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Estimation of an inter-rater intra-class correlation coefficient that overcomes common assumption violations in the assessment of health measurement scales

Overview of attention for article published in BMC Medical Research Methodology, September 2018
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  • Good Attention Score compared to outputs of the same age (68th percentile)

Mentioned by

blogs
1 blog

Citations

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148 Dimensions

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292 Mendeley
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Title
Estimation of an inter-rater intra-class correlation coefficient that overcomes common assumption violations in the assessment of health measurement scales
Published in
BMC Medical Research Methodology, September 2018
DOI 10.1186/s12874-018-0550-6
Pubmed ID
Authors

Carly A. Bobak, Paul J. Barr, A. James O’Malley

Abstract

Intraclass correlation coefficients (ICC) are recommended for the assessment of the reliability of measurement scales. However, the ICC is subject to a variety of statistical assumptions such as normality and stable variance, which are rarely considered in health applications. A Bayesian approach using hierarchical regression and variance-function modeling is proposed to estimate the ICC with emphasis on accounting for heterogeneous variances across a measurement scale. As an application, we review the implementation of using an ICC to evaluate the reliability of Observer OPTION5, an instrument which used trained raters to evaluate the level of Shared Decision Making between clinicians and patients. The study used two raters to evaluate recordings of 311 clinical encounters across three studies to evaluate the impact of using a Personal Decision Aid over usual care. We particularly focus on deriving an estimate for the ICC when multiple studies are being considered as part of the data. The results demonstrate that ICC varies substantially across studies and patient-physician encounters within studies. Using the new framework we developed, the study-specific ICCs were estimated to be 0.821, 0.295, and 0.644. If the within- and between-encounter variances were assumed to be the same across studies, the estimated within-study ICC was 0.609. If heteroscedasticity is not properly adjusted for, the within-study ICC estimate was inflated to be as high as 0.640. Finally, if the data were pooled across studies without accounting for the variability between studies then ICC estimates were further inflated by approximately 0.02 while formerly allowing for between study variation in the ICC inflated its estimated value by approximately 0.066 to 0.072 depending on the model. We demonstrated that misuse of the ICC statistics under common assumption violations leads to misleading and likely inflated estimates of interrater reliability. A statistical analysis that overcomes these violations by expanding the standard statistical model to account for them leads to estimates that are a better reflection of a measurement scale's reliability while maintaining ease of interpretation. Bayesian methods are particularly well suited to estimating the expanded statistical model.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 292 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 292 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 12%
Student > Bachelor 33 11%
Student > Master 32 11%
Researcher 19 7%
Other 16 5%
Other 50 17%
Unknown 108 37%
Readers by discipline Count As %
Medicine and Dentistry 53 18%
Nursing and Health Professions 26 9%
Engineering 21 7%
Sports and Recreations 16 5%
Psychology 12 4%
Other 42 14%
Unknown 122 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 September 2018.
All research outputs
#6,358,277
of 24,654,416 outputs
Outputs from BMC Medical Research Methodology
#890
of 2,193 outputs
Outputs of similar age
#104,850
of 342,185 outputs
Outputs of similar age from BMC Medical Research Methodology
#19
of 33 outputs
Altmetric has tracked 24,654,416 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 2,193 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has gotten more attention than average, scoring higher than 55% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 342,185 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.