A simple Cox approach to estimating risk ratios without sharing individual-level data in multi-site studies.

Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (e.g., distributed regression) or only provide approximate estimation of the risk ratio (e.g., meta-analysis). Here we develop a practical method that requires a single transfer of eight summary-level quantities from each data partner. Our approach leverages an existing risk-set method and software originally developed for Cox regression. Sharing only summary-level information, the proposed method provides risk ratio estimates and confidence intervals identical to those that would be provided - if individual-level data were pooled - by the modified Poisson regression. We justify the method theoretically, confirm its performance using simulated data, and implement it in a distributed analysis of COVID-19 data from the U.S. Food and Drug Administration's Sentinel System.

Triple challenges - Small sample size in both exposure and control groups to scan rare maternal outcomes in a signal identification approach: A simulation study.

There is a dearth of safety data on maternal outcomes after perinatal medication exposure. Data-mining for unexpected adverse event occurrence in existing datasets is a potentially useful approach. One method, the Poisson tree-based scan statistic (TBSS), assumes that the expected outcome counts, based on incidence of outcomes in the control group, are estimated without error. This assumption may be difficult to satisfy with a small control group. Our simulation study evaluated the effect of imprecise incidence proportions from the control group on TBSS' ability to identify maternal outcomes in pregnancy research. We simulated base case analyses with "true" expected incidence proportions and compared these to imprecise incidence proportions derived from sparse control samples. We varied parameters impacting Type I error and statistical power (exposure group size, outcome's incidence proportion, and effect size). We found that imprecise incidence proportions generated by a small control group resulted in inaccurate alerting, inflation of Type I error, and removal of very rare outcomes for TBSS analysis due to "zero" background counts. Ideally, the control size should be at least several times larger than the exposure size to limit the number of false positive alerts and retain statistical power for true alerts.

Use of the Sentinel System to Examine Medical Product Use and Outcomes During Pregnancy.

While many pregnant individuals use prescription medications, evidence supporting product safety during pregnancy is often inadequate. Existing electronic healthcare data sources provide large, diverse samples of health plan members to allow for the study of medical product utilization during pregnancy, as well as pregnancy, maternal, and infant outcomes. The Sentinel System is a national medical product surveillance system that includes administrative claims and electronic health record databases from large national and regional health insurers. In addition to these data sources, Sentinel develops and maintains a sizeable selection of analytic tools to facilitate epidemiologic analyses in a way that protects patient privacy and health system autonomy. In this article, we provide an overview of Sentinel System infrastructure, including the Mother-Infant Linkage Table, parameterizable analytic tools, and algorithms to estimate gestational age and identify pregnancy outcomes. We also describe past and future Sentinel work that contributes to our understanding of the way medical products are used and the safety of these products during pregnancy.

Transparency, reproducibility, and replicability of pharmacoepidemiology studies in a distributed network environment.

PURPOSE: Our objective is to describe how the U.S. Food and Drug Administration (FDA)'s Sentinel System implements best practices to ensure trust in drug safety studies using real-world data from disparate sources. METHODS: We present a stepwise schematic for Sentinel's data harmonization, data quality check, query design and implementation, and reporting practices, and describe approaches to enhancing the transparency, reproducibility, and replicability of studies at each step. CONCLUSIONS: Each Sentinel data partner converts its source data into the Sentinel Common Data Model. The transformed data undergoes rigorous quality checks before it can be used for Sentinel queries. The Sentinel Common Data Model framework, data transformation codes for several data sources, and data quality assurance packages are publicly available. Designed to run against the Sentinel Common Data Model, Sentinel's querying system comprises a suite of pre-tested, parametrizable computer programs that allow users to perform sophisticated descriptive and inferential analysis without having to exchange individual-level data across sites. Detailed documentation of capabilities of the programs as well as the codes and information required to execute them are publicly available on the Sentinel website. Sentinel also provides public trainings and online resources to facilitate use of its data model and querying system. Its study specifications conform to established reporting frameworks aimed at facilitating reproducibility and replicability of real-world data studies. Reports from Sentinel queries and associated design and analytic specifications are available for download on the Sentinel website. Sentinel is an example of how real-world data can be used to generate regulatory-grade evidence at scale using a transparent, reproducible, and replicable process.

Safety signal identification for COVID-19 bivalent booster vaccination using tree-based scan statistics in the Vaccine Safety Datalink.

