Statistical visualization for assessing performance of methods for safety surveillance using electronic databases
Article first published online: 14 FEB 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Pharmacoepidemiology and Drug Safety
Volume 22, Issue 5, pages 503–509, May 2013
How to Cite
Li, X., Hui, S., Ryan, P., Rosenman, M. and Overhage, M. (2013), Statistical visualization for assessing performance of methods for safety surveillance using electronic databases. Pharmacoepidem. Drug Safe., 22: 503–509. doi: 10.1002/pds.3419
- Issue published online: 3 MAY 2013
- Article first published online: 14 FEB 2013
- Manuscript Accepted: 22 JAN 2013
- Manuscript Revised: 21 JAN 2013
- Manuscript Received: 17 OCT 2012
- Foundation for the National Institutes of Health. Grant Number: LIXIA11OMOP
- safety surveillance;
- electronic observational databases;
- statistical visualization;
- sensitivity (recall);
- specificity (FPR);
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.
Performance measures were produced for each setting, including sensitivity (recall), specificity (1-FPR), AUC, MAP, and Pk. 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.
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.
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. Copyright © 2013 John Wiley & Sons, Ltd.