Dataset Information

Development and application of a novel metric to assess effectiveness of biomedical data.

ABSTRACT:

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.

SUBMITTER: Bloom GC

PROVIDER: S-EPMC3753524 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Publications

Development and application of a novel metric to assess effectiveness of biomedical data.

Bloom Gregory C GC Eschrich Steven S Han Gang G Hang Gang G Schabath Matthew B MB Bhansali Neera N Hoerter Andrew M AM Morgan Scott S Fenstermacher David A DA

BMJ open 20130823 8

<h4>Objective</h4>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.<h4>Materials and methods</h4>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 mode ...[more]

PMID: 23975264

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Dataset Information

Development and application of a novel metric to assess effectiveness of biomedical data.

Objective

Materials and methods

Results

Conclusions

Publications

Development and application of a novel metric to assess effectiveness of biomedical data.

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