Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic
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 cond...
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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MDPI AG,
2013-03-01T00:00:00Z.
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| LEADER | 00000 am a22000003u 4500 | ||
|---|---|---|---|
| 001 | doaj_8eb7d379bd084f9c9bcdc9bae0c49e43 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a David Smith |e author |
| 700 | 1 | 0 | |a Robert Reynolds |e author |
| 700 | 1 | 0 | |a Marsha A. Raebel |e author |
| 700 | 1 | 0 | |a Douglas Roblin |e author |
| 700 | 1 | 0 | |a Margaret J. Gunter |e author |
| 700 | 1 | 0 | |a Lisa Herrinton |e author |
| 700 | 1 | 0 | |a Pamala A. Pawloski |e author |
| 700 | 1 | 0 | |a Denise Boudreau |e author |
| 700 | 1 | 0 | |a Susan E. Andrade |e author |
| 700 | 1 | 0 | |a K. Arnold Chan |e author |
| 700 | 1 | 0 | |a Taliser R. Avery |e author |
| 700 | 1 | 0 | |a Robert L. Davis |e author |
| 700 | 1 | 0 | |a Martin Kulldorff |e author |
| 700 | 1 | 0 | |a Andrew Bate |e author |
| 700 | 1 | 0 | |a Fang Zhang |e author |
| 700 | 1 | 0 | |a Inna Dashevsky |e author |
| 700 | 1 | 0 | |a Kenneth R. Petronis |e author |
| 700 | 1 | 0 | |a Jeffrey S. Brown |e author |
| 245 | 0 | 0 | |a Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic |
| 260 | |b MDPI AG, |c 2013-03-01T00:00:00Z. | ||
| 500 | |a 10.3390/pharmaceutics5010179 | ||
| 500 | |a 1999-4923 | ||
| 520 | |a 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. | ||
| 546 | |a EN | ||
| 690 | |a pharmacovigilance | ||
| 690 | |a drug safety surveillance | ||
| 690 | |a adverse events data mining | ||
| 690 | |a gamma Poisson shrinkage | ||
| 690 | |a tree-based scan statistic | ||
| 690 | |a Pharmacy and materia medica | ||
| 690 | |a RS1-441 | ||
| 655 | 7 | |a article |2 local | |
| 786 | 0 | |n Pharmaceutics, Vol 5, Iss 1, Pp 179-200 (2013) | |
| 787 | 0 | |n http://www.mdpi.com/1999-4923/5/1/179 | |
| 787 | 0 | |n https://doaj.org/toc/1999-4923 | |
| 856 | 4 | 1 | |u https://doaj.org/article/8eb7d379bd084f9c9bcdc9bae0c49e43 |z Connect to this object online. |