Power to the people: why person-generated health data are important for pharmacoepidemiology.

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.

Demonstrating Benefit-Risk Profiles of Novel Therapeutic Strategies in Kidney Transplantation: Opportunities and Challenges of Real-World Evidence.

While great progress has been made in transplantation medicine, long-term graft failure and serious side effects still pose a challenge in kidney transplantation. Effective and safe long-term treatments are needed. Therefore, evidence of the lasting benefit-risk of novel therapies is required. Demonstrating superiority of novel therapies is unlikely via conventional randomized controlled trials, as long-term follow-up in large sample sizes pose statistical and operational challenges. Furthermore, endpoints generally accepted in short-term clinical trials need to be translated to real-world (RW) care settings, enabling robust assessments of novel treatments. Hence, there is an evidence gap that calls for innovative clinical trial designs, with RW evidence (RWE) providing an opportunity to facilitate longitudinal transplant research with timely translation to clinical practice. Nonetheless, the current RWE landscape shows considerable heterogeneity, with few registries capturing detailed data to support the establishment of new endpoints. The main recommendations by leading scientists in the field are increased collaboration between registries for data harmonization and leveraging the development of technology innovations for data sharing under high privacy standards. This will aid the development of clinically meaningful endpoints and data models, enabling future long-term research and ultimately establish optimal long-term outcomes for transplant patients.

COVID-19 Vaccinations in Pregnancy: Comparative Evaluation of Acute Side Effects and Self-Reported Impact on Quality of Life between Pregnant and Nonpregnant Women in the United States.

OBJECTIVE: The objective of this study was to describe the acute side effects experienced by pregnant women who received a coronavirus disease 2019 (COVID-19) vaccine in the United States and to compare their experience to nonpregnant women of similar age. STUDY DESIGN: Adults who received a COVID-19 vaccine in the United States were invited via social media to enroll in an online, longitudinal, community-based registry ( www.helpstopCOVID19.com ). Participants self-reported pregnancy status, vaccination dates, manufacturer, acute side effects, impact on work and self-care, medical consultation, and hospitalization. This analysis was restricted to women aged 20 to 39 at the time of vaccination. Side effects reported by pregnant women were compared to those reported by nonpregnant women. RESULTS: This analysis included 946 pregnant women, with 572 (60%) receiving at least one dose of Pfizer, 321 (34%) Moderna, and 53 (6%) J&J, and 1,178 nonpregnant women. Demographic and medical history were similar across manufacturers for both cohorts.Overall, pregnant women reported similar side effects as nonpregnant women, with the most common being injection site reactions (83 vs. 87%), fatigue (72 vs.78%), and headache (45 vs. 59%). Pregnant women reported fewer side effects (median: 3 vs. 4, respectively). In both cohorts, very few reported seeking medical care (<5%) or being hospitalized (<0.3%) after vaccination. Fewer pregnant women reported working less after vaccination than nonpregnant women (32 vs. 40%) or trouble with self-care (32 vs. 46%), respectively. CONCLUSION: Pregnant women reported similar COVID-19 vaccine side effects as nonpregnant women, although fewer total side effects; pregnant women judged these side effects to have less impact on work and self-care. While these results do not address pregnancy outcomes or long-term effects, findings about acute side effects and impact offer reassurance for all three vaccines in terms of tolerability. KEY POINTS: · COVID vaccines were well tolerated by pregnant women.. · Pregnant women reported fewer total side effects.. · Pregnant women reported less impact on work and self-care..

Real-world evidence to support regulatory decision making: New or expanded medical product indications.

There is increasing interest in utilizing real-world data (RWD) to produce real-world evidence (RWE) on the benefits and risks of medical products that could support regulatory approval decisions. The field of pharmacoepidemiology has a long history of focusing on data and evidence that would now be termed "real-world," including evidence from healthcare claims, registries, and electronic health records. However, several emerging trends over the past decade are converging to support the use of these and other RWD sources for approval decisions, and there are several recent examples and ongoing research that demonstrate how RWE may be used to support regulatory approval of new or expanded indications. The goal of this article is to review the current landscape and future directions of the use of RWE in this context. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).

Trial designs using real-world data: The changing landscape of the regulatory approval process.

