Nancy A. Dreyer

In June 2026, the FDA issued a revised draft guidance describing the circumstances in which one adequate and well‐controlled clinical investigation plus confirmatory evidence may satisfy the statutory substantial evidence standard for establishing efficacy and safety. Confirmatory evidence includes real‐world evidence (RWE). The evolution of the regulatory use of RWE builds on more than two decades of FDA experience with real‐world data (RWD), first for pharmacovigilance and later in effectiveness and risk/benefit determination. RWD/RWE guidances have been released since the 21st Century Cures Act was passed in 2016, but implementation remains operationally and methodologically challenging. This article provides practical guidance for teams considering RWE as part of a registrational program. Key topics include selection of fit‐for‐purpose data, study design, protocol development, study execution and governance, data transformation, analysis and assessment of residual bias, and submission. Recommendations emphasize early FDA alignment, filling gaps in procedural documentation and systems, multidisciplinary collaboration, and anticipating technological demands associated with large real‐world datasets.

Background and objective: High dimensional, hypothesis free signal detection in claims data can identify adverse drug events (ADEs), yet these exploratory signals remain vulnerable to false positives and residual confounding. Rigorous internal confirmation using causal inference methods is therefore a critical intermediate step in a staged pharmacovigilance paradigm before external validation. We aimed to confirm or refute previously detected ADE signals associated with atorvastatin initiation among older adults after myocardial or cerebral infarction. Methods: Internal confirmatory sequential target trial emulations were conducted using Medicare fee-for-service claims (2017–2019). Eligible participants were statin naïve beneficiaries aged ≥ 65 years hospitalized for myocardial or cerebral infarction. Up to 14 nested daily trials were constructed beginning on the discharge date. Pragmatic treatment strategies compared atorvastatin initiation with initiation of a different new outpatient medication. Per protocol effects were estimated using inverse probability weighted Fine-Gray models treating death as a competing risk. Previously detected ADE signals and clinically coherent alternatives within the same outcome families were evaluated according to prespecified dual criteria controlling for the false discovery rate and residual bias. Absolute risks and numbers needed to harm (NNH) were reported. A prespecified sensitivity analysis restricted inference to the first two trials with optimal covariate balance; a post hoc analysis examined effect modification by concomitant high risk antithrombotic therapy. Results: Of 70,130 eligible patients, 39,948 initiated atorvastatin (81% high intensity) and 19,182 initiated a different new medication. After weighting, baseline characteristics were closely balanced. Associations meeting dual confirmatory criteria formed a gradient of support. The strongest findings were early (days 1 to 30) acute hemorrhagic cerebrovascular disease (sHR 2.20, 95% CI 1.35-3.58; NNH 351) and acute hepatic failure (sHR 1.72, 1.16-2.55; NNH 468), both robust to high risk antithrombotic therapy and to the restricted trial set with optimal covariate balance. Intermediate support was observed for biliary tract disease (women; sHR 1.45, 1.14-1.85; NNH 102) and musculoskeletal injuries (men; sHR 1.66, 1.24-2.21; NNH 71). Tentative evidence was observed for cardiac valve disorders (attenuated in the absence of high risk antithrombotic therapy) and sensory symptoms (non-White patients). Prediabetes and posthemorrhagic anemia were not confirmed. Conclusions: Several previously detected ADE associations with atorvastatin initiation met dual confirmatory criteria and formed a gradient of confirmatory support. Strongest evidence supported early hemorrhagic cerebrovascular disease, hepatic dysfunction, and musculoskeletal injuries. Absolute excess risks were modest yet clinically relevant to a high risk post-infarction population (NNH 71-468) and must be interpreted alongside the established benefits of high intensity statin therapy (number needed to treat to prevent one event (NNT) 28-93). These findings support a two stage active pharmacovigilance paradigm (signal detection followed by rigorous confirmation) and justify heightened clinical vigilance for the confirmed events, while underscoring the need for external validation in independent populations and data sources. KEY POINTS: Question: Can previously detected signals of adverse drug events associated with atorvastatin initiation in older adults be confirmed under more rigorous dual confirmatory criteria? Findings: In this internal confirmatory Medicare claims study using sequential target trial emulation, associations meeting dual confirmatory criteria formed a gradient of support. The strongest findings were early acute hemorrhagic cerebrovascular disease (sHR 2.20) and acute hepatic failure (sHR 1.72), both robust to high risk antithrombotic therapy and to the optimally balanced early trials. Intermediate support was observed for biliary tract disease and musculoskeletal injuries. Weaker evidence was observed for cardiac valve disorders (attenuated in the absence of high risk antithrombotic therapy) and sensory symptoms. Prediabetes and posthemorrhagic anemia were not confirmed. Meaning: Although atorvastatin remains foundational for secondary cardiovascular prevention (NNT 28–93 to prevent one event), these findings support heightened clinical vigilance for early hemorrhagic, hepatic, and musculoskeletal events in older adults after myocardial or cerebral infarction, while underscoring the need for external validation. PLAIN LANGUAGE SUMMARY: Older adults who start atorvastatin after a heart attack or stroke may face a higher risk of certain serious side effects, according to this large study of Medicare insurance records. Researchers previously identified possible safety signals for atorvastatin using advanced methods that mimic clinical trials with real world data. This follow up study carefully reexamined those signals with stricter eligibility rules, more precise outcome definitions, and statistical techniques designed to reduce false findings. The analysis confirmed a range of associations that varied in strength. The strongest evidence pointed to higher rates of bleeding in the brain (especially in the first 30 days) and acute liver failure. Intermediate evidence supported bile duct problems and muscle strains or joint injuries. Weaker evidence was found for certain heart valve problems and for dizziness or sensory symptoms. Some earlier possible signals, such as prediabetes, were not confirmed. Although atorvastatin remains an important treatment to prevent future heart attacks and strokes, these findings suggest that older adults starting the drug may benefit from close monitoring for early bleeding in the brain, liver problems, and muscle or joint injuries, and that suspicion for these...

