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Commentary on “Alternative graphs for diagnostic tests: the agreement chart and the receiver operating characteristic curve”
Journal of Clinical Epidemiology 61 (2008) 855e856
COMMENTARY
Commentary on ‘‘Alternative graphs for diagnostic tests: the agreement chart and the receiver operating characteristic curve’’ Charlie Goldsmith McMaster University, CE&B; Biostatistics, 3rd floor Martha, Room H322, St. Joseph’s Healthcare, Hamilton, ON, Canada L8N 4A6 Accepted 6 May 2008
The Journal of Clinical Epidemiology (JCE) would like authors to consider using more graphic displays in presenting their findings. This is appropriate particularly where readers are more visually oriented than numerically oriented and, as a result, a visual display of key findings often goes a long way in helping readers understand the key points of the manuscripts that are published in JCE. In this issue of the Journal, Bangdiwala et al [1] have proposed an alternative way of displaying agreement charts and receiver operating characteristic curves as areas shaded in the unit square. Although this is an interesting idea and certainly should be checked out for its ability to distinguish alternative diagnostic tests and alternative reliability studies where agreement tests are used, the feature of human perception should not be lost in creating such a display. Human perception of graphs as an area that is not always appropriately recognized by many software packages where things like labeling y-axes vertically rather than horizontally, using color over other features, and not recognizing that readers indeed can have difficulty figuring out exactly what is intended by the authors when they create a visual display. For example, in his summary of the human perception research, Cleveland [2] suggested that the criteria involved in creating a graph should be ordered with the following principles that begin with easy perception and end with more difficult perception, including (1) position along a common line; (2) position along identical nonaligned scales; (3) length; (4) angle or slope; (5) area; (6) volume; and (7) color saturation or color density. These features, when applied to the Bangdiwala et al article [1], suggest that they are way down the list in ease of perception by choosing areas (5) to display key pieces of information rather than positioning information on a common line (1), which is at the top of the list. As a result, the perception of the graph becomes more difficult as the graphing features become more complex. Area is difficult for humans
DOI of original article: 10.1016/j.jclinepi.2008.04.002. E-mail address: [email protected] 0895-4356/08/$ e see front matter Ó 2008 Elsevier Inc. All rights reserved. doi: 10.1016/j.jclinepi.2008.05.003
to judge the magnitude of perceptually and as a result, making comparisons with areas will be difficult. Indeed, it would be useful to have a verification of these principles put together by Cleveland based on a lot of the current graphs that are being published in the health sciences literature. Another feature for those who are trying to decide about graphic principles is to consider using an inference graph rather than a descriptive graph. A descriptive graph does not usually convey important conclusions about a study. However, an inference graph displays contrasts among the study groups that answer scientifically interesting questions. With many questions, the inference graph can look like a forest plot used in displaying individual studies in a visual display of a meta-analysis. When created with point estimates and their confidence intervals, they display data on the original-data scale and can include reference lines at zero or if relevant as the minimum clinically important difference. Most of these features are often not shown on a descriptive graph, so it is less useful to a reader. For example, when somebody uses a set of descriptive statistics graphically and bar charts with error bars on them depicting standard deviations or standard errors, these are often supplemented with lines that connect the groups with P-values. However, the magnitude of the difference, its confidence and its P-value at the inference level often are not displayed graphically. Authors should be encouraged to use more informative inferential displays showing estimates of important findings with their confidence interval at, say, the 95% level, with a suitable criterion value added to the graph such as zero when it is appropriate in a null hypothesis. This will mean that authors are putting estimates back in terms of original units that are at the level of generally what one designs a study to do, such as comparing alternative groups in a randomized trial. There are many other references about graphic scales that should be considered by authors of articles in JCE, including Tufte [3], Tufte [4], and Robins [5], as they try to create an appropriate graph to display the key findings and recommendations in their manuscripts. Although it is usual to display key findings in tables in published
856
C. Goldsmith / Journal of Clinical Epidemiology 61 (2008) 855e856
articles, the editors would like to have authors consider adding a key graphic display. References [1] Bangdiwala SI, Haedo AS, Natal ML, Villaveces A. Alternative graphs for diagnostic tests: the agreement chart and the receiver operating characteristic curve. J Clin Epidemiol 2008;61:866e74.
[2] Cleveland WS. The elements of graphing data. Monterey CA: Wadsworth Advanced Books and Software; 1985. [re-issued by Hobart Press, Summit NJ, as a revised edition in 1994.]. [3] Tufte ER. Envisioning information. Cheshire CN: Graphics Press; 1990. [4] Tufte ER. Visual explanations. Cheshire CN: Graphics Press; 1997. [5] Robins NB. Creating more effective graphs. Toronto ON: John Wiley & Sons Inc; 2005.
