient Misleading?" Measurement in Physical Education and Exercise Science 4, no. 2 (June 2000): 73–78. http://dx.doi.org/10.1207/s15327841mpee0402_3.

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7

Lee, Seung-A., and Dal-Ho Kim. "Bayesian Hypothesis Testing for Intraclass Correlation Coefficient." Communications for Statistical Applications and Methods 13, no. 3 (December 31, 2006): 551–66. http://dx.doi.org/10.5351/ckss.2006.13.3.551.

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8

Plaza-Puche, Ana B., Liberdade C. Salerno, Francesco Versaci, Daniel Romero, and Jorge L. Alio. "Clinical evaluation of the repeatability of ocular aberrometry obtained with a new pyramid wavefront sensor." European Journal of Ophthalmology 29, no. 6 (December 5, 2018): 585–92. http://dx.doi.org/10.1177/1120672118816060.

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Purpose:To evaluate the intrasubject repeatability of the ocular aberrometry obtained with a new ocular pyramidal aberrometer technology in a sample of normal eyes.Methods:A total of 53 healthy eyes of 53 subjects with ages ranging from 18 to 45 years were included in this study. In all cases, three consecutive acquisitions were obtained. Intrasubject repeatability of the measurements with a pyramidal aberrometer was calculated. Intrasubject repeatability for 4.0- and 6.0-mm pupils was evaluated within the subject standard deviation (Sw) and intraclass correlation coefficient.Results:Low values of the Swand intraclass correlation coefficient outcomes close to 1 were observed for the sphere and cylinder at 3.0-mm pupil size. Most low Swand intraclass correlation coefficient values close to 1 were observed for total, low-order aberrations and higher-order aberrations root mean square and for each Zernike coefficient analysis (intraclass correlation coefficient ⩾0.798) at 4.0-mm pupil size, with more limited outcomes for the aberrometric coefficient of Z(4, 4) with an intraclass correlation coefficient of 0.683. For a 6.0 mm pupil diameter, low Swand intraclass correlation coefficient values close to 1 were observed for all aberrometric parameters or Zernike coefficients analyzed (intraclass correlation coefficient ⩾0.850).Conclusion:The new pyramidal aberrometer Osiris provides repeatable and consistent measurements of ocular aberrometry measurements in normal eyes.
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9

Giraudeau, Bruno, Alain Mallet, and Claude Chastang. "Case Influence on the Intraclass Correlation Coefficient Estimate." Biometrics 52, no. 4 (December 1996): 1492. http://dx.doi.org/10.2307/2532864.

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10

Bansal, Naveen K., and M. Bhandary. "ROBUST M-ESTIMATION OF THE INTRACLASS CORRELATION COEFFICIENT." Australian Journal of Statistics 36, no. 3 (September 1994): 287–301. http://dx.doi.org/10.1111/j.1467-842x.1994.tb00882.x.

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11

Bangdiwala, Shrikant I. "Are injuries correlated? Interpreting the intraclass correlation coefficient." International Journal of Injury Control and Safety Promotion 18, no. 2 (June 2011): 169–71. http://dx.doi.org/10.1080/17457300.2011.578923.

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12

Kim, Dal-Ho. "Noninformative Priors for the Common Intraclass Correlation Coefficient." Communications for Statistical Applications and Methods 18, no. 2 (March 31, 2011): 189–99. http://dx.doi.org/10.5351/ckss.2011.18.2.189.

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13

Araújo, Jaqueline Alves, Tiago Peçanha, Fabiula Isoton Novelli, César Siqueira Aleixes Mello, Daniel Moreira-Gonçalves, Gisela Arsa, and Lucieli Teresa Cambri. "Reproducibility of Heart Rate Variability Indices at Post-maximal Exercise." International Journal of Sports Medicine 41, no. 08 (March 11, 2020): 512–19. http://dx.doi.org/10.1055/a-1114-6297.

