Judith C. Maro's research while affiliated with Harvard Medical School and other places

Publications (50)

Article
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 evidence generation to study rare adverse events following medical product exposure increasingly requires analysis of millions of longitudinal patient records...
Article
The recombinant herpes zoster vaccine (RZV), approved as a 2-dose series in the U.S. in October 2017, has proven highly effective and generally safe. However, a small risk of Guillain-Barré syndrome (GBS) after vaccination was identified post-approval, and questions remain about other possible adverse events. This data-mining study assessed RZV saf...
Article
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 p...
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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 informatio...
Article
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 sign...
Article
Purpose: Identifying hospitalizations for serious infections among patients dispensed biologic therapies within healthcare databases is important for post-marketing surveillance of these drugs. We determined the positive predictive value (PPV) of an ICD-10-CM-based diagnostic coding algorithm to identify hospitalization for serious infection among...
Article
The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post‐market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses tha...
Article
Purpose: Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify lymphoma in healthcare claim...
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Background and purpose: The transition from International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) to ICD-10-CM poses a challenge to epidemiologic studies that use diagnostic codes to identify health outcomes and covariates. We evaluated coding trends in health outcomes in the US Food and Drug Administration's Sen...
Article
Tree-based scan statistics (TreeScan) are a data-mining method that adjusts for multiple testing of correlated hypotheses when screening thousands of potential adverse events for signal identification. Simulation has demonstrated the promise of TreeScan with a propensity score (PS) matched cohort design. However, it is unclear which variables to in...
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Surveys of parents indicate safety is their top concern about human papillomavirus (HPV) vaccination. A data-mining method not requiring pre-specification of health outcome(s) of interest or post-exposure period(s) of potentially increased risk can check for associations between an exposure and any of thousands of medically attended health outcomes...
Article
The Sentinel System is a national electronic postmarketing resource established by the US Food and Drug Administration to support assessment of the safety and effectiveness of marketed medical products. It has built a large, multi-institutional, distributed data network that contains comprehensive electronic health data, covering about 700 million...
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We use simulated data to examine the consequences of depletion of susceptibles for hazard ratio (HR) estimators based on a propensity score (PS). First, we show that the depletion of susceptibles attenuates marginal HRs toward the null by amounts that increase with the incidence of the outcome, the variance of susceptibility, and the impact of susc...
Article
Human immunodeficiency virus (HIV) pre-exposure prophylaxis (PrEP) protects high risk patients from becoming infected with HIV. Clinicians need help to identify candidates for PrEP based on information routinely collected in electronic health records (EHRs). The greatest statistical challenge in developing a risk prediction model is that acquisitio...
Article
Background: Oxymorphone's metabolism does not involve the hepatic cytochrome P450 (CYP) system. The effect of this pharmacokinetic feature of oxymorphone on opioid prescribing is unknown. Objective: To assess the relative frequency with which oxymorphone and oxycodone (a CYP3A-metabolized opioid analgesic) were each prescribed to patients concom...
Article
The US Food and Drug Administration (FDA) Sentinel System uses a distributed data network, a common data model, curated real-world data, and distributed analytic tools to generate evidence for FDA decision-making. Sentinel system needs include analytic flexibility, transparency, and reproducibility while protecting patient privacy. Based on over a...
Article
Background: Epidemiological study reporting is improving but is not transparent enough for easy evaluation or replication. One barrier is insufficient details about design elements in published studies. Methods: Using a previously conducted drug safety evaluation in claims as a test case, we investigated the impact of small changes in five key d...
Article
Objective: To estimate real-world off-label use of sodium-glucose cotransporter 2 (SGLT2) inhibitors in patients with type 1 diabetes, estimate rates of diabetic ketoacidosis (DKA), and compare them with DKA rates observed in sotagliflozin clinical trials. Research design and methods: We identified initiators of SGLT2 inhibitors in the Sentinel...
Article
The U.S. Sentinel System and the Canadian Network for Observational Drug Effect Studies (CNODES) are two medical product safety surveillance networks. Using Sentinel’s pre‐programmed, parameterizable analytic tools, we reproduced two protocol‐based studies conducted by CNODES to assess the risks of acute pancreatitis and heart failure (HF) associat...
Article
The self-controlled tree-temporal scan statistic allows detection of potential vaccine- or drug-associated adverse events without prespecifying the specific events or postexposure risk intervals of concern. It thus opens a promising new avenue for safety studies. The method has been successfully used to evaluate the safety of 2 vaccines for adolesc...
