Data Mining for Adverse Drug Events with a Propensity Score Matched Tree-Based Scan Statistic

Project Title Data Mining for Adverse Drug Events with a Propensity Score Matched Tree-Based Scan Statistic
Date
Wednesday, August 1, 2018
Location
Description

The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis. The authors propose a method that combines tree-based scan statistics with propensity score matched analysis of new initiator cohorts, a robust design for investigations of drug safety.

Authors

Shirley V. Wang, Judith C. Maro, Elande Baro, Rima Izem, Inna Dashevsky, James R. Rogers, Michael Nguyen, Joshua J. Gagne, Elisabetta Patorno, Krista F. Huybrechts, Jacqueline M. Major, Esther Zhou, Megan Reidy, Austin Cosgrove, Sebastian Schneeweiss, Martin Kulldorff

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