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AbstractTo analyze whether heart rate variability is reproducible after maximal exercise, 11 men (22.1±3.2 years) performed four incremental exercise tests followed by passive or active recovery. There was high reliability (intraclass coefficient correlation: 0.72–0.96) and fair-to-excellent agreement (coefficient of variation: 7.81–22.09%) in passive recovery, as well as moderate-to-high reliability (intraclass coefficient correlation: 0.50–0.87) and good agreement (coefficient of variation: 11.08–20.89%) in active recovery for LnRMSSD index. There was moderate-to-high reliability (intraclass coefficient correlation: 0.51–0.81) and good agreement (coefficient of variation: 10.41–18.87%) in most of the analyzed time points, in both recovery types for LnSDNN. In both types of recovery, the time domain heart rate variability 5–10 min indices (passive: intraclass coefficient correlation : 0.87–0.88; coefficient of variation: 7.67–13.44%; active: intraclass coefficient correlation 0.59–0.80; coefficient of variation: 14.62–16.26%) presented higher intraclass coefficient correlation and lower coefficient of variation than the spectral heart rate variability indices (passive: intraclass coefficient correlation: 0.71–0.87; coefficient of variation: 12.33–34.21%; active: intraclass coefficient correlation: 0.46–0.77; coefficient of variation: 24.41–105.12%). The LnRMSSD and LnSDNN indices analyzed in 30 s segments and the heart rate variability 5–10 min indices after maximal exercise in untrained healthy men showed satisfactory reproducibility, regardless of the type of recovery, with the time-domain indices showing higher reproducibility than the frequency-domain indices.
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14

Commenges, Daniel, and Helene Jacqmin. "The Intraclass Correlation Coefficient: Distribution-Free Definition and Test." Biometrics 50, no. 2 (June 1994): 517. http://dx.doi.org/10.2307/2533395.

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15

Bhandary, Madhusudan, and Koji Fujiwara. "Test for Intraclass Correlation Coefficient under Unequal Family Sizes." Journal of Modern Applied Statistical Methods 12, no. 2 (November 1, 2013): 171–83. http://dx.doi.org/10.22237/jmasm/1383278880.

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16

Tian, Lili. "On confidence intervals of a common intraclass correlation coefficient." Statistics in Medicine 24, no. 21 (2005): 3311–18. http://dx.doi.org/10.1002/sim.2145.

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17

Shoukri, Mohamed M., Allan Donner, and Abdelmoneim El-Dali. "Covariate-adjusted confidence interval for the intraclass correlation coefficient." Contemporary Clinical Trials 36, no. 1 (September 2013): 244–53. http://dx.doi.org/10.1016/j.cct.2013.07.003.

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18

İlgen, Ufuk, and İsmail Uçar. "Intraclass Correlation Coefficient and Reliability of Muscle Mass Measurements." Journal of Parenteral and Enteral Nutrition 45, no. 5 (June 29, 2021): 867–69. http://dx.doi.org/10.1002/jpen.2207.

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19

Chen, Chia-Cheng, and Huiman X. Barnhart. "Assessing agreement with intraclass correlation coefficient and concordance correlation coefficient for data with repeated measures." Computational Statistics & Data Analysis 60 (April 2013): 132–45. http://dx.doi.org/10.1016/j.csda.2012.11.004.

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20

Serbecic, Nermin, Sven Beutelspacher, Lovro Markovic, Abhijit Sina Roy, and Rohit Shetty. "Repeatability and reproducibility of corneal biomechanical parameters derived from Corvis ST." European Journal of Ophthalmology 30, no. 6 (November 20, 2019): 1287–94. http://dx.doi.org/10.1177/1120672119864554.