Importance: Adverse drug events (ADEs) in older adults are a substantial public health burden, yet spontaneous reporting systems detect them poorly owing to underreporting and the lack of a defined population. These limitations are of particular concern for older adults, who are underrepresented in pre-approval trials yet at elevated ADE risk owing to polypharmacy, multimorbidity, and age-related changes in drug metabolism. Objective: To develop and apply an active, claims-based pharmacovigilance framework using sequential target trial emulation to detect ADE signals in older adults, with atorvastatin as the initial application. Methods: Using Medicare fee-for-service claims (2017-2019), we studied statin-naïve beneficiaries aged 65 years or older following hospitalization for myocardial or cerebral infarction. We emulated up to 14 daily sequential trials from the discharge date, classifying patients as initiating atorvastatin (A1), initiating a different medication (A2), or no new medication (A0); the primary contrast was A1 versus A2. For each trial, incident ADEs were ascertained and classified into 552 outcomes based on the Clinical Classifications Software Refined categories. Per-protocol effect estimates were measured over a 6-month follow-up period using Fine-Gray regression weighted by the inverse probability of treatment (IPT) and censoring, treating death as a competing risk, with the false discovery rate controlled via the Benjamini-Hochberg procedure. A signal was declared when the q-value was ≤ 0.10 and the subdistribution hazard ratio (sHR) was ≥ 1.20 in any prespecified analytic stratum (sensitivity analyses used thresholds of q ≤ 0.20 and sHR ≥ 1.20). Results: Of 70,130 eligible patients, 39,948 initiated atorvastatin (A1), 19,182 initiated another new medication (A2); after IPT-weighting, baseline characteristics were closely balanced. After excluding outcomes with sparse cell counts, 295 outcomes were analyzed; five met the primary signal detection criteria: valve disorders (sHR 1.71, 1.20-2.43); sprains and strains (sHR 1.79, 1.26-2.54); general sensation/perception symptoms (sHR 1.23, 95% CI 1.11-1.36); abnormal findings without diagnosis (sHR 1.55, 1.18-2.05); and prediabetes (sHR 1.71, 1.24-2.36). In the sensitivity analysis, we additionally detected: posthemorrhagic anemia, hemorrhagic stroke, varicose veins, other circulatory and skin conditions. Conclusions: A claims-based framework using sequential target trial emulations detected both expected and previously unrecognized ADE signals following atorvastatin initiation in older adults, offering a systematic alternative to passive surveillance that can be extended to other commonly prescribed medications. Confirmatory analyses with disaggregated outcomes and narrower comparators are underway and will be reported separately. KEY POINTS: Question: Can an active, claims-based pharmacovigilance framework using sequential target trial emulation detect adverse drug event signals among older Medicare beneficiaries who initiated atorvastatin? Findings: In this cohort study of 59,130 Medicare beneficiaries discharged after myocardial or cerebral infarction, a hypothesis-free scan across hundreds of prespecified outcomes detected five primary signals (valve disorders, sprains and strains, sensory symptoms including dizziness, abnormal laboratory findings, and prediabetes) and additional signals in sensitivity analyses (including hemorrhagic stroke), several of which align with known or labeled statin adverse effects (serving as positive controls that validate the framework) while others remain uncertain. Meaning: This framework offers a systematic, quantitative, and proactive alternative to spontaneous reporting for detecting medication safety signals in high-risk older adults. PLAIN LANGUAGE SUMMARY: Older adults use more prescription drugs than any other age group, yet they are often left out of clinical trials conducted before a drug is approved. As a result, the safety of many medications in older patients is poorly understood until the drugs are already in wide use. The main system for detecting new drug safety problems in the United States, the FDA Adverse Event Reporting System, relies on voluntary reports and captures only a small fraction of harms, which limits its usefulness. Using Medicare reimbursement records, the researchers developed an active monitoring method and tested it on a commonly used cholesterol-lowering medication (atorvastatin) – that is frequently prescribed after a heart attack or stroke. They followed thousands of patients for six months, statistically balancing those who started atorvastatin with those who started a different new drug, and screened for hundreds of possible side effects. Evidence of potential medication harm emerged along a clear range of how likely the drug caused each problem. This ranged from well-known side effects (such as diabetes and sprains and strains) and outcomes already noted in the product labeling or prior research (such as hemorrhagic stroke and dizziness) to more uncertain associations (such as cardiac valve disorders) that are difficult to interpret. Overall, atorvastatin appeared largely safe in this older population, and the same method can now be used to study other widely used drugs.