BACKGROUND: Traditional active vaccine safety monitoring involves pre-specifying health outcomes and biologically plausible outcome-specific time windows of concern, limiting the adverse events that can be evaluated. In this study, we used tree-based scan statistics to look broadly for >60,000 possible adverse events after bivalent COVID-19 vaccination. METHODS: Vaccine Safety Datalink enrollees aged ≥5 years receiving Moderna or Pfizer-BioNTech bivalent COVID-19 vaccine through November 2022 were followed for 56 days post-vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within the hierarchical ICD-10-CM diagnosis code "tree" and temporally within post-vaccination follow-up. The conditional self-controlled tree-temporal scan statistic was used, conditioning on total number of cases of each diagnosis and total number of cases of any diagnosis occurring during the scanning risk window across the entire tree. P = 0.01 was the pre-specified cut-off for statistical significance. RESULTS: Analysis included 352,509 doses of Moderna and 979,189 doses of Pfizer-BioNTech bivalent vaccines. After Moderna vaccination, no statistically significant clusters were found. After Pfizer-BioNTech, there were clusters of unspecified adverse events (Days 1-3, p = 0.0001-0.0007), influenza (Days 35-56, p = 0.0001), cough (Days 44-55, p = 0.0002), and COVID-19 (Days 52-56, p = 0.0004). CONCLUSIONS: For Pfizer-BioNTech only, we detected clusters of: (1) unspecified adverse effects, as have been observed in other vaccine studies using this method, and (2) respiratory disease toward the end of follow-up. The respiratory clusters were likely due to overlap of follow-up with the spread of respiratory syncytial virus, influenza, and COVID-19, i.e., confounding by seasonality. The untargeted nature of the method and its inherent adjustment for the many diagnoses and risk intervals evaluated are unique advantages. Limitations include susceptibility to time-varying confounding, lower statistical power for assessing risks of specific outcomes than in traditional studies targeting fewer outcomes, and the possibility of missing adverse events not strongly clustered in time or within the "tree."

Six Years of the US Food and Drug Administration's Postmarket Active Risk Identification and Analysis System in the Sentinel Initiative: Implications for Real World Evidence Generation.

Congress mandated the creation of a postmarket Active Risk Identification and Analysis (ARIA) system containing data on 100 million individuals for monitoring risks associated with drug and biologic products using data from disparate sources to complement the US Food and Drug Administration's (FDA's) existing postmarket capabilities. We report on the first 6 years of ARIA utilization in the Sentinel System (2016-2021). The FDA has used the ARIA system to evaluate 133 safety concerns; 54 of these evaluations have closed with regulatory determinations, whereas the rest remain in progress. If the ARIA system and the FDA's Adverse Event Reporting System are deemed insufficient to address a safety concern, then the FDA may issue a postmarket requirement to a product's manufacturer. One hundred ninety-seven ARIA insufficiency determinations have been made. The most common situation for which ARIA was found to be insufficient is the evaluation of adverse pregnancy and fetal outcomes following in utero drug exposure, followed by neoplasms and death. ARIA was most likely to be sufficient for thromboembolic events, which have high positive predictive value in claims data alone and do not require supplemental clinical data. The lessons learned from this experience illustrate the continued challenges using administrative claims data, especially to define novel clinical outcomes. This analysis can help to identify where more granular clinical data are needed to fill gaps to improve the use of real-world data for drug safety analyses and provide insights into what is needed to efficiently generate high-quality real-world evidence for efficacy.

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.

INTRODUCTION: Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance. METHODS: To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices. RESULTS: We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations. CONCLUSION: Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Valsartan, Losartan and Irbesartan use in the USA, UK, Canada and Denmark after the nitrosamine recalls: a descriptive cohort study.

OBJECTIVES: To examine valsartan, losartan and irbesartan usage and switching patterns in the USA, UK, Canada and Denmark before and after July 2018, when the first Angiotensin-Receptor-Blocker (ARB) (valsartan) was recalled. DESIGN: Retrospective cohort study. SETTING: USA, Canadian administrative healthcare data, Danish National Prescription Registry and UK primary care electronic health records. PARTICIPANTS: Patients aged 18 years and older between January 2014 and December 2020. INTERVENTION: Valsartan, losartan and irbesartan. MAIN OUTCOME: Monthly percentages of individual ARB episodes, new users and switches to another ARB, ACE inhibitors (ACEI) or calcium channel blockers containing products. RESULTS: We identified 10.8, 3.2, 1.8 and 1.2 million ARB users in the USA, UK, Canada and Denmark, respectively. Overall proportions of valsartan, losartan and irbesartan use were 18.4%, 67.9% and 5.2% in the USA; 3.1%, 48.3% and 10.2% in the UK, 16.3%, 11.4% and 18.3% in Canada, 1%, 93.5% and 0.6% in Denmark. In July 2018, we observed an immediate steep decline in the proportion of valsartan use in the USA and Canada. A similar trend was observed in Denmark; however, the decline was only minimal. We observed no change in trends of ARB use in the UK. Accompanying the valsartan decline was an increase in switching to other ARBs in the USA, Canada and Denmark. There was a small increase in switching to ACEI relative to the valsartan-to-other-ARBs switch. We also observed increased switching from other affected ARBs, losartan and irbesartan, to other ARBs throughout 2019, in the USA and Canada, although the usage trends in the USA remained unchanged. CONCLUSION: The first recall notice for valsartan resulted in substantial decline in usage due to increased switching to other ARBs. Subsequent notices for losartan and irbesartan were also associated with increased switching around the time of the recall, however, overall usage trends remained unchanged.

Monitoring Drug Safety in Pregnancy with Scan Statistics: A Comparison of Two Study Designs.

BACKGROUND: Traditional surveillance of adverse infant outcomes following maternal medication exposures relies on pregnancy exposure registries, which are often underpowered. We characterize the statistical power of TreeScan, a data mining tool, to identify potential signals in the setting of perinatal medication exposures and infant outcomes. METHODS: We used empirical data to inform background incidence of major congenital malformations and other birth conditions. Statistical power was calculated using two probability models compatible with TreeScan, Bernoulli and Poisson, while varying the sample size, magnitude of the risk increase, and incidence of a specified outcome. We also simulated larger referent to exposure matching ratios when using the Bernoulli model in the setting of fixed N:1 propensity score matching. Finally, we assessed the impact of outcome misclassification on power. RESULTS: The Poisson model demonstrated greater power to detect signals than the Bernoulli model across all scenarios and suggested a sample size of 4,000 exposed pregnancies is needed to detect a twofold increase in risk of a common outcome (approximately 8 per 1,000) with 85% power. Increasing the fixed matching ratio with the Bernoulli model did not reliably increase power. An outcome definition with high sensitivity is expected to have somewhat greater power to detect signals than an outcome definition with high positive predictive value. CONCLUSIONS: Use of the Poisson model with an outcome definition that prioritizes sensitivity may be optimal for signal detection. TreeScan is a viable method for surveillance of adverse infant outcomes following maternal medication use.

A broad assessment of covid-19 vaccine safety using tree-based data-mining in the vaccine safety datalink.

BACKGROUND: Except for spontaneous reporting systems, vaccine safety monitoring generally involves pre-specifying health outcomes and post-vaccination risk windows of concern. Instead, we used tree-based data-mining to look more broadly for possible adverse events after Pfizer-BioNTech, Moderna, and Janssen COVID-19 vaccination. METHODS: Vaccine Safety Datalink enrollees receiving ≥1 dose of COVID-19 vaccine in 2020-2021 were followed for 70 days after Pfizer-BioNTech or Moderna and 56 days after Janssen vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the post-vaccination follow-up period. We used the self-controlled tree-temporal scan statistic and TreeScan software. Monte Carlo simulation was used to estimate p-values; p = 0.01 was the pre-specified cut-off for statistical significance of a cluster. RESULTS: There were 4.1, 2.6, and 0.4 million Pfizer-BioNTech, Moderna, and Janssen vaccinees, respectively. Clusters after Pfizer-BioNTech vaccination included: (1) unspecified adverse effects, (2) common vaccine reactions, such as fever, myalgia, and headache, (3) myocarditis/pericarditis, and (4) less specific cardiac or respiratory symptoms, all with the strongest clusters generally after Dose 2; and (5) COVID-19/viral pneumonia/sepsis/respiratory failure in the first 3 weeks after Dose 1. Moderna results were similar but without a significant myocarditis/pericarditis cluster. Further investigation suggested the fifth signal group was a manifestation of mRNA vaccine effectiveness after the first 3 weeks. Janssen vaccinees had clusters of unspecified or common vaccine reactions, gait/mobility abnormalities, and muscle weakness. The latter two were deemed to have arisen from confounding related to practices at one site. CONCLUSIONS: We detected post-vaccination clusters of unspecified adverse effects, common vaccine reactions, and, for the mRNA vaccines, chest pain and palpitations, as well as myocarditis/pericarditis after Pfizer-BioNTech Dose 2. Unique advantages of this data mining are its untargeted nature and its inherent adjustment for the multiplicity of diagnoses and risk intervals scanned.

