title: Statistical visualization for assessing performance of methods for safety surveillance using electronic databases.
abstract: OBJECTIVE
  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.
authors: Division of Biostatistics, Indiana University School of Medicine, Indianapolis, IN 46202, USA. xiaochun@iupui.edu
authors: Xiaochun
authors: X
authors: Li
authors: Siu
authors: S
authors: Hui
authors: Patrick
authors: P
authors: Ryan
authors: Marc
authors: M
authors: Rosenman
authors: Marc
authors: M
authors: Overhage
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D017891:Pharmacoepidemiology / Q000379:methods*; D011237:Predictive Value of Tests; D011358:Product Surveillance, Postmarketing / Q000379:methods; D012044:Regression Analysis; D012680:Sensitivity and Specificity