First-in-class (FIC) oncology drugs—defined by their novel mechanisms of action or targeting of previously unaddressed molecular targets—have emerged as a leading force in cancer therapeutic innovation. In this Review, we delineate the global landscape of FIC oncology drug approvals between 2009 and 2024 and analyze the evolving characteristics of both approved FIC therapies and potential FIC (PFIC) candidates in clinical development. To clarify the underlying dynamics of this evolution, we construct an analytic framework encompassing four distinct archetypes of FIC development: new targets, new mutation subtypes, new modalities, and new multi-target strategies. This typology enables a systematic dissection of divergent translational trajectories and bottlenecks across the evolving FIC landscape. While most approved FIC drugs are driven by target novelty, PFIC candidates increasingly capitalize on technological breakthroughs, particularly in modalities such as cell and gene therapies, antibody–drug conjugates, proteolysis-targeting chimeras, cancer vaccines and bispecific antibodies. These advances not only expand the boundaries of druggability but also redefine how therapeutic interventions are conceived, delivered and translated into patient outcomes. Despite this momentum, critical bottlenecks—including the identification and validation of tractable targets, high clinical attrition rates, and persistent disparities in global access—continue to limit the real-world impact of oncology innovation. Looking forward, the convergence of artificial intelligence, next-generation modalities, and translational collaboration may serve as powerful engines for translating scientific breakthroughs into widely accessible therapies and for driving sustainable, mechanism-based innovations in oncology.

Clinical trials with pragmatic elements can capture diverse patient populations, enable efficient follow‐up, and reflect routine clinical practices; yet there is limited understanding of their design features, characteristics, and use of real‐world data (RWD). Based on a targeted search of PubMed/EMBASE (Jan‐2016 to Jul‐2024), a review was conducted to characterize clinical trial use cases with pragmatic elements during the design and conduct of trials, as well as clinical trials that utilized RWD for long‐term follow‐up in the extension phase of the original (“parent”) trial. A total of 27 use cases were characterized. There were 22 use cases with pragmatic elements, and nearly all employed randomization ( n = 21, 95.5%), open‐label design ( n = 20, 90.9%), with usual/standard of care ( n = 13, 59.1%) as comparators. Most generated evidence on both effectiveness and safety ( n = 18, 81.8%), primarily in diabetes and cardiovascular disease areas ( n = 14, 63.6%). The most common pragmatic elements were broad eligibility criteria, flexible treatment management, minimal or no follow‐up, and streamlined endpoint collection. RWD (including electronic health records, claims, registries) was utilized in half of the use cases ( n = 4 to enrich trial data with additional information abstracted from RWD; n = 7 to embed trials within routine healthcare databases/systems). In addition, 5 use cases were identified that utilized RWD in the extension phase of the original “parent” trials (4 utilizing RWD for long‐term follow‐up purposes, and one utilizing RWD to construct external controls for clinical trial infrastructure‐based extension study). This review underscores the emerging role of pragmatic elements in enhancing trial efficiency, generalizability, and evidence generation on long‐term outcomes.