COMMENTARY
Commentary on ‘‘Alternative graphs for diagnostic tests: the agreement chart and the receiver operating characteristic curve’’ Charlie Goldsmith McMaster University, CE&B; Biostatistics, 3rd floor Martha, Room H322, St. Joseph’s Healthcare, Hamilton, ON, Canada L8N 4A6 Accepted 6 May 2008
The Journal of Clinical Epidemiology (JCE) would like authors to consider using more graphic displays in presenting their findings. This is appropriate particularly where readers are more visually oriented than numerically oriented and, as a result, a visual display of key findings often goes a long way in helping readers understand the key points of the manuscripts that are published in JCE. In this issue of the Journal, Bangdiwala et al [1] have proposed an alternative way of displaying agreement charts and receiver operating characteristic curves as areas shaded in the unit square. Although this is an interesting idea and certainly should be checked out for its ability to distinguish alternative diagnostic tests and alternative reliability studies where agreement tests are used, the feature of human perception should not be lost in creating such a display. Human perception of graphs as an area that is not always appropriately recognized by many software packages where things like labeling y-axes vertically rather than horizontally, using color over other features, and not recognizing that readers indeed can have difficulty figuring out exactly what is intended by the authors when they create a visual display. For example, in his summary of the human perception research, Cleveland [2] suggested that the criteria involved in creating a graph should be ordered with the following principles that begin with easy perception and end with more difficult perception, including (1) position along a common line; (2) position along identical nonaligned scales; (3) length; (4) angle or slope; (5) area; (6) volume; and (7) color saturation or color density. These features, when applied to the Bangdiwala et al article [1], suggest that they are way down the list in ease of perception by choosing areas (5) to display key pieces of information rather than positioning information on a common line (1), which is at the top of the list. As a result, the perception of the graph becomes more difficult as the graphing features become more complex. Area is difficult for humans
DOI of original article: 10.1016/j.jclinepi.2008.04.002. E-mail address: [email protected] 0895-4356/08/$ e see front matter Ó 2008 Elsevier Inc. All rights reserved. doi: 10.1016/j.jclinepi.2008.05.003
to judge the magnitude of perceptually and as a result, making comparisons with areas will be difficult. Indeed, it would be useful to have a verification of these principles put together by Cleveland based on a lot of the current graphs that are being published in the health sciences literature. Another feature for those who are trying to decide about graphic principles is to consider using an inference graph rather than a descriptive graph. A descriptive graph does not usually convey important conclusions about a study. However, an inference graph displays contrasts among the study groups that answer scientifically interesting questions. With many questions, the inference graph can look like a forest plot used in displaying individual studies in a visual display of a meta-analysis. When created with point estimates and their confidence intervals, they display data on the original-data scale and can include reference lines at zero or if relevant as the minimum clinically important difference. Most of these features are often not shown on a descriptive graph, so it is less useful to a reader. For example, when somebody uses a set of descriptive statistics graphically and bar charts with error bars on them depicting standard deviations or standard errors, these are often supplemented with lines that connect the groups with P-values. However, the magnitude of the difference, its confidence and its P-value at the inference level often are not displayed graphically. Authors should be encouraged to use more informative inferential displays showing estimates of important findings with their confidence interval at, say, the 95% level, with a suitable criterion value added to the graph such as zero when it is appropriate in a null hypothesis. This will mean that authors are putting estimates back in terms of original units that are at the level of generally what one designs a study to do, such as comparing alternative groups in a randomized trial. There are many other references about graphic scales that should be considered by authors of articles in JCE, including Tufte [3], Tufte [4], and Robins [5], as they try to create an appropriate graph to display the key findings and recommendations in their manuscripts. Although it is usual to display key findings in tables in published
856
C. Goldsmith / Journal of Clinical Epidemiology 61 (2008) 855e856
articles, the editors would like to have authors consider adding a key graphic display. References [1] Bangdiwala SI, Haedo AS, Natal ML, Villaveces A. Alternative graphs for diagnostic tests: the agreement chart and the receiver operating characteristic curve. J Clin Epidemiol 2008;61:866e74.
[2] Cleveland WS. The elements of graphing data. Monterey CA: Wadsworth Advanced Books and Software; 1985. [re-issued by Hobart Press, Summit NJ, as a revised edition in 1994.]. [3] Tufte ER. Envisioning information. Cheshire CN: Graphics Press; 1990. [4] Tufte ER. Visual explanations. Cheshire CN: Graphics Press; 1997. [5] Robins NB. Creating more effective graphs. Toronto ON: John Wiley & Sons Inc; 2005.