Visualizing the Bayesian 2-test case: The effect of tree diagrams on medical decision making

URN to cite this document:: urn:nbn:de:bvb:355-epub-373295
DOI to cite this document:: 10.5283/epub.37329

Binder, Karin

; Krauss, Stefan

; Bruckmaier, Georg ; Marienhagen, Jörg

Preview

License: Creative Commons Attribution 4.0
PDF - Published Version
(5MB)

Date of publication of this fulltext: 15 May 2018 11:02

Alternative links to fulltext:DOI Webofscience Publisher Publisher

Details

Preview

Indexed in

Export bibliographical data

Abstract

Abstract

In medicine, diagnoses based on medical test results are probabilistic by nature. Unfortunately, cognitive illusions regarding the statistical meaning of test results are well documented among patients, medical students, and even physicians. There are two effective strategies that can foster insight into what is known as Bayesian reasoning situations: (1) translating the statistical information on the prevalence of a disease and the sensitivity and the false-alarm rate of a specific test for that disease from probabilities into natural frequencies, and (2) illustrating the statistical information with tree diagrams, for instance, or with other pictorial representation. So far, such strategies have only been empirically tested in combination for "1-test cases", where one binary hypothesis ("disease" vs. "no disease") has to be diagnosed based on one binary test result ("positive" vs. "negative"). However, in reality, often more than one medical test is conducted to derive a diagnosis. In two studies, we examined a total of 388 medical students from the University of Regensburg (Germany) with medical "2-test scenarios". Each student had to work on two problems: diagnosing breast cancer with mammography and sonography test results, and diagnosing HIV infection with the ELISA and Western Blot tests. In Study 1 (N = 190 participants), we systematically varied the presentation of statistical information ("only textual information" vs. "only tree diagram" vs. "text and tree diagram in combination"), whereas in Study 2 (N = 198 participants), we varied the kinds of tree diagrams ("complete tree" vs. "highlighted tree" vs. "pruned tree"). All versions were implemented in probability format (including probability trees) and in natural frequency format (including frequency trees). We found that natural frequency trees, especially when the question-related branches were highlighted, improved performance, but that none of the corresponding probabilistic visualizations did.

Owner only: item control page

Download Statistics

Article Level Metrics (PLOS)

Altmetric

Alternative Statistics (altmetrics)

More literature (via CORE)

Details

Preview

Indexed in

Export bibliographical data

Abstract

Abstract

Download Statistics

Downloads

Article Level Metrics (PLOS)

Alternative Statistics (altmetrics)

More literature (via CORE)

University Library