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Introduction: The aim of this study was to evaluate repeatability and reproducibility of newly calculated biomechanical parameters of the cornea, developed by our research group. Methods: One eye from each of the 23 healthy subjects was measured three times consecutively, three times at different daytimes and on three different days. The within-subject standard deviation and coefficient of variation, as well as the intraclass correlation coefficient, were calculated for every parameter in each group. Results: Excellent repeatability and reproducibility (coefficient of variation < 5%, intraclass correlation coefficient > 0.75) was found for corrected values measured at A1, HC, and A2 time points (2nd A2 Time, 2nd A1 Time, 2nd HC Time, 2nd HC Def Amp and 2nd A1 Def Amp). Corneal-specific stiffness parameters, which showed good repeatability and reliability, were DA_cor (coefficient of variation = 4.02%, intraclass correlation coefficient = 0.919), KcLinear (coefficient of variation = 4.03%, intraclass correlation coefficient = 0.895), areaForceCornea (coefficient of variation = 3.34%, intraclass correlation coefficient = 0.853) and E2 (coefficient of variation = 4.1%, intraclass correlation coefficient = 0.78). Overall, most parameters fell into the category of good reliability (high intraclass correlation coefficient) and poor reproducibility (low coefficient of variation), including all the parameters describing extraocular deformation (DA_ext, AEPvED, AUC EDef, areaForceExtra, Kg and μg). Comparing the coefficient of variation values for intrasession, intersession and daytime measurements, there were no indices for diurnal changes. Conclusion: Most parameters showed good repeatability and reliability. The extraocular stiffness parameters showed poor reproducibility. KcLinear can serve as a very reliable and repeatable indicator of corneal stiffness.
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21

Kuo, B. I. "Intraclass correlation coefficient rather than correlation coefficient to examine agreements among different methods measuring valvular area." Circulation 89, no. 4 (April 1994): 1910–11. http://dx.doi.org/10.1161/01.cir.89.4.1910.

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22

Shieh, Gwowen. "A comparison of two indices for the intraclass correlation coefficient." Behavior Research Methods 44, no. 4 (March 7, 2012): 1212–23. http://dx.doi.org/10.3758/s13428-012-0188-y.

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23

Ukoumunne, Obioha C., Anthony C. Davison, Martin C. Gulliford, and Susan Chinn. "Non-parametric bootstrap confidence intervals for the intraclass correlation coefficient." Statistics in Medicine 22, no. 24 (2003): 3805–21. http://dx.doi.org/10.1002/sim.1643.

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24

Xiao, Yuanhui, Jiawei Liu, and Madhusudan Bhandary. "Profile Likelihood Based Confidence Intervals for Common Intraclass Correlation Coefficient." Communications in Statistics - Simulation and Computation 39, no. 1 (December 8, 2009): 111–18. http://dx.doi.org/10.1080/03610910903324834.

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25

Luz, Laércio Lima, Lívia Maria Santiago, João Francisco Santos da Silva, and Inês Echenique Mattos. "Psychometric properties of the Brazilian version of the Vulnerable Elders Survey-13 (VES-13)." Cadernos de Saúde Pública 31, no. 3 (March 2015): 507–15. http://dx.doi.org/10.1590/0102-311x00011714.

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This study aims to assess the psychometric properties of the Brazilian version of the Vulnerable Elders Survey-13 (VES-13). Interviews were carried out with individuals aged 60 years and over receiving care at an ambulatory cancer centre. Test-retest reliability was assessed using the Spearman correlation coefficient, intraclass correlation coefficients and Cohen's kappa coefficient. Construct validity was assessed by testing convergent and discriminant validity using principal component analysis and Varimax rotation. The Spearman correlation coefficient value of the comparison between test and retest scores was 0.98 (p < 0.001). All intraclass correlation coefficient values were higher than 0.60 and kappa coefficients varied between 0.33 and 0.94. Three identified factors explained 72.6% of overall sample variance. VES-13 presented good convergent validity and reasonable discriminant validity. The psychometric properties of the adapted version of the VES-13 are consistent and adequate for use with the Brazilian population.
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26

Itoh, C., K. Kimura, and S. Wakayama. "Comparison of intraclass correlation coefficient and the coefficient of multiple correlation in the reliability of gait analysis." Physiotherapy 102 (November 2016): e83. http://dx.doi.org/10.1016/j.physio.2016.10.082.

