ArticleBMC medical informatics and decision making2016
Measuring agreement between healthcare survey instruments using mutual information.
Article in BMC medical informatics and decision making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Nonresponse Bias on Inpatient Rehabilitation Hospitals' Experience of Care Quality Measure Scores.Archives of rehabilitation research and clinical translation · 2024Article
- Evaluating the Psychometric Properties and Clinical Utility of a Digital Psychosocial Self-Screening Tool (HEARTSMAP-U) for Postsecondary Students: Prospective Cohort Study.JMIR mental health · 2023Article
- Fifty years of Shannon information theory in assessing the accuracy and agreement of diagnostic tests.Medical & biological engineering & computing · 2022Review
- Beyond kappa: an informational index for diagnostic agreement in dichotomous and multivalue ordered-categorical ratings.Medical & biological engineering & computing · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHealthcare researchers often use multiple healthcare survey instruments to examine a particular patient symptom. The use of multiple instruments can pose some interesting research questions, such as whether the outcomes produced by the different instruments are in agreement. We tackle this problem using information theory, focusing on mutual information to compare outcomes from multiple healthcare survey instruments.
methodsWe review existing methods of measuring agreement/disagreement between the instruments and suggest a procedure that utilizes mutual information to quantitatively measure the amount of information shared by outcomes from multiple healthcare survey instruments. We also include worked examples to explain the approach.
resultsAs a case study, we employ the suggested procedure to analyze multiple healthcare survey instruments used for detecting delirium superimposed on dementia (DSD) in community-dwelling older adults. In addition, several examples are used to assess the mutual information technique in comparison with other measures, such as odds ratio and Cohen's kappa.
conclusionsAnalysis of mutual information can be useful in explaining agreement/disagreement between multiple instruments. The suggested approach provides new insights into and potential improvements for the application of healthcare survey instruments.
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Registered trials
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