The Use of Information Graphs to Evaluate and Compare Diagnostic Tests

Benish, William A.

doi:10.1055/s-0038-1634294

Cited by 26 publications

(16 citation statements)

References 16 publications

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“…Such an approach would be of potential benefit at the bedside when faced with an unexpected result. 24 For visual convenience, we consider a transformed version of the Bayes nomogram such that the pre-TP axis is linear (note that in the standard Bayes nomogram, the pre-TP values are distributed in a nonlinear fashion). 1,5 This transformed graph contains the same information as the standard nomogram, but the probability axes are linear (Figure 2).…”

Section: Uncertainty In Pretest Probability Estimatesmentioning

confidence: 99%

Incorporating Uncertainty Into Medical Decision Making: An Approach to Unexpected Test Results

2009

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The utility of diagnostic tests derives from the ability to translate the population concepts of sensitivity and specificity into information that will be useful for the individual patient: the predictive value of the result. As the array of available diagnostic testing broadens, there is a temptation to de-emphasize history and physical findings and defer to the objective rigor of technology. However, diagnostic test interpretation is not always straightforward. One significant barrier to routine use of probability-based test interpretation is the uncertainty inherent in pretest probability estimation, the critical first step of Bayesian reasoning. The context in which this uncertainty presents the greatest challenge is when test results oppose clinical judgment. It is this situation when decision support would be most helpful. The authors propose a simple graphical approach that incorporates uncertainty in pretest probability and has specific application to the interpretation of unexpected results. This method quantitatively demonstrates how uncertainty in disease probability may be amplified when test results are unexpected (opposing clinical judgment), even for tests with high sensitivity and specificity. The authors provide a simple nomogram for determining whether an unexpected test result suggests that one should "switch diagnostic sides.'' This graphical framework overcomes the limitation of pretest probability uncertainty in Bayesian analysis and guides decision making when it is most challenging: interpretation of unexpected test results.

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Section: Uncertainty In Pretest Probability Estimatesmentioning

confidence: 99%

Incorporating Uncertainty Into Medical Decision Making: An Approach to Unexpected Test Results

2009

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“…In the life sciences, Shannon entropy has been used to measure cellular diversity (12,13) and phylogenetic variation (14), and to model molecular interactions (15). This concept has previously been applied to singleanalyte laboratory testing by Rudolph (16)(17)(18) and, more recently, by Benish (19)(20)(21)(22)(23) and Vollmer (24). However, their approaches have not been widely adopted or disseminated and, in particular, have not been applied to NGS.…”

mentioning

confidence: 98%

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

et al. 2015

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“…Benish [19] applied information graphs for relative entropy to evaluate and compare clinical diagnostic tests. Here we derive relative entropies from Biggerstaff's likelihood ratios graph and present the results in a new diagrammatic format, with relative entropies for + and − predictions on the axes of the graph.…”

Section: Discussionmentioning

confidence: 99%

Information Graphs Incorporating Predictive Values of Disease Forecasts

Hughes

Reed

McRoberts

2020

Entropy

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Diagrammatic formats are useful for summarizing the processes of evaluation and comparison of forecasts in plant pathology and other disciplines where decisions about interventions for the purpose of disease management are often based on a proxy risk variable. We describe a new diagrammatic format for disease forecasts with two categories of actual status and two categories of forecast. The format displays relative entropies, functions of the predictive values that characterize expected information provided by disease forecasts. The new format arises from a consideration of earlier formats with underlying information properties that were previously unexploited. The new diagrammatic format requires no additional data for calculation beyond those used for the calculation of a receiver operating characteristic (ROC) curve. While an ROC curve characterizes a forecast in terms of sensitivity and specificity, the new format described here characterizes a forecast in terms of relative entropies based on predictive values. Thus it is complementary to ROC methodology in its application to the evaluation and comparison of forecasts.

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The Use of Information Graphs to Evaluate and Compare Diagnostic Tests

Cited by 26 publications

References 16 publications

Incorporating Uncertainty Into Medical Decision Making: An Approach to Unexpected Test Results

Incorporating Uncertainty Into Medical Decision Making: An Approach to Unexpected Test Results

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

Information Graphs Incorporating Predictive Values of Disease Forecasts

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