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27

Raschner, C., H. P. Platzer, C. Patterson, M. Zeppetzauer, B. Del Frari, D. Estermann, and H. Piza-Katzer. "An isometric hand tester: quantifying motor function in the hand." Journal of Hand Surgery (European Volume) 35, no. 6 (March 17, 2010): 486–93. http://dx.doi.org/10.1177/1753193410363532.

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The aim of this study was to develop and assess an isometric hand tester to measure multiple strength parameters of the muscles of the hand. Nineteen men and 22 women took part in a first study and 17 men were tested in a second one. Five different tests were developed: wrist flexion, wrist extension, metacarpophalangeal joint flexion, thumb adduction and proximal and distal interphalangeal joint flexion. Reliability was tested with an intraclass correlation coefficient for single measures. When the results from men and women were analysed separately, all tests except metacarpophalangeal joint flexion and thumb adduction produced intraclass correlation coefficients greater than 0.7. Combining the genders gave intraclass correlation coefficients greater than 0.9 except for metacarpophalangeal joint flexion and thumb adduction. After adjustments, intraclass correlation coefficients for metacarpophalangeal joint flexion and thumb adduction were between 0.70 and 0.83 in the second study. The new isometric hand tests improve the measurement of intrinsic and extrinsic hand muscle strength.
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28

Tzamalis, Argyrios, Riccardo Vinciguerra, Vito Romano, Esmaeil Arbabi, Mark Batterbury, and Stephen B. Kaye. "Intraobserver reproducibility and interobserver agreement of demarcation line depth measurements following corneal cross linking." European Journal of Ophthalmology 30, no. 4 (March 12, 2019): 635–42. http://dx.doi.org/10.1177/1120672119835116.

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Purpose: The aim of this study was to evaluate the intraobserver reproducibility of demarcation line depth measurement in keratoconic patients after epithelium-off corneal collagen cross linking and assess the interobserver variability/agreement among examiners of various experience levels. Methods: 56 eyes of 56 patients undergoing corneal collagen cross linking for progressive keratoconus were enrolled. After 4 weeks, all patients underwent an anterior segment optical coherence tomography (AS-OCT; CASIA SS-1000; Tomey, Nagoya, Japan) in the cross-linked eye by four masked examiners (two medical students (S1 and S2) and two corneal fellows (F1 and F2)) in order to identify and measure the demarcation line depth in experienced and non-experienced users. The intraclass correlation coefficient and the coefficient of variation were calculated. Agreement between raters was evaluated using Bland–Altman plots, intraclass correlation coefficient and weighted-kappa statistics comparing raters in pairs. Results: The average demarcation line depth of all measurements was 254.34 ± 72.3 μm, varying from 84 to 459 μm. The intraclass correlation coefficient evaluating the reproducibility of measurements for F1 was 0.9379 (95% confidence interval (CI) =0.9035–0.9619), for the second (F2), it was 0.9837 (95% confidence interval = 0.9743–0.9901), while intraclass correlation coefficient between medical students was calculated 0.844. The overall intraclass correlation coefficient among all four observers was 0.8706 (95% confidence interval = 0.8061–0.9185). The coefficient of variation for repeated measurements was 5.981 μm (95% confidence interval = 3.966–7.471) and 3.312 μm (95% confidence interval = 2.468–3.981) for F1 and F2 raters, respectively. The percentage of demarcation line detection was 90.32%. Conclusion: The reproducibility of demarcation line measures although very good (intraclass correlation coefficient > 0.9), yielded a difference between the two experienced raters. Furthermore, the novice raters did not reach an excellent level of agreement with the expert ones showing greater variability in their recordings.
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Donner, Allan, and George Wells. "A Comparison of Confidence Interval Methods for the Intraclass Correlation Coefficient." Biometrics 42, no. 2 (June 1986): 401. http://dx.doi.org/10.2307/2531060.

