Literature DB >> 32252039

A comprehensive guideline for Bland-Altman and intra class correlation calculations to properly compare two methods of measurement and interpret findings.

Shahab Haghayegh1, Hyeon-Ah Kang, Sepideh Khoshnevis, Michael H Smolensky, Kenneth R Diller.   

Abstract

The rapid emergence of new measurement instruments and methods requires personnel and researchers of different disciplines to know the correct statistical methods to utilize to compare their performance with reference ones and properly interpret findings. We discuss the often-made mistake of applying the inappropriate correlation and regression statistical approaches to compare methods and then explain the concepts of agreement and reliability. Then, we introduce the intraclass correlation as a measure of inter-rater reliability, and the Bland-Altman plot as a measure of agreement, and we provide formulae to calculate them along with illustrative examples for different types of study designs, specifically single measurement per subject, repeated measurement while the true value is constant, and repeated measurement when the true value is not constant. We emphasize the requirement to validate the assumptions of these statistical approaches, and also how to deal with violations and provide formulae on how to calculate the confidence interval for estimated values of agreement and intraclass correlation. Finally, we explain how to interpret and report the findings of these statistical analyses.

Mesh:

Year:  2020        PMID: 32252039     DOI: 10.1088/1361-6579/ab86d6

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  13 in total

1.  A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code.

Authors:  Luca Menghini; Nicola Cellini; Aimee Goldstone; Fiona C Baker; Massimiliano de Zambotti
Journal:  Sleep       Date:  2021-02-12       Impact factor: 5.849

2.  Nutritional Status and Other Clinical Variables Are Associated to the Resting Energy Expenditure in Patients With Chronic Kidney Disease: A Validity Study.

Authors:  Samuel Ramos-Acevedo; Luis Rodríguez-Gómez; Sonia López-Cisneros; Ailema González-Ortiz; Ángeles Espinosa-Cuevas
Journal:  Front Nutr       Date:  2022-05-18

3.  Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls.

Authors:  Luca Menghini; Dilara Yuksel; Aimee Goldstone; Fiona C Baker; Massimiliano de Zambotti
Journal:  Chronobiol Int       Date:  2021-04-01       Impact factor: 3.749

4.  Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network.

Authors:  Huijun Yue; Yu Lin; Yitao Wu; Yongquan Wang; Yun Li; Xueqin Guo; Ying Huang; Weiping Wen; Gansen Zhao; Xiongwen Pang; Wenbin Lei
Journal:  Nat Sci Sleep       Date:  2021-03-12

5.  Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data.

Authors:  Shahab Haghayegh; Sepideh Khoshnevis; Michael H Smolensky; Kenneth R Diller; Richard J Castriotta
Journal:  Sensors (Basel)       Date:  2020-12-23       Impact factor: 3.576

6.  Reliability of hip joint position sense tests using a clinically applicable measurement tool in elderly participants with unilateral hip osteoarthritis.

Authors:  Ravi Shankar Reddy; Jaya Shanker Tedla; Mastour Saeed Alshahrani; Faisal Asiri; Venkata Nagaraj Kakaraparthi; Paul Silvian Samuel; Praveen Kumar Kandakurti
Journal:  Sci Rep       Date:  2022-01-10       Impact factor: 4.379

7.  Application of Artificial Intelligence and Deep Learning for Choroid Segmentation in Myopia.

Authors:  Hung-Ju Chen; Yu-Len Huang; Siu-Lun Tse; Wei-Ping Hsia; Chung-Hao Hsiao; Yang Wang; Chia-Jen Chang
Journal:  Transl Vis Sci Technol       Date:  2022-02-01       Impact factor: 3.283

8.  Performance Evaluation of a Smart Bed Technology against Polysomnography.

Authors:  Farzad Siyahjani; Gary Garcia Molina; Shawn Barr; Faisal Mushtaq
Journal:  Sensors (Basel)       Date:  2022-03-29       Impact factor: 3.576

9.  Physical activity estimated by osteogenic potential and energy expenditure has differing associations with bone mass in young adults: the raine study.

Authors:  Carrie-Anne Ng; David Scott; Marc Sim; Kun Zhu; Aris Siafarikas; Nicolas H Hart; Jocelyn Tan; Paola Chivers
Journal:  Arch Osteoporos       Date:  2022-04-18       Impact factor: 2.879

10.  Emfit Bed Sensor Activity Shows Strong Agreement with Wrist Actigraphy for the Assessment of Sleep in the Home Setting.

Authors:  Juan Piantino; Madison Luther; Christina Reynolds; Miranda M Lim
Journal:  Nat Sci Sleep       Date:  2021-07-16
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