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Statistical visualization for assessing performance of methods for safety surveillance using electronic databases
Author(s) -
Li Xiaochun,
Hui Siu,
Ryan Patrick,
Rosenman Marc,
Overhage Marc
Publication year - 2013
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.3419
Subject(s) - visualization , observational study , data mining , variety (cybernetics) , medicine , computer science , statistical hypothesis testing , data visualization , database , data science , statistics , artificial intelligence , pathology , mathematics
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. Copyright © 2013 John Wiley & Sons, Ltd.