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30

Zhang, Duo, and Min Wang. "Objective Bayesian inference for the intraclass correlation coefficient in linear models." Statistics & Probability Letters 137 (June 2018): 292–96. http://dx.doi.org/10.1016/j.spl.2018.02.004.

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31

Bhandary, Madhusudan, and Koji Fujiwara. "Confidence Interval Estimation for Intraclass Correlation Coefficient Under Unequal Family Sizes." Journal of Modern Applied Statistical Methods 8, no. 2 (November 1, 2009): 520–25. http://dx.doi.org/10.22237/jmasm/1257034500.

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32

Han, Soo-Yeon, Jung-Mo Nam, Sung-Min Myoung, and Ki-Jun Song. "A Comparison of Sample Size Requirements for Intraclass Correlation Coefficient(ICC)." Korean Journal of Applied Statistics 23, no. 3 (June 30, 2010): 497–510. http://dx.doi.org/10.5351/kjas.2010.23.3.497.

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33

Shieh, Gwowen. "Choosing the best index for the average score intraclass correlation coefficient." Behavior Research Methods 48, no. 3 (July 17, 2015): 994–1003. http://dx.doi.org/10.3758/s13428-015-0623-y.

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34

Madsen, Ole Rintek. "Reliability of muscle strength testing quantified by the intraclass correlation coefficient." Archives of Physical Medicine and Rehabilitation 83, no. 4 (April 2002): 582. http://dx.doi.org/10.1053/apmr.2002.33138.

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35

Giraudeau, Bruno. "Negative values of the intraclass correlation coefficient are not theoretically possible." Journal of Clinical Epidemiology 49, no. 10 (October 1996): 1205. http://dx.doi.org/10.1016/0895-4356(96)00053-4.

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36

Vasudevan, Alampallam Ramaswamy, and Subramanian Selvakumar. "Intraclass and interclass correlation coefficient-based feature selection in NIDS dataset." Security and Communication Networks 8, no. 18 (May 20, 2015): 3441–58. http://dx.doi.org/10.1002/sec.1269.

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37

Ho, Kwok Ming, Julian Sunario, and Matthew Anstey. "Response to “Intraclass Correlation Coefficient and Reliability of Muscle Mass Measurements”." Journal of Parenteral and Enteral Nutrition 45, no. 5 (July 2021): 870–71. http://dx.doi.org/10.1002/jpen.2208.

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38

Sahathevan, Sharmela, Ban‐Hock Khor, Chai Hong Yeong, Teik Hin Tan, Abdul Kareem Meera Mohaideen, Hi Ming Ng, Gild Rick Ong, et al. "Response to “Intraclass Correlation Coefficient and Reliability of Muscle Mass Measurements”." Journal of Parenteral and Enteral Nutrition 45, no. 5 (July 2021): 872–73. http://dx.doi.org/10.1002/jpen.2206.

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39

Xue, Cindy, Jing Yuan, Gladys G. Lo, Amy T. Y. Chang, Darren M. C. Poon, Oi Lei Wong, Yihang Zhou, and Winnie C. W. Chu. "Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review." Quantitative Imaging in Medicine and Surgery 11, no. 10 (October 2021): 4431–60. http://dx.doi.org/10.21037/qims-21-86.

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40

Ouriel, Kenneth, Richard L. Ouriel, Yeun J. Lim, Gregory Piazza, and Samuel Z. Goldhaber. "Computed tomography angiography with pulmonary artery thrombus burden and right-to-left ventricular diameter ratio after pulmonary embolism." Vascular 25, no. 1 (July 9, 2016): 54–62. http://dx.doi.org/10.1177/1708538116645056.

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Purpose Computed tomography angiography is used for quantifying the significance of pulmonary embolism, but its reliability has not been well defined. Methods The study cohort comprised 10 patients randomly selected from a 150-patient prospective trial of ultrasound-facilitated fibrinolysis for acute pulmonary embolism. Four reviewers independently evaluated the right-to-left ventricular diameter ratios using the standard multiplanar reformatted technique and a simplified (axial) method, and thrombus burden with the standard modified Miller score and a new, refined Miller scoring system. Results The intraclass correlation coefficient for intra-observer variability was .949 and .970 for the multiplanar reformatted and axial methods for estimating right-to-left ventricular ratios, respectively. Inter-observer agreement was high and similar for the two methods, with intraclass correlation coefficient of .969 and .976. The modified Miller score had good intra-observer agreement (intraclass correlation coefficient .820) and was similar to the refined Miller method (intraclass correlation coefficient .883) for estimating thrombus burden. Inter-observer agreement was also comparable between the techniques, with intraclass correlation coefficient of .829 and .914 for the modified Miller and refined Miller methods. Conclusions The reliability of computed tomography angiography for pulmonary embolism was excellent for the axial and multiplanar reformatted methods for quantifying the right-to-left ventricular ratio and for the modified Miller and refined Miller scores for quantifying of pulmonary artery thrombus burden.
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41

Comfort, Paul, Thomas Dos’Santos, Paul A. Jones, John J. McMahon, Timothy J. Suchomel, Caleb Bazyler, and Michael H. Stone. "Normalization of Early Isometric Force Production as a Percentage of Peak Force During Multijoint Isometric Assessment." International Journal of Sports Physiology and Performance 15, no. 4 (April 1, 2020): 478–82. http://dx.doi.org/10.1123/ijspp.2019-0217.

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Purpose: To determine the reliability of early force production (50, 100, 150, 200, and 250 ms) relative to peak force (PF) during an isometric mid-thigh pull and to assess the relationships between these variables. Methods: Male collegiate athletes (N = 29; age 21.1 [2.9] y, height 1.71 [0.07] m, body mass 71.3 [13.6] kg) performed isometric mid-thigh pulls during 2 separate testing sessions. Net PF and net force produced at each epoch were calculated. Within- and between-session reliabilities were determined using intraclass correlation coefficients and coefficient of variation percentages. In addition, Pearson correlation coefficients and coefficient of determination were calculated to examine the relationships between PF and time-specific force production. Results: Net PF and time-specific force demonstrated very high to almost perfect reliability both within and between sessions (intraclass correlation coefficients .82–.97; coefficient of variation percentages 0.35%–1.23%). Similarly, time-specific force expressed as a percentage of PF demonstrated very high to almost perfect reliability both within and between sessions (intraclass correlation coefficients .76–.86; coefficient of variation percentages 0.32%–2.51%). Strong to nearly perfect relationships (r = .615–.881) exist between net PF and time-specific net force, with relationships improving over longer epochs. Conclusion: Based on the smallest detectable difference, a change in force at 50 milliseconds expressed relative to PF > 10% and early force production (100, 150, 200, and 250 ms) expressed relative to PF of >2% should be considered meaningful. Expressing early force production as a percentage of PF is reliable and may provide greater insight into the adaptations to the previous training phase than PF alone.
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42

Nazary-Moghadam, Salman, Mahyar Salavati, Ali Esteki, Behnam Akhbari, Sohrab Keyhani, and Afsaneh Zeinalzadeh. "Reliability of Knee Flexion–Extension Lyapunov Exponent in People With and Without Anterior Cruciate Ligament Deficiency." Journal of Sport Rehabilitation 29, no. 2 (February 1, 2020): 253–56. http://dx.doi.org/10.1123/jsr.2018-0468.

