An event-based approach for comparing the performance of methods for prospective medical product monitoring.
BACKGROUND
Prospective medical product monitoring is intended to alert stakeholders about whether and when safety problems are identifiable in longitudinal electronic healthcare data. Little attention has been given to how to compare methods in this setting.


OBJECTIVE
To explore aspects of prospective monitoring that should be considered when comparing method performance and to develop a metric that explicitly accounts for these considerations.


METHODS
We reviewed existing metrics and propose an event-based approach that classifies exposed outcomes according to whether a prior alert was generated.


RESULTS
In comparing performance of methods for prospective monitoring, three factors must be considered: (1) accuracy in alerting; (2) timeliness of alerting; and (3) the trade-offs between the costs of false negative and false positive alerting. Traditional scenario-based measures of accuracy, such as sensitivity and specificity, which classify only at the end of monitoring, fail to appreciate timeliness of alerting and impose fixed tradeoffs between false positive versus false negative consequences. We provide an expression that summarizes event-based sensitivity (the proportion of exposed events that occur after alerting among all exposed events in scenarios with true safety issues) and event-based specificity (the proportion of exposed events that occur in the absence of alerting among all exposed events in scenarios with no true safety issues) by taking an average weighted by relative costs of false positive and false negative alerting.


CONCLUSIONS
The proposed approach explicitly accounts for accuracy in alerting, timeliness in alerting, and the trade-offs between the costs of false negative and false positive alerting.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. jgagne1@partners.org
Joshua J
JJ
Gagne
Alexander M
AM
Walker
Robert J
RJ
Glynn
Jeremy A
JA
Rassen
Sebastian
S
Schneeweiss
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data / Q000639:trends*; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods* / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions; D005188:False Negative Reactions; D005189:False Positive Reactions; D015233:Models, Statistical; D060735:Pharmacovigilance; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity