Robust tools for monitoring the safety of marketed therapeutic products are of paramount importance to public health. In recent years, innovative statistical approaches have been developed to screen large post-marketing safety databases for adverse events (AEs) that occur with disproportionate frequency. These methods, known variously as quantitative signal detection, disproportionality analysis, or safety data mining, facilitate the identification of new safety issues or possible harmful effects of a product. In this article, we describe the statistical concepts behind these methods, as well as their practical application to monitoring the safety of pharmaceutical products using spontaneous AE reports. We also provide examples of how these tools can be used to identify novel drug interactions and demographic risk factors for adverse drug reactions. Challenges, controversies, and frontiers for future research are discussed.

Most regulatory agencies and pharmaceutical companies focus the majority of their pharmacovigilance on safety signal identification in large databases. GlaxoSmithKline (GSK) has > 100 drugs marketed worldwide. In order to determine which database has the highest statistical power to detect safety signals in three large global databases, ten GSK marketed drugs were randomly selected for review in the three databases. At the time of data lock, the FDA database (Adverse Event Reporting System [AERS]) contained approximately 6.2 million total records of adverse drug reactions (ADRs). The WHO database (VIGIBASE) contained 7.2 million total records of ADRs. GSK's global safety database (OCEANS) contained approximately 2 million total ADRs for all of its marketed drugs. For the ten drugs selected, there was an average of 7566 reports found in AERS, 8661 reports found in VIGIBASE and 15,496 reports in OCEANS. The information from all three databases was used in pairs (AERS/OCEANS; AERS/VIGIBASE; and OCEANS/VIGIBASE) to calculate power using the maximum likelihood estimation. The OCEANS database contained more ADRs for all 10 drugs than AERS. OCEANS also contained more ADRs for 8/10 drugs than VIGIBASE. The highest statistical power to detect safety signals was determined by the pair of databases which had the greatest number of reports for the given drug. Based on this data, it was concluded that the highest power may be achieved by combining those databases with the most drug-specific data. It is also believe that early safety signal detection should involve the use of multiple large global databases because this permits the use of the largest number of reports for a given drug, and that reliance on a single database may reduce statistical power and diversity of ADRs.

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Pharmacovigilance objectives and activities are designed to protect the health of consumers and are generally based on data acquisition from spontaneous adverse event reports (SADRs). SADRs come from different sources, including healthcare professionals, consumers, lawyers, other pharmaceutical companies, regulatory agencies and so on. Pharmacovigilance activities derived from SADRs include signal detection and description of the safety profile of the drug. Consumers are the most frequent source of most SADRs, even though the system was originally designed to receive reports from healthcare professionals. Most spontaneous adverse event reports are received from the US. GlaxoSmithKline (GSK) conducts monthly signal detection on all marketed compounds in its global database using disproportionality analysis, the empirical Bayesian algorithm known as a multiple-item gamma-Poisson shrinker. There are no systematic survey data or reviews of actual experiences within existing safety surveillance databases of how pharmaceutical companies handle consumer reports. Thus, a study was undertaken to determine the impact of consumer reports on signal detection using MGPS disproportionality analysis. Two data sets were created for four randomly selected GSK marketed compounds; one data set included reports from both consumer and healthcare providers and the second included only reports from healthcare providers. Disproportionality analysis was then used to evaluate the two data sets. A total of 23 signals were identified with a mean difference in time to signal detection of 1.8 years. The difference was in the range of -8-10 years. In 52.2% of events (12/23), the signal was identified earlier when consumer reports were included in the data. In 34.8% of events (8/23), the signal was identified in the same year in both data sets and, in 13% of the events (3/23), the signal was identified later when consumer reports were included in the data. It was concluded from this study that adverse event reports submitted directly to pharmaceutical companies by consumers can help significantly in the early detection of safety signals.

Objective: Ospemifene is a nonsteroidal selective estrogen receptor modulator (SERM) for the treatment of moderate symptomatic vulvar and vaginal atrophy (VVA) due to menopause. A postauthorization safety study is currently examining the incidence of venous thromboembolism (VTE) among postmenopausal women receiving ospemifene or other SERM (raloxifene, bazedoxifene, or tamoxifen, for noncancer indications), or with untreated VVA. Methods: This interim analysis used the US MarketScan Commercial and Medicare Supplemental claims database from 2013 to 2017 to identify incident VTE. The incidence rate and 95% confidence interval of VTE during the first continuous course of treatment (or continuous untreated time for the untreated cohort) were calculated for each cohort overall and by age group, with sensitivity analyses examining incidence in the short term (up to 90 days) and long term (all available follow-up, regardless of treatment changes). Results: Analyses included 8,188 ospemifene users, 11,777 other SERM users, and 220,242 women with untreated VVA. The incidence per 1,000 person-years and 95% confidence interval of VTE were 3.7 (1.7-7.1) for ospemifene, 11.5 (8.9-14.6) for other SERM, and 11.3 (10.8-11.7) for untreated VVA. Stratification by age and altering the time frame for analysis produced results with similar patterns to the primary analysis. Conclusions: This interim analysis of an ongoing study suggests a favorable safety profile for ospemifene with respect to VTE. Comparative analyses with covariate adjustment will be performed when data accrual is complete.