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Objectives: The current study assessed the intrasession and intersession reliability of the knee flexion–extension Lyapunov exponent in patients with anterior cruciate ligament deficiency and healthy individuals. Study Design: University research laboratory. Methods: Kinematic data were collected in 14 patients with anterior cruciate ligament deficiency and 14 healthy individuals walked on a treadmill at a self-selected, low, and high speed, with and without cognitive load. The intraclass correlation coefficient, standard error of measurement, minimal metrically detectable change, and percentage of coefficient of variation were calculated to assess the reliability. Results: The knee flexion–extension Lyapunov exponent had high intrasession reliability, with intraclass correlation coefficients ranging from .83 to .98. In addition, the intersession intraclass correlation coefficient values of these measurements ranged from .35 to .85 regardless of group, gait speed, and dual tasking. In general, relative and absolute reliability were higher in the patients with anterior cruciate ligament deficiency than in the healthy individuals. Conclusions: Although knee flexion–extension Lyapunov exponent demonstrates good intrasession reliability, its low intersession reliability indicates that changes of these measurements between different days should be interpreted with caution.
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43

WEIR, JOSEPH P. "QUANTIFYING TEST-RETEST RELIABILITY USING THE INTRACLASS CORRELATION COEFFICIENT AND THE SEM." Journal of Strength and Conditioning Research 19, no. 1 (February 2005): 231–40. http://dx.doi.org/10.1519/00124278-200502000-00038.

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44

Zou, Guangyong, and Allan Donner. "Confidence Interval Estimation of the Intraclass Correlation Coefficient for Binary Outcome Data." Biometrics 60, no. 3 (August 27, 2004): 807–11. http://dx.doi.org/10.1111/j.0006-341x.2004.00232.x.

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45

Weir, Joseph P. "Quantifying Test-Retest Reliability Using the Intraclass Correlation Coefficient and the SEM." Journal of Strength and Conditioning Research 19, no. 1 (2005): 231. http://dx.doi.org/10.1519/15184.1.

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46

van de Wassenberg, Wilma J. G., Johannes H. van der Hoeven, Klaus L. Leenders, and Natasha M. Maurits. "Quantifying Interhemispheric Symmetry of Somatosensory Evoked Potentials With the Intraclass Correlation Coefficient." Journal of Clinical Neurophysiology 25, no. 3 (June 2008): 139–46. http://dx.doi.org/10.1097/wnp.0b013e31817759e2.

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47

Prieto, L., R. Lamarca, A. Casado, and J. Alonso. "The evaluation of agreement on continuous variables by the intraclass correlation coefficient." Journal of Epidemiology & Community Health 51, no. 5 (October 1, 1997): 579–81. http://dx.doi.org/10.1136/jech.51.5.579-a.

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Gulliford, M. C., G. Adams, O. C. Ukoumunne, R. Latinovic, S. Chinn, and M. J. Campbell. "Intraclass correlation coefficient and outcome prevalence are associated in clustered binary data." Journal of Clinical Epidemiology 58, no. 3 (March 2005): 246–51. http://dx.doi.org/10.1016/j.jclinepi.2004.08.012.

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Paul, S. R. "Interval estimation of the intraclass correlation coefficient based on Bartlett’s score procedure." Journal of the Italian Statistical Society 6, no. 3 (December 1997): 257–72. http://dx.doi.org/10.1007/bf03178916.

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Chen, Tom, and Rui Wang. "Inference for variance components in linear mixed-effect models with flexible random effect and error distributions." Statistical Methods in Medical Research 29, no. 12 (July 15, 2020): 3586–604. http://dx.doi.org/10.1177/0962280220933909.

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Abstract:
In many biomedical investigations, parameters of interest, such as the intraclass correlation coefficient, are functions of higher-order moments reflecting finer distributional characteristics. One popular method to make inference for such parameters is through postulating a parametric random effects model. We relax the standard normality assumptions for both the random effects and errors through the use of the Fleishman distribution, a flexible four-parameter distribution which accounts for the third and fourth cumulants. We propose a Fleishman bootstrap method to construct confidence intervals for correlated data and develop a normality test for the random effect and error distributions. Recognizing that the intraclass correlation coefficient may be heavily influenced by a few extreme observations, we propose a modified, quantile-normalized intraclass correlation coefficient. We evaluate our methods in simulation studies and apply these methods to the Childhood Adenotonsillectomy Trial sleep electroencephalogram data in quantifying wave-frequency correlation among different channels.
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