Impact of exposure accrual on sequential postmarket evaluations: a simulation study.
OBJECTIVE
We frame the challenges in conducting sequential database surveillance (SDS) analyses and use simulation techniques to illustrate one of these aspects: the accrual of exposed person-time. We discuss the role of SDS analyses in the US Food and Drug Administration's planned Sentinel System and suggest the outline of a decision-analytic framework that might aid regulatory authorities in planning postmarket surveillance.


METHODS
We trace exposure accrual using US Medicaid data for new molecular entities approved in 2005 and 2006 and simulate risk estimates that are observable using SDS by varying the event rate of the health outcome of interest (HOI) in the control group.


RESULTS
Sequential database surveillance may considerably narrow risk estimates for most products within 2 years when the underlying control group HOI occurs commonly (i.e., at least 1 HOI/100 person-years). Exposure accrual may be insufficient for detecting relative risks with infrequent, rare, or very rare HOIs (i.e., less frequent than 1 event/1000 person-years). However, if SDS is used to quickly detect or rule out an incremental risk difference, then a majority of products are viable SDS candidates.


CONCLUSIONS
As a surveillance tool, SDS performs differently depending on whether its intended regulatory target is to narrow relative risk estimates or to confirm/rule out absolute levels of excess risk. These desired endpoints, in addition to the number of exposures available for analysis and the underlying HOI rates, influence the number of products that would be viable candidates for SDS.
Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA, USA. jmaro@mit.edu
Judith C
JC
Maro
Jeffrey S
JS
Brown
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data; D003198:Computer Simulation / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data; D017277:Drug Approval / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D016012:Poisson Distribution; D011358:Product Surveillance, Postmarketing / Q000706:statistics & numerical data*; D012107:Research Design; D012306:Risk; D018570:Risk Assessment / Q000706:statistics & numerical data; D018571:Sentinel Surveillance; D012984:Software; D013997:Time Factors; D014481:United States; D014486:United States Food and Drug Administration / Q000706:statistics & numerical data