Citation

Rassen JA, Schneeweiss S. Pharmacoepidemiol. Drug Saf. 2012; 21(Suppl 1): 41-49.

Affiliation

Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. jrassen@post.harvard.edu.

Copyright

(Copyright © 2012, John Wiley and Sons)

DOI

10.1002/pds.2328

PMID

22262592

Abstract

Distributed medical product safety monitoring systems such as the Sentinel System, to be developed as a part of Food and Drug Administration's Sentinel Initiative, will require automation of large parts of the safety evaluation process to achieve the necessary speed and scale at reasonable cost without sacrificing validity. Although certain functions will require investigator intervention, confounding control is one area that can largely be automated. The high-dimensional propensity score (hd-PS) algorithm is one option for automated confounding control in longitudinal healthcare databases. In this article, we discuss the use of hd-PS for automating confounding control in sequential database cohort studies, as applied to safety monitoring systems. In particular, we discuss the robustness of the covariate selection process, the potential for over- or under-selection of variables including the possibilities of M-bias and Z-bias, the computation requirements, the practical considerations in a federated database network, and the cases where automated confounding adjustment may not function optimally. We also outline recent improvements to the algorithm and show how the algorithm has performed in several published studies. We conclude that despite certain limitations, hd-PS offers substantial advantages over non-automated alternatives in active product safety monitoring systems. Copyright © 2012 John Wiley & Sons, Ltd.

Language: en