Article
Background: HIV pre-exposure prophylaxis (PrEP) is effective but underused, in part because clinicians do not have the tools to identify PrEP candidates. We developed and validated an automated prediction algorithm that uses electronic health record (EHR) data to identify individuals at increased risk for HIV acquisition. Methods: We used machin...
Article
Introduction While medical chart review remains the gold standard to validate health conditions or events identified in administrative claims and electronic health record databases, it is time consuming, expensive and can involve subjective decisions. Aim The aim of this study was to describe the landscape of technology-enhanced approaches that co...
Article
Objective: Study designs involving self-controlled or exposure-matched samples are commonly used to monitor postmarket vaccine and drug safety, and they use a subset of the available larger cohort. This article overviews group sequential methods designed for observational data safety monitoring that use the whole exposed and unexposed cohorts by i...
Article
Purpose The U.S. Food and Drug Administration's Sentinel Initiative “modular programs” have been shown to replicate findings from conventional protocol‐driven, custom‐programmed studies. One such parallel assessment—dabigatran and warfarin and selected outcomes—produced concordant findings for three of four study outcomes. The effect estimates and...
Article
Introduction Neurological complications including seizures have been reported with ranolazine. We sought to quantify the risk of seizure-related hospitalizations or emergency department events following ranolazine exposure in the Sentinel System (2006–2015). Study Design and Setting Eligibility criteria were new use of ranolazine after 183 days wa...
Article
Importance Continuous/extended cyclic estrogen use (84/7 or 365/0 days cycles) in combined oral contraceptives (COCs) could potentially expose women to an increased cumulative dose of estrogen, compared with traditional cyclic regimens (21/7 days cycle), and may increase the risk for venous thromboembolism (VTE). Objective To determine, while hold...
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Full-text available
The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-contr...
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The self-controlled tree-temporal scan statistic-a new signal-detection method-can evaluate whether any of a wide variety of health outcomes are temporally associated with receipt of a specific vaccine, while adjusting for multiple testing. Neither health outcomes nor postvaccination potential periods of increased risk need be prespecified. Using U...
Conference Paper
Background Individuals with rheumatoid arthritis (RA) have an increased risk of venous thromboembolism (VTE), including pulmonary embolism (PE) and deep vein thrombosis (DVT), compared with non-RA populations based on several recent studies1,2. However, information is sparse on the risk of VTE among patients receiving treatment with specific diseas...
Article
Purpose of review: An important component of the Food and Drug Administration's Sentinel Initiative is the active post-market risk identification and analysis (ARIA) system, which utilizes semi-automated, parameterized computer programs to implement propensity-score adjusted and self-controlled risk interval designs to conduct targeted surveillanc...
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Objective To perform sample size calculations when using tree-based scan statistics in longitudinal observational databases. Methods Tree-based scan statistics enable data mining on epidemiologic datasets where thousands of disease outcomes are organized into hierarchical tree structures with automatic adjustment for multiple testing. We show how...
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Introduction: The large-scale assembly of electronic health care data combined with the use of sequential monitoring has made proactive postmarket drug- and vaccine-safety surveillance possible. Although sequential designs have been used extensively in randomized trials, less attention has been given to methods for applying them in observational el...
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Purpose: To develop the infrastructure to conduct timely active surveillance for safety of influenza vaccines and other medical countermeasures in the Sentinel System (formerly the Mini-Sentinel Pilot), a Food and Drug Administration-sponsored national surveillance system that typically relies on data that are mature, settled, and updated quarterl...
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Managing emerging vaccine safety signals during an influenza pandemic is challenging. Federal regulators must balance vaccine risks against benefits while maintaining public confidence in the public health system. We developed a multi-criteria decision analysis model to explore regulatory decision-making in the context of emerging vaccine safety si...
Article
Background: Postmarket surveillance of the comparative safety and efficacy of orphan therapeutics is challenging, particularly when multiple therapeutics are licensed for the same orphan indication. To make best use of product-specific registry data collected to fulfill regulatory requirements, we propose the creation of a distributed electronic h...
Article
PurposeOutcome misclassification in retrospective epidemiologic analyses has been well-studied, but little is known about such misclassification with respect to sequential statistical analysis during surveillance of medical product-associated risks, a planned capability of the US Food and Drug Administration's Sentinel System.Methods Using a vaccin...
Article
Large linked database networks, like the US Food and Drug Administration's Sentinel System, are being built for medical product surveillance. One use of these networks is for "near real-time" sequential database surveillance of prespecified medical product-adverse event pairs, which may result in a "safety signal" when a statistical excess risk is...
Article
We frame the challenges in conducting sequential database surveillance (SDS) analyses and use simulation techniques to illustrate one of these aspects: the accrual of exposed person-time. We discuss the role of SDS analyses in the US Food and Drug Administration's planned Sentinel System and suggest the outline of a decision-analytic framework that...