Complementing randomized controlled trials, real-world evidence (RWE) from observational analyses can extend clinical insights in oncology. While healthcare stakeholders have published rigorous RWE frameworks and resources, a multidisciplinary think tank was established to further advance acceptance and use of RWE in treatment decision-making, with the focus on breast cancer (while recognizing relevance in oncology more broadly). Members discussed perceptions of RWE from a clinical perspective, across domains of data, methodology, and mindset, and “calls-to-action” for stakeholders. Agreement was reached on a primary “call-to-action,” to develop clinically-relevant, patient-informed, real-world endpoints, and secondary “calls-to-action”: establish a multidisciplinary consensus forum; publish examples of unique RWE value; build upon existing frameworks and resources; and tailor an approach for exhibiting utility to guideline bodies.

A compilation of factors over the past decade—including the availability of increasingly large and rich healthcare datasets, advanced technologies to extract unstructured information from health records and digital sources, advancement of principled study design and analytic methods to emulate clinical trials, and frameworks to support transparent study conduct—has ushered in a new era of real‐world evidence (RWE). This review article describes the evolution of the RWE era, including pharmacoepidemiologic methods designed to support causal inferences regarding treatment effects, the role of regulators and other health authorities in establishing distributed real‐world data networks enabling analytics at scale, and the many global guidance documents on principled methods of producing RWE. This article also highlights the growing opportunity for RWE to support decision making by regulators, health technology assessment groups, clinicians, patients, and other stakeholders and provides examples of influential RWE studies. RWE holds promise to address important questions that clinical trials typically do not answer about treatment benefits and risks, and to ultimately impact public health by helping to guide decision making across the healthcare ecosystem.

The assumption of “no unmeasured confounders” is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of real‐world evidence remains under‐utilized. The lack of use is likely in part due to complexity of implementation and often specific and restrictive data requirements for application of each method. With the advent of methods that are broadly applicable in that they do not require identification of a specific unmeasured confounder—along with publicly available code for implementation—roadblocks toward broader use of sensitivity analyses are decreasing. To spur greater application, here we offer a good practice guidance to address the potential for unmeasured confounding at both the design and analysis stages, including framing questions and an analytic toolbox for researchers. The questions at the design stage guide the researcher through steps evaluating the potential robustness of the design while encouraging gathering of additional data to reduce uncertainty due to potential confounding. At the analysis stage, the questions guide quantifying the robustness of the observed result and providing researchers with a clearer indication of the strength of their conclusions. We demonstrate the application of this guidance using simulated data based on an observational fibromyalgia study, applying multiple methods from our analytic toolbox for illustration purposes.

Person-generated health data (PGHD) are valuable for studying outcomes relevant to everyday living, for obtaining information not otherwise available, for long-term follow-up, and in situations where decisions cannot wait for traditional clinical research to be completed. While there is no dispute that these data are subject to bias, insights gained may be better than having an information void, provided the biases are understood and addressed. People will share information known uniquely to them about exposures that may affect drug tolerance, safety, and effectiveness (eg, nonprescription and complementary medications, alcohol, tobacco, illicit drugs, exercise, etc). Patients may be the best source of safety information when long-term follow-up is needed (eg, the 5- to 15-year follow-up required for some gene therapies). Validation studies must be performed to evaluate what people can accurately report and when supplementary confirmation information is needed. However, PGHD has already proven valuable in quantifying and contrasting COVID-19 vaccine benefits and risks and for evaluating disease transmission and the accuracy of COVID-19 testing. Going forward, PGHD will be used for patient-measured and patient-relevant outcomes, including for regulatory purposes, and will be linked to broader health data networks using tokenization, becoming a mainstay for signals about risks and benefits for diverse populations. This article is part of a Special Collection on Pharmacoepidemiology.