Abstract:
PURPOSE:In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drug-event pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance. METHODS:We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic. RESULTS:Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation. CONCLUSION:The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drug-event pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs.
journal_name
Pharmacoepidemiol Drug Safjournal_title
Pharmacoepidemiology and drug safetyauthors
Kulldorff M,Dashevsky I,Avery TR,Chan AK,Davis RL,Graham D,Platt R,Andrade SE,Boudreau D,Gunter MJ,Herrinton LJ,Pawloski PA,Raebel MA,Roblin D,Brown JSdoi
10.1002/pds.3423subject
Has Abstractpub_date
2013-05-01 00:00:00pages
517-23issue
5eissn
1053-8569issn
1099-1557journal_volume
22pub_type
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