Tree-based data mining for safety assessment of first COVID-19 booster doses in the Vaccine Safety Datalink.

BACKGROUND: The Centers for Disease Control and Prevention's Vaccine Safety Datalink (VSD) has been performing safety surveillance for COVID-19 vaccines since their earliest authorization in the United States. Complementing its real-time surveillance for pre-specified health outcomes using pre-specified risk intervals, the VSD conducts tree-based data-mining to look for clustering of a broad range of health outcomes after COVID-19 vaccination. This study's objective was to use this untargeted, hypothesis-generating approach to assess the safety of first booster doses of Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Ad26.COV2.S) COVID-19 vaccines. METHODS: VSD enrollees receiving a first booster of COVID-19 vaccine through April 2, 2022 were followed for 56 days. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the follow-up period. The self-controlled tree-temporal scan statistic was used, conditioning on the total number of cases for each diagnosis. P-values were estimated by Monte Carlo simulation; p = 0.01 was pre-specified as the cut-off for statistical significance of clusters. RESULTS: More than 2.4 and 1.8 million subjects received Pfizer-BioNTech and Moderna boosters after an mRNA primary series, respectively. Clusters of urticaria/allergy/rash were found during Days 10-15 after the Moderna booster (p = 0.0001). Other outcomes that clustered after mRNA boosters, mostly with p = 0.0001, included unspecified adverse effects, common vaccine-associated reactions like fever and myalgia, and COVID-19. COVID-19 clusters were in Days 1-10 after booster receipt, before boosters would have become effective. There were no noteworthy clusters after boosters following primary Janssen vaccination. CONCLUSIONS: In this untargeted data-mining study of COVID-19 booster vaccination, a cluster of delayed-onset urticaria/allergy/rash was detected after the Moderna booster, as has been reported after Moderna vaccination previously. Other clusters after mRNA boosters were of unspecified or common adverse effects and COVID-19, the latter evidently reflecting immunity to COVID-19 after 10 days.

Sequential Data-Mining for Adverse Events After Recombinant Herpes Zoster Vaccination Using the Tree-Based Scan Statistic.

Tree-based scan statistics have been successfully used to study the safety of several vaccines without prespecifying health outcomes of concern. In this study, the binomial tree-based scan statistic was applied sequentially to detect adverse events in days 1-28 compared with days 29-56 after recombinant herpes zoster (RZV) vaccination, with 5 looks at the data and formal adjustment for the repeated analyses over time. IBM MarketScan data on commercially insured persons ≥50 years of age receiving RZV during January 1, 2018, to May 5, 2020, were used. With 999,876 doses of RZV included, statistically significant signals were detected only for unspecified adverse effects/complications following immunization, with attributable risks as low as 2 excess cases per 100,000 vaccinations. Ninety percent of cases in the signals occurred in the week after vaccination and, based on previous studies, likely represent nonserious events like fever, fatigue, and headache. Strengths of our study include its untargeted nature, self-controlled design, and formal adjustment for repeated testing. Although the method requires prespecification of the risk window of interest and may miss some true signals detectable using the tree-temporal variant of the method, it allows for early detection of potential safety problems through early initiation of ongoing monitoring.

Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System.

