We have located links that may give you full text access.
JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
Statistical visualization for assessing performance of methods for safety surveillance using electronic databases.
Pharmacoepidemiology and Drug Safety 2013 May
PURPOSE: The success of an epidemiological study for drug safety surveillance or comparative effectiveness depends largely on design and analysis strategies besides data quality. The Observational Medical Outcomes Partnership (OMOP) methods community implemented a collection of statistical methods with extensive parameters allowing a wide variety of designs and analyses. Our objective was to develop a visualization tool to explore which parameter settings may enable better predictive properties for a given method in a database.
METHODS: Performance measures were produced for each setting, including sensitivity (recall), specificity (1-FPR), AUC, MAP, and P(k). Multiple regressions with relevant parameters as main effects were run for performance measures on all test cases and subgroups. Heatmaps with sequential palettes to indicate the parameters' impacts on performance measures were generated based on matrices of the standardized coefficients (t-statistics) by parameter settings and test case subgroups.
RESULTS: Heatmaps help researchers to explore design and analysis options of methods for evaluating a variety of drug-outcome relationships and also to explore data issues.
CONCLUSIONS: Statistical visualization through heatmaps is a useful tool for summarizing and presenting method performance results and for the exploration of the parameter settings for method performance characteristics and data limitations.
METHODS: Performance measures were produced for each setting, including sensitivity (recall), specificity (1-FPR), AUC, MAP, and P(k). Multiple regressions with relevant parameters as main effects were run for performance measures on all test cases and subgroups. Heatmaps with sequential palettes to indicate the parameters' impacts on performance measures were generated based on matrices of the standardized coefficients (t-statistics) by parameter settings and test case subgroups.
RESULTS: Heatmaps help researchers to explore design and analysis options of methods for evaluating a variety of drug-outcome relationships and also to explore data issues.
CONCLUSIONS: Statistical visualization through heatmaps is a useful tool for summarizing and presenting method performance results and for the exploration of the parameter settings for method performance characteristics and data limitations.
Full text links
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app