Active Postmarketing Drug Surveillance for Multiple Adverse Events

• 成果类型: Article
• 署名作者: Goh, Joel; Bjarnadottir, Margret V.; Bayati, Mohsen; Zenios, Stefanos A.
• 署名单位: Harvard University; University System of Maryland; University of Maryland College Park; Stanford University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2015.1435
• 发表日期: 2015
• 页码: 1528-1546
• 关键词: vaccine safety surveillance; coronary-heart-disease; probability ratio test; diabetes-mellitus; cardiovascular-disease; myocardial-infarction; signal generation; clinical-practice; RISK; stroke

摘要

Postmarketing drug surveillance is the process of monitoring the adverse events of pharmaceutical or medical devices after they are approved by the appropriate regulatory authorities. Historically, such surveillance was based on voluntary reports by medical practitioners, but with the widespread adoption of electronic medical records and comprehensive patient databases, surveillance systems that utilize such data are of considerable interest. Unfortunately, existing methods for analyzing the data in such systems ignore the open-ended exploratory nature of such systems that requires the assessment of multiple possible adverse events. In this article, we propose a method, SEQMEDS, that assesses the effect of a single drug on multiple adverse events by analyzing data that accumulate sequentially and explicitly captures interdependencies among the multiple events. The method continuously monitors a vector-valued test-statistic derived from the cumulative number of adverse events. It flags a potential adverse event once the test-statistic crosses a stopping boundary. We employ asymptotic analysis that assumes a large number of observations in a given window of time to show how to compute the stopping boundary by solving a convex optimization problem that achieves a desired Type I error and minimizes the expected time to detection under a pre-specified alternative hypothesis. We apply our method to a model in which the interdependency among the multiple adverse events is captured by a Cox proportional hazards model with time-dependent covariates and demonstrate that it provides an approximation of a fully sequential test for the maximum hazard ratio of the drug over multiple adverse events. A numerical study verifies that our method delivers Type I/II errors that are below pre-specified levels and is robust to distributional assumptions and parameter values.