Methods for observational post-licensure medical product safety surveillance

Stat Methods Med Res. 2015 Apr;24(2):177-93. doi: 10.1177/0962280211413452. Epub 2011 Dec 2.


Post-licensure medical product safety surveillance is important for detecting adverse events potentially not identified pre-licensure. Historically, post-licensure safety monitoring has been accomplished using passive reporting systems and by conducting formal Phase IV randomized trials or large epidemiological studies, also known as safety surveillance or pharmacovigilance studies. However, crucial gaps in the safety evidence base provided by these approaches have led to high profile product withdrawals and growing public concern about unknown health risks associated with licensed products. To address the limitations of existing surveillance systems and to facilitate more accurate and rapid detection of safety problems, new systems involving active surveillance of large, population-based cohorts using observational health care databases are being developed. In this article, we review common statistical methods that have been employed previously for post-licensure safety monitoring, including data mining and sequential hypothesis testing, and assess which methods may be promising for potential use within this newly proposed prospective observational cohort monitoring framework. We discuss gaps in existing approaches and identify areas where methodological development is needed to improve the success of safety surveillance efforts in this setting.

Keywords: data mining; observational study; post-licensure safety; sequential testing.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Biostatistics
  • Clinical Trials, Phase IV as Topic / statistics & numerical data
  • Cohort Studies
  • Data Mining
  • Databases, Factual
  • Device Approval
  • Equipment Safety / statistics & numerical data*
  • Humans
  • Observational Studies as Topic / statistics & numerical data
  • Product Surveillance, Postmarketing / statistics & numerical data*
  • Prospective Studies