PURPOSE: There is a need to develop hybrid trial methodology combining the best parts of traditional randomized controlled trials (RCTs) and observational study designs to produce real-world evidence (RWE) that provides adequate scientific evidence for regulatory decision-making. METHODS: This review explores how hybrid study designs that include features of RCTs and studies with real-world data (RWD) can combine the advantages of both to generate RWE that is fit for regulatory purposes. RESULTS: Some hybrid designs include randomization and use pragmatic outcomes; other designs use single-arm trial data supplemented with external comparators derived from RWD or leverage novel data collection approaches to capture long-term outcomes in a real-world setting. Some of these approaches have already been successfully used in regulatory decisions, raising the possibility that studies using RWD could increasingly be used to augment or replace traditional RCTs for the demonstration of drug effectiveness in certain contexts. These changes come against a background of long reliance on RCTs for regulatory decision-making, which are labor-intensive, costly, and produce data that can have limited applicability in real-world clinical practice. CONCLUSIONS: While RWE from observational studies is well accepted for satisfying postapproval safety monitoring requirements, it has not commonly been used to demonstrate drug effectiveness for regulatory purposes. However, this position is changing as regulatory opinions, guidance frameworks, and RWD methodologies are evolving, with growing recognition of the value of using RWE that is acceptable for regulatory decision-making.

Real-world evidence to support regulatory decision-making for medicines: Considerations for external control arms.

Randomized clinical trials (RCTs) are the gold standard in producing clinical evidence of efficacy and safety of medical interventions. More recently, a new paradigm is emerging-specifically within the context of preauthorization regulatory decision-making-for some novel uses of real-world evidence (RWE) from a variety of real-world data (RWD) sources to answer certain clinical questions. Traditionally reserved for rare diseases and other special circumstances, external controls (eg, historical controls) are recognized as a possible type of control arm for single-arm trials. However, creating and analyzing an external control arm using RWD can be challenging since design and analytics may not fully control for all systematic differences (biases). Nonetheless, certain biases can be attenuated using appropriate design and analytical approaches. The main objective of this paper is to improve the scientific rigor in the generation of external control arms using RWD. Here we (a) discuss the rationale and regulatory circumstances appropriate for external control arms, (b) define different types of external control arms, and (c) describe study design elements and approaches to mitigate certain biases in external control arms. This manuscript received endorsement from the International Society for Pharmacoepidemiology (ISPE).

COVID-19 Vaccination Reactions and Risk of Breakthrough Infections Among People With Diabetes: Cohort Study Derived From Community Reporters.

BACKGROUND: This exploratory study compares self-reported COVID-19 vaccine side effects and breakthrough infections in people who described themselves as having diabetes with those who did not identify as having diabetes. OBJECTIVE: The study uses person-reported data to evaluate differences in the perception of COVID-19 vaccine side effects between adults with diabetes and those who did not report having diabetes. METHODS: This is a retrospective cohort study conducted using data provided online by adults aged 18 years and older residing in the United States. The participants who voluntarily self-enrolled between March 19, 2021, and July 16, 2022, in the IQVIA COVID-19 Active Research Experience project reported clinical and demographic information, COVID-19 vaccination, whether they had experienced any side effects, test-confirmed infections, and consented to linkage with prescription claims. No distinction was made for this study to differentiate prediabetes or type 1 and type 2 diabetes nor to verify reports of positive COVID-19 tests. Person-reported medication use was validated using pharmacy claims and a subset of the linked data was used for a sensitivity analysis of medication effects. Multivariate logistic regression was used to estimate the adjusted odds ratios of vaccine side effects or breakthrough infections by diabetic status, adjusting for age, gender, education, race, ethnicity (Hispanic or Latino), BMI, smoker, receipt of an influenza vaccine, vaccine manufacturer, and all medical conditions. Evaluations of diabetes medication-specific vaccine side effects are illustrated graphically to support the examination of the magnitude of side effect differences for various medications and combinations of medications used to manage diabetes. RESULTS: People with diabetes (n=724) reported experiencing fewer side effects within 2 weeks of vaccination for COVID-19 than those without diabetes (n=6417; mean 2.7, SD 2.0 vs mean 3.1, SD 2.0). The adjusted risk of having a specific side effect or any side effect was lower among those with diabetes, with significant reductions in fatigue and headache but no differences in breakthrough infections over participants' maximum follow-up time. Diabetes medication use did not consistently affect the risk of specific side effects, either using self-reported medication use or using only diabetes medications that were confirmed by pharmacy health insurance claims for people who also reported having diabetes. CONCLUSIONS: People with diabetes reported fewer vaccine side effects than participants not reporting having diabetes, with a similar risk of breakthrough infection. TRIAL REGISTRATION: ClinicalTrials.gov NCT04368065; https://clinicaltrials.gov/study/NCT04368065.

