The agreement chart

BMC Medical Research Methodology

Viswanathan Shankar²  Shrikant I Bangdiwala¹ 

[1] Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA;Division of Biostatistics, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

关键词: B-statistic;    Kappa statistic;    Concordance;    Intra- and inter-observer agreement;   

Others  :  1092157
DOI  :  10.1186/1471-2288-13-97

 received in 2013-01-08, accepted in 2013-07-23,  发布年份 2013

【 摘 要 】

Background

When assessing the concordance between two methods of measurement of ordinal categorical data, summary measures such as Cohen’s (1960) kappa or Bangdiwala’s (1985) B-statistic are used. However, a picture conveys more information than a single summary measure.

Methods

We describe how to construct and interpret Bangdiwala’s (1985) agreement chart and illustrate its use in visually assessing concordance in several example clinical applications.

Results

The agreement charts provide a visual impression that no summary statistic can convey, and summary statistics reduce the information to a single characteristic of the data. However, the visual impression is personal and subjective, and not usually reproducible from one reader to another.

Conclusions

The agreement chart should be used to complement the summary kappa or B-statistics, not to replace them. The graphs can be very helpful to researchers as an early step to understand relationships in their data when assessing concordance.

【 授权许可】

   
2013 Bangdiwala and Shankar; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Landis JR, Koch GG: The measurement of observer agreement for categorical data. Biometrics 1977, 33(1):159-174.
• [2]Westlund KB, Kurland LT: Studies on multiple sclerosis in Winnipeg, Manitoba, and New Orleans, Louisiana. II. A controlled investigation of factors in the life history of the Winnipeg patients. Am J Hyg 1953, 57(3):397-407.
• [3]Cohen J: A coefficient for agreement for nominal scales. Educ Psychol Meas 1960, 20:37-46.
• [4]Bangdiwala SI: A graphical test for observer agreement. Amsterdam: International Statistical Institute; 1985:307-308. [International statistical institute centenary session 1985]
• [5]Munoz SR, Bangdiwala SI: Interpretation of Kappa and B statistics measures of agreement. J Appl Stat 1997, 24(1):105-112.
• [6]Shankar V, Bangdiwala SI: Behavior of agreement measures in the presence of zero cells and biased marginal distributions. J Appl Stat 2008, 35(4):445-464.
• [7]Bangdiwala SI, Cohn R, Hazard C, Davis CE, Prineas RJ: Comparisons of cause of death verification methods and costs in the lipid research clinics program mortality follow-up study. Control Clin Trials 1989, 10(2):167-187.
• [8]Michel CE, Solomon AW, Magbanua JP, Massae PA, Huang L, Mosha J, West SK, Nadala EC, Bailey R, Wisniewski C, et al.: Field evaluation of a rapid point-of-care assay for targeting antibiotic treatment for trachoma control: a comparative study. Lancet 2006, 367(9522):1585-1590.
• [9]Meyer D, Zeileis A, Hornik K: vcd: Visualizing Categorical Data: R package version 1.2-13. 2012.
• [10]Tufte ER: The visual display of quantitative information. Cheshire, CT: Graphics Press; 1983.