PURPOSE: It is a priority of the US Food and Drug Administration (FDA) to monitor the safety of medications used during pregnancy. Pregnancy exposure registries and cohort studies utilizing electronic health record data are primary sources of information but are limited by small sample sizes and limited outcome assessment. TreeScan™, a statistical data mining tool, can be applied within the FDA Sentinel System to simultaneously identify multiple potential adverse neonatal and infant outcomes after maternal medication exposure. METHODS: We implemented TreeScan using the Sentinel analytic tools in a cohort of linked live birth deliveries and infants nested in the IBM MarketScan® Research Database. As a case study, we compared first trimester fluoroquinolone use and cephalosporin use. We used the Bernoulli and Poisson TreeScan statistics with compatible propensity score-based study designs for confounding control (matching and stratification) and used multiple propensity score models with various strategies for confounding control to inform best practices. We developed a hierarchical outcome tree including major congenital malformations and outcomes of gestational length and birth weight. RESULTS: A total of 1791 fluoroquinolone-exposed and 8739 cephalosporin-exposed mother-infant pairs were eligible for analysis. Both TreeScan analysis methods resulted in single alerts that were deemed to be due to uncontrolled confounding or otherwise not warranting follow-up. CONCLUSIONS: In this implementation of TreeScan using Sentinel analytic tools, we did not observe any new safety signals for fluoroquinolone use in the first trimester. TreeScan, with tailored or high-dimensional propensity scores for confounding control, is a valuable tool in addition to current safety surveillance methods for medications used during pregnancy.

Assessing medical product safety during pregnancy using parameterizable tools in the sentinel distributed database.

PURPOSE: The US Food and Drug Administration established the Sentinel System to monitor the safety of medical products. A component of this system includes parameterizable analytic tools to identify mother-infant pairs and evaluate infant outcomes to enable the routine monitoring of the utilization and safety of drugs used in pregnancy. We assessed the feasibility of using the data and tools in the Sentinel System by assessing a known association between topiramate use during pregnancy and oral clefts in the infant. METHODS: We identified mother-infant pairs using the mother-infant linkage table from six data partners contributing to the Sentinel Distributed Database from January 1, 2000, to September 30, 2015. We compared mother-infant pairs with first-trimester exposure to topiramate to mother-infant pairs that were topiramate-unexposed or lamotrigine-exposed and used a validated algorithm to identify oral clefts in the infant. We estimated adjusted risk ratios through propensity score stratification. RESULTS: There were 2007 topiramate-exposed and 1 066 086 unexposed mother-infant pairs in the main comparison. In the active-comparator analysis, there were 1996 topiramate-exposed and 2859 lamotrigine-exposed mother-infant pairs. After propensity score stratification, the odds ratio for oral clefts was 2.92 (95% CI: 1.43, 5.93) comparing the topiramate-exposed to unexposed groups and 2.72 (95% CI: 0.75, 9.93) comparing the topiramate-exposed to lamotrigine-exposed groups. CONCLUSIONS: We found an increased risk of oral clefts after topiramate exposure in the first trimester in the Sentinel database. These results are similar to prior published observational study results and demonstrate the ability of Sentinel's data and analytic tools to assess medical product safety in cohorts of mother-infant pairs in a timely manner.

The US Food and Drug Administration Sentinel System: a national resource for a learning health system.

The US Food and Drug Administration (FDA) created the Sentinel System in response to a requirement in the FDA Amendments Act of 2007 that the agency establish a system for monitoring risks associated with drug and biologic products using data from disparate sources. The Sentinel System has completed hundreds of analyses, including many that have directly informed regulatory decisions. The Sentinel System also was designed to support a national infrastructure for a learning health system. Sentinel governance and guiding principles were designed to facilitate Sentinel's role as a national resource. The Sentinel System infrastructure now supports multiple non-FDA projects for stakeholders ranging from regulated industry to other federal agencies, international regulators, and academics. The Sentinel System is a working example of a learning health system that is expanding with the potential to create a global learning health system that can support medical product safety assessments and other research.

Invited Commentary: Go BIG and Go Global-Executing Large-Scale, Multisite Pharmacoepidemiologic Studies Using Real-World Data.

At the time medical products are approved, we rarely know enough about their comparative safety and effectiveness vis-à-vis alternative therapies to advise patients and providers. Postmarket generation of evidence on rare adverse events following medical product exposure increasingly requires analysis of millions of longitudinal patient records that can provide complete capture of data on patient experiences. In the accompanying article by Pradhan et al. (Am J Epidemiology. 2022;191(8):1352-1367), the authors demonstrate how observational database studies are often the most practical approach, provided these databases are carefully chosen to be "fit for purpose." Distributed data networks with common data models have proliferated in the last 2 decades in pharmacoepidemiology, allowing efficient capture of patient data in a standardized and structured format across disparate real-world data sources. Use of common data models facilitates transparency by allowing standardized programming approaches that can be easily reproduced. The distributed data network architecture, combined with a common data approach, supports not only multisite observational studies but also pragmatic clinical trials. It also helps bridge international boundaries and further increases the sample size and diversity of study populations.

