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Development and application of a novel metric to assess effectiveness of biomedical data
OBJECTIVE: Design a metric to assess the comparative effectiveness of biomedical data elements within a study that incorporates their statistical relatedness to a given outcome variable as well as a measurement of the quality of their underlying data. MATERIALS AND METHODS: The cohort consisted of 8...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753524/ https://www.ncbi.nlm.nih.gov/pubmed/23975264 http://dx.doi.org/10.1136/bmjopen-2013-003220 |
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author | Bloom, Gregory C Eschrich, Steven Hang, Gang Schabath, Matthew B Bhansali, Neera Hoerter, Andrew M Morgan, Scott Fenstermacher, David A |
author_facet | Bloom, Gregory C Eschrich, Steven Hang, Gang Schabath, Matthew B Bhansali, Neera Hoerter, Andrew M Morgan, Scott Fenstermacher, David A |
author_sort | Bloom, Gregory C |
collection | PubMed |
description | OBJECTIVE: Design a metric to assess the comparative effectiveness of biomedical data elements within a study that incorporates their statistical relatedness to a given outcome variable as well as a measurement of the quality of their underlying data. MATERIALS AND METHODS: The cohort consisted of 874 patients with adenocarcinoma of the lung, each with 47 clinical data elements. The p value for each element was calculated using the Cox proportional hazard univariable regression model with overall survival as the endpoint. An attribute or A-score was calculated by quantification of an element's four quality attributes; Completeness, Comprehensiveness, Consistency and Overall-cost. An effectiveness or E-score was obtained by calculating the conditional probabilities of the p-value and A-score within the given data set with their product equaling the effectiveness score (E-score). RESULTS: The E-score metric provided information about the utility of an element beyond an outcome-related p value ranking. E-scores for elements age-at-diagnosis, gender and tobacco-use showed utility above what their respective p values alone would indicate due to their relative ease of acquisition, that is, higher A-scores. Conversely, elements surgery-site, histologic-type and pathological-TNM stage were down-ranked in comparison to their p values based on lower A-scores caused by significantly higher acquisition costs. CONCLUSIONS: A novel metric termed E-score was developed which incorporates standard statistics with data quality metrics and was tested on elements from a large lung cohort. Results show that an element's underlying data quality is an important consideration in addition to p value correlation to outcome when determining the element's clinical or research utility in a study. |
format | Online Article Text |
id | pubmed-3753524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-37535242013-08-28 Development and application of a novel metric to assess effectiveness of biomedical data Bloom, Gregory C Eschrich, Steven Hang, Gang Schabath, Matthew B Bhansali, Neera Hoerter, Andrew M Morgan, Scott Fenstermacher, David A BMJ Open Research Methods OBJECTIVE: Design a metric to assess the comparative effectiveness of biomedical data elements within a study that incorporates their statistical relatedness to a given outcome variable as well as a measurement of the quality of their underlying data. MATERIALS AND METHODS: The cohort consisted of 874 patients with adenocarcinoma of the lung, each with 47 clinical data elements. The p value for each element was calculated using the Cox proportional hazard univariable regression model with overall survival as the endpoint. An attribute or A-score was calculated by quantification of an element's four quality attributes; Completeness, Comprehensiveness, Consistency and Overall-cost. An effectiveness or E-score was obtained by calculating the conditional probabilities of the p-value and A-score within the given data set with their product equaling the effectiveness score (E-score). RESULTS: The E-score metric provided information about the utility of an element beyond an outcome-related p value ranking. E-scores for elements age-at-diagnosis, gender and tobacco-use showed utility above what their respective p values alone would indicate due to their relative ease of acquisition, that is, higher A-scores. Conversely, elements surgery-site, histologic-type and pathological-TNM stage were down-ranked in comparison to their p values based on lower A-scores caused by significantly higher acquisition costs. CONCLUSIONS: A novel metric termed E-score was developed which incorporates standard statistics with data quality metrics and was tested on elements from a large lung cohort. Results show that an element's underlying data quality is an important consideration in addition to p value correlation to outcome when determining the element's clinical or research utility in a study. BMJ Publishing Group 2013-08-23 /pmc/articles/PMC3753524/ /pubmed/23975264 http://dx.doi.org/10.1136/bmjopen-2013-003220 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Research Methods Bloom, Gregory C Eschrich, Steven Hang, Gang Schabath, Matthew B Bhansali, Neera Hoerter, Andrew M Morgan, Scott Fenstermacher, David A Development and application of a novel metric to assess effectiveness of biomedical data |
title | Development and application of a novel metric to assess effectiveness of biomedical data |
title_full | Development and application of a novel metric to assess effectiveness of biomedical data |
title_fullStr | Development and application of a novel metric to assess effectiveness of biomedical data |
title_full_unstemmed | Development and application of a novel metric to assess effectiveness of biomedical data |
title_short | Development and application of a novel metric to assess effectiveness of biomedical data |
title_sort | development and application of a novel metric to assess effectiveness of biomedical data |
topic | Research Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753524/ https://www.ncbi.nlm.nih.gov/pubmed/23975264 http://dx.doi.org/10.1136/bmjopen-2013-003220 |
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