Assessing Agreement When Agreement Is Hard to Assess—The Agatston Score for Coronary Calcification
Abstract
:1. Introduction
- Which statistical measures and analyses were employed for agreement assessment of the Agatston score during the past three decades?
- To which extent did the increased awareness of alternative analysis strategies (such as Bland–Altman LoA and related research) influence and change the way agreement assessments of the Agatston score were performed?
- In light of recent research endeavors, what are potential routes for agreement analyses for highly skewed biomarkers like the Agatston score?
- The material that agreement assessment was based on varied massively from a few dozens to thousands of observations.
- Simple scatterplots of paired measurements against each other and accompanying correlation coefficients have prevailed across three decades.
- Logarithmic transformations have been popular to counteract non-normally distributed Agatston scores.
- Bland–Altman analysis has been increasingly used, but in many variations.
- Only very few publications were capable of deriving LoA that fit the observed data nicely in a difference plot.
2. Materials and Methods
2.1. Literature SEARCH
2.2. Data Extraction
2.3. Data Analysis
3. Results
4. Discussion
4.1. Key Findings
- Sample sizes of studies assessing agreement of the Agatston score ranged from 10 to 9761, with a median of 85 and a third quartile of 120.
- Simple scatterplots of paired measurements against each other and accompanying correlation coefficients have prevailed across three decades.
- Logarithmic transformations have been popular to counteract skewed distributions of the Agatston score.
- Bland–Altman plots have replaced difference plots of various kinds, but 95% CIs accompanied the LoA only in 4 out of 49 studies (8%).
- Only two publications (4%) applied nonparametric quantile regression to derive 95% repeatability limits that fit the observed data nicely in a difference plot.
4.2. Limitations of the Study
4.3. What This Adds to What Is Known
4.4. What Is the Implication, What Should Change Now?
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Type of Analysis | Number of Studies (%) |
---|---|
Bland–Altman plot or a variant thereof 1 | 25 (51) |
Relative change (in %) | 23 (47) |
Scatterplot of paired measurements | 20 (41) |
Correlation coefficient | 20 (41) |
Logarithmic transformation | 15 (31) |
ANOVA | 13 (27) |
Proportion of agreement | 12 (25) |
T test | 10 (20) |
Linear regression | 10 (20) |
Intra-class correlation coefficient | 10 (20) |
Kappa | 6 (12) |
Quantile regression | 2 (4) |
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Andersen, K.P.; Gerke, O. Assessing Agreement When Agreement Is Hard to Assess—The Agatston Score for Coronary Calcification. Diagnostics 2022, 12, 2993. https://doi.org/10.3390/diagnostics12122993
Andersen KP, Gerke O. Assessing Agreement When Agreement Is Hard to Assess—The Agatston Score for Coronary Calcification. Diagnostics. 2022; 12(12):2993. https://doi.org/10.3390/diagnostics12122993
Chicago/Turabian StyleAndersen, Kristoffer Papsø, and Oke Gerke. 2022. "Assessing Agreement When Agreement Is Hard to Assess—The Agatston Score for Coronary Calcification" Diagnostics 12, no. 12: 2993. https://doi.org/10.3390/diagnostics12122993
APA StyleAndersen, K. P., & Gerke, O. (2022). Assessing Agreement When Agreement Is Hard to Assess—The Agatston Score for Coronary Calcification. Diagnostics, 12(12), 2993. https://doi.org/10.3390/diagnostics12122993