Literature DB >> 19691034

A conditional sequential sampling procedure for drug safety surveillance.

Lingling Li1.   

Abstract

We propose a practical group sequential method, a conditional sequential sampling procedure, to test if a drug of interest (D) leads to an elevated risk for an adverse event E compared with a comparison drug C. The method is designed for prospective drug safety surveillance studies, in which, for each considered drug, a summary table with the exposed person-times and the associated numbers of adverse events summed by strata defined by several potential confounders, is collected and updated periodically using the health plans' administrative claims data. This new approach can be applied to test for elevated relative risk whenever the data are updated. Our approach adjusts for multiple testing to preserve the overall type I error with any specified alpha-spending function. Furthermore, it automatically adjusts for temporal trend and population heterogeneity across strata by conditioning on the numbers of adverse events within each stratum during each time period. Therefore, this approach is very flexible and applies to a wide class of settings. We conduct a simulation study to evaluate its performance under various scenarios. The approach is also applied to an example to examine if Rofecoxib leads to an increased relative risk for acute myocardial infraction (AMI) compared with its two counterparts Diclofenac and Naproxen, respectively. We end with discussions.

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Year:  2009        PMID: 19691034     DOI: 10.1002/sim.3689

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  4 in total

1.  Managing data quality for a drug safety surveillance system.

Authors:  Abraham G Hartzema; Christian G Reich; Patrick B Ryan; Paul E Stang; David Madigan; Emily Welebob; J Marc Overhage
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

2.  Orphan therapies: making best use of postmarket data.

Authors:  Judith C Maro; Jeffrey S Brown; Gerald J Dal Pan; Lingling Li
Journal:  J Gen Intern Med       Date:  2014-08       Impact factor: 5.128

3.  Massive parallelization of serial inference algorithms for a complex generalized linear model.

Authors:  Marc A Suchard; Shawn E Simpson; Ivan Zorych; Patrick Ryan; David Madigan
Journal:  ACM Trans Model Comput Simul       Date:  2013-01       Impact factor: 1.075

4.  Signal detection and monitoring based on longitudinal healthcare data.

Authors:  Marc Suling; Iris Pigeot
Journal:  Pharmaceutics       Date:  2012-12-13       Impact factor: 6.321

  4 in total

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