Article
Postmarket data on prescription medical product performance has historically been incomplete, underutilized, and mismanaged to inform safety and comparative clinical effectiveness. Congress has tasked the Food and Drug Administration to build a public health information infrastructure for drug safety. It also has allotted $1.1 billion dollars in ne...
Article
A distributed health data network is a system that allows secure remote analysis of separate data sets, each comprising a different medical organization's or health plan's records. Distributed health data networks are currently being planned that could cover millions of people, permitting studies of comparative clinical effectiveness, best practice...

Citations

... The historical distinction between "research" and "treatment" intent is not always clear-nor should this imply that the primary intent (treatment) should prevent other (research) usages. Electronic health records are clearly intended to aid in the treatment of patients, but have been harnessed on a grand scale to simultaneously facilitate research (Desai et al., 2021). ...
... Both International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis coding systems were used at various points during the study period. Diagnosis-based code lists for study characteristics were developed using either ICD-9-CM or ICD-10-CM codes and then were mapped to the other system with General Equivalence Mapping [5]; mapped code lists were manually reviewed to ensure conceptual consistency after mapping [6]. ...
... For example, Bayesian Confidence Propagation Neural Networks [37] are used to estimate the Information Component (a well-known Bayesian disproportionality measure), and although traditionally used with SRS data, the approach has been tested with longitudinal (observational) data [35]. Machine learning may also be used to help reduce the effects of potential confounding in signal detection activities through the estimation of propensity scores (described later), which may be used with approaches such as the new-user cohort design or tree-based scan statistic [34,38,39]. In addition, other innovative approaches for using ML with longitudinal data in signal detection activities are being explored. ...
... Additional limitations of the approach we used are that any adverse events with an increased risk sustained throughout the follow-up period would not have been detected, and adverse events with long latency periods could have been missed, due to the follow-up period of 56 days and day 42 being the latest day in any potential risk interval evaluated. We chose to limit the follow-up period to 56 days to minimize the possibility of time-varying confounding, which has been seen with longer follow-up (42), but these parameters can be changed in future applications. ...
... To help disentangle true heterogeneity in patient populations and care from data quality problems, it is important that DDNs conduct regular and robust data quality assessments, ideally according to a systematic and conceptually based framework [78][79][80][81]. For example, to minimize data quality issues and errors that may arise during the CDM creation process, the Sentinel System requires that all extracts from their data partners first pass an extensive data quality review process [73,82]. The OHDSI collaborators have also developed the Data Quality Dashboard [83], representing an open-source tool that performs a series of systematic data quality checks on databases mapped to the OMOP CDM to report potential data quality issues before these databases are used in modeling activities [13]. ...
... Zu (u<t) perish faster; that is why this bias is also called depletion of susceptibles (18,22,33). As a result, an imbalance of Z emerges over time and deviates 1 3 $ ,…,1 3 + from 1 $ ,…, 1 + . ...
... With SL, this task can be done sensibly using a variety of data adaptive techniques. Recent applications of SL include risk score prediction (Pirrachio et al. 2015, Gruber et al. 2020, identifying health outcomes of interest (Carrell et al. 2022), and estimating causal effects of treatments and exposures in observational and randomized studies (Balzer et al. 2019, Kreif et al. 2017, Kempker et al. 2020. ...
... FDA's definition of PV is broad and includes the use of a wide range of scientific inquiry, such as Individual Case Safety Reports (ICSRs), pharmacoepidemiologic studies, registries, clinical pharmacology studies, and other approaches. Although FDA is exploring the use of AI in many of these areas [1,[3][4][5][6][7], research in these areas is not yet mature enough to consider widespread implementation from a regulatory perspective. We focus here on the application of AI to the processing of data from multiple sources to identify adverse events (AEs) meeting regulatory reporting requirements, the preparation of these AEs as ICSRs, and their further reporting and evaluation. ...
... This can help reducing the risk of small variations, which may have a large impact. 44 In strategies C and D, transparency between sites is supported by design because of the shared and distributed data transformation processes. As for scientific independence of sites, control by local partners is complete in strategy A, high in strategies C and D, and low in strategy B. In strategy D, there is by design a dependence on a source of funding independent of studies, to enable the regular extraction of the source data to the general CDM. ...
... Fortunately, substantially discrepant results have been rare. In one collaboration with Sentinel scientists, 11 and one ongoing joint project with Health Canada and the European Medicines Agency, results across databases were consistent. In one CNODES study, 12 one site did appear as an outlier. ...