Utility of fertility procedures and prenatal tests to estimate gestational age for live-births and stillbirths in electronic health plan databases.

PURPOSE: Current algorithms to evaluate gestational age (GA) during pregnancy rely on hospital coding at delivery and are not applicable to non-live births. We developed an algorithm using fertility procedures and fertility tests, without relying on delivery coding, to develop a novel GA algorithm in live-births and stillbirths. METHODS: Three pregnancy cohorts were identified from 16 health-plans in the Sentinel System: 1) hospital admissions for live-birth, 2) hospital admissions for stillbirth, and 3) medical chart-confirmed stillbirths. Fertility procedures and prenatal tests, recommended within specific GA windows were evaluated for inclusion in our GA algorithm. Our GA algorithm was developed against a validated delivery-based GA algorithm in live-births, implemented within a sample of chart-confirmed stillbirths, and compared to national estimates of GA at stillbirth. RESULTS: Our algorithm, including fertility procedures and 11 prenatal tests, assigned a GA at delivery to 97.9% of live-births and 92.6% of stillbirths. For live-births (n = 4 701 207), it estimated GA within 2 weeks of a reference delivery-based GA algorithm in 82.5% of pregnancies, with a mean difference of 3.7 days. In chart-confirmed stillbirths (n = 49), it estimated GA within 2 weeks of the clinically recorded GA at delivery for 80% of pregnancies, with a mean difference of 11.1 days. Implementation of the algorithm in a cohort of stillbirths (n = 40 484) had an increased percentage of deliveries after 36 weeks compared to national estimates. CONCLUSIONS: In a population of primarily commercially-insured pregnant women, fertility procedures and prenatal tests can estimate GA with sufficient sensitivity and accuracy for utility in pregnancy studies.

A Broad Safety Assessment of the Recombinant Herpes Zoster Vaccine.

The recombinant herpes zoster vaccine (RZV), approved as a 2-dose series in the United States in October 2017, has proven highly effective and generally safe. However, a small risk of Guillain-Barré syndrome after vaccination was identified after approval, and questions remain about other possible adverse events. This data-mining study assessed RZV safety in the United States using the self-controlled tree-temporal scan statistic, scanning data on thousands of diagnoses recorded during follow-up to detect any statistically unusual temporal clustering of cases within a large hierarchy of diagnoses. IBM MarketScan data on commercially insured persons at least 50 years of age receiving RZV between January 1, 2018, and May 5, 2020, were used, including 56 days of follow-up; 1,014,329 doses were included. Statistically significant clustering was found within a few days of vaccination for unspecified adverse effects, complications, or reactions to immunization or other medical substances/care; fever; unspecified allergy; syncope/collapse; cellulitis; myalgia; and dizziness/giddiness. These findings are consistent with the known safety profile of this and other injected vaccines. No cluster of Guillain-Barré syndrome was detected, possibly due to insufficient sample size. This signal-detection method has now been applied to 5 vaccines, with consistently plausible results, and seems a promising addition to vaccine-safety evaluation methods.

Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework.

The Sentinel System is a major component of the United States Food and Drug Administration's (FDA) approach to active medical product safety surveillance. While Sentinel has historically relied on large quantities of health insurance claims data, leveraging longitudinal electronic health records (EHRs) that contain more detailed clinical information, as structured and unstructured features, may address some of the current gaps in capabilities. We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center's initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance.

Exact sequential test for clinical trials and post-market drug and vaccine safety surveillance with Poisson and binary data.

In sequential analysis, hypothesis testing is performed repeatedly in a prospective manner as data accrue over time to quickly arrive at an accurate conclusion or decision. In this tutorial paper, detailed explanations are given for both designing and operating sequential testing. We describe the calculation of exact thresholds for stopping or signaling, statistical power, expected time to signal, and expected sample sizes for sequential analysis with Poisson and binary type data. The calculations are run using the package Sequential, constructed in R language. Real data examples are inspired on clinical trials practice, such as the current efforts to develop treatments to face the COVID-19 pandemic, and the comparison of treatments of osteoporosis. In addition, we mimic the monitoring of adverse events following influenza vaccination and Pediarix vaccination.