Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic

Abstract

Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method—the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds.

Statistical Power for Postlicensure Medical Product Safety Data Mining

“…Brown et al compared Poisson tree-based scan statistics to disproportionality analyses and found reasonable concordance with the two approaches. 6 In other words, when the methods are applied to the same empirical dataset, both techniques showed similar detection capability although no formal power studies were performed for this comparison.…”

“…3 Here, we focus on one data mining method that leverages these longitudinal data: the tree-based scan statistic. 4 Previously, it has been shown to perform well in postmarket medical product safety settings, [5][6][7] and is planned to monitor nine-valent human papillomavirus vaccine exposure in the FDA's Sentinel System. 8 Additionally, the United States Centers for Disease Control and Prevention have indicated that they intend to use the method in their vaccine monitoring system, the Vaccine Safety Datalink.…”

Statistical Power for Postlicensure Medical Product Safety Data Mining

“…Here, we focus on one data mining method that leverages these longitudinal data: the tree-based scan statistic [ 4 ]. Previously, it has been shown to perform well in postmarket medical product safety settings [ 5 6 7 ], and is planned to monitor nine-valent human papillomavirus vaccine exposure in the FDA’s Sentinel System [ 8 ]. Additionally, the United States Centers for Disease Control and Prevention have indicated that they intend to use the method in their vaccine monitoring system, the Vaccine Safety Datalink [ 9 10 ].…”

“…Nelson et al’s paper does not formally compare methods. Brown et al compared Poisson tree-based scan statistics to disproportionality analyses and found reasonable concordance with the two approaches [ 6 ]. In other words, when the methods are applied to the same empirical dataset, both techniques showed similar detection capability although no formal power studies were performed for this comparison.…”

Assessment of Quadrivalent Human Papillomavirus Vaccine Safety Using the Self-Controlled Tree-Temporal Scan Statistic Signal-Detection Method in the Sentinel System

“…We studied the safety of Gardasil, hereafter called quadrivalent human papillomavirus vaccine (4vHPV), applying a self-controlled tree-temporal scan statistic data-mining method ( 22 – 24 ) to health insurance claims data for signal detection. The method allows a wide variety of unsuspected but potential adverse reactions and a range of potential postvaccination periods of increased risk (“risk windows”) to be simultaneously evaluated, adjusting for the multiple testing involved.…”