Division of Biostatistics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Pharmacoepidemiol Drug Saf. 2013 May;22(5):503-9. doi: 10.1002/pds.3419. Epub 2013 Feb 14.
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 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.
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.
药物安全性监测或比较疗效的流行病学研究的成功在很大程度上取决于设计和分析策略，而不仅仅是数据质量。观察性医学结局伙伴关系（OMOP）方法社区实施了一系列具有广泛参数的统计方法，允许进行各种设计和分析。我们的目标是开发一种可视化工具，以探索在给定数据库中，哪些参数设置可能为给定方法提供更好的预测性能。
为每个设置生成了性能指标，包括敏感性（召回率）、特异性（1-FPR）、AUC、MAP 和 P(k)。对所有测试案例和子组，通过主要参数的多元回归，对性能指标进行了运行。基于参数设置和测试案例子组的标准化系数（t 统计量）矩阵，生成了具有顺序调色板的热图，以指示参数对性能指标的影响。
热图有助于研究人员探索评估各种药物-结果关系的方法的设计和分析选项，也有助于探索数据问题。
通过热图进行统计可视化是总结和呈现方法性能结果以及探索方法性能特征和数据限制的参数设置的有用工具。