Linking clinical trial participants to their U.S. real-world data through tokenization: A practical guide.

In drug development, the use of real-world data (RWD) has augmented our understanding of patients' health care experiences and the effects of treatments beyond clinical trials. Although electronic health record (EHR) data integration at clinical trial sites is a widely adopted practice, primarily for recruitment and data capture, a challenge to data utility is the fragmentation of health data across different sources. Linking RWD sources to each other and to trial data -- while preserving patient privacy through tokenization -- aids in filling evidence gaps with outcome data and facilitates the generalization of effects from controlled trial environments to real-world settings. This paper describes the applications of RWD linkage and how they benefit both clinical development and real-world decision-making. Trial benefits include improving interpretability and generalizability (e.g., by remediating missing data or losses to follow-up), extending follow-up beyond trial closeout, and characterizing the applicability of trial results to under-represented groups. The operational aspects of linking trial data to RWD are addressed, emphasizing the importance of using privacy-preserving record linking systems with established metrics of accuracy and precision, managing consent, and providing the necessary training and resources at trial sites to inform participants about providing access to their RWD through data linkage.

Human influenza A H5N1 in Indonesia: health care service-associated delays in treatment initiation.

BACKGROUND: Indonesia has had more recorded human cases of influenza A H5N1 than any other country, with one of the world's highest case fatality rates. Understanding barriers to treatment may help ensure life-saving influenza-specific treatment is provided early enough to meaningfully improve clinical outcomes. METHODS: Data for this observational study of humans infected with influenza A H5N1 were obtained primarily from Ministry of Health, Provincial and District Health Office clinical records. Data included time from symptom onset to presentation for medical care, source of medical care provided, influenza virology, time to initiation of influenza-specific treatment with antiviral drugs, and survival. RESULTS: Data on 124 human cases of virologically confirmed avian influenza were collected between September 2005 and December 2010, representing 73% of all reported Indonesia cases. The median time from health service presentation to antiviral drug initiation was 7.0 days. Time to viral testing was highly correlated with starting antiviral treatment (p < 0.0001). We found substantial variability in the time to viral testing (p = 0.04) by type of medical care provider. Antivirals were started promptly after diagnosis (median 0 days). CONCLUSIONS: Delays in the delivery of appropriate care to human cases of avian influenza H5N1 in Indonesia appear related to delays in diagnosis rather than presentation to health care settings. Either cases are not suspected of being H5N1 cases until nearly one week after presenting for medical care, or viral testing and/or antiviral treatment is not available where patients are presenting for care. Health system delays have increased since 2007.

Determinants of antiviral effectiveness in influenza virus A subtype H5N1.

BACKGROUND: Oseltamivir is widely used as treatment for influenza virus A subtype H5N1 (hereafter, "H5N1") infection but, like any intervention, is not always effective. METHODS: We used Avian Influenza Registry data from 10 countries to examine the risk of death in 215 patients with confirmed H5N1 infection who were treated with oseltamivir, according to viral clade, age, respiratory failure, and adjunctive treatment with corticosteroids or antibiotics. RESULTS: The median age of infected individuals was 18 years, and 50% were male. The highest fatality rate occurred in a country with clade 2.1 virus circulation, and the lowest occurred in countries with clade 2.2 virus circulation (P < .001). In univariate analyses, age of ≤5 years and treatment ≤2 days after symptom onset were protective against fatality. When accounting for all risk factors, early initiation of oseltamivir was found to be particularly effective in individuals without respiratory failure (odds ratio, 0.17; P = .04). Patients who had advanced respiratory failure requiring ventilatory support at the time of oseltamivir initiation were more likely to die from the episode of H5N1 infection than patients who did not (P < .001). Adjunctive therapy did not improve the likelihood of surviving the episode. CONCLUSIONS: Oseltamivir is especially effective for treating H5N1 infection when given early and before onset of respiratory failure. The effect of viral clade on fatality and treatment response deserves further investigation.

Tactical Considerations for Designing Real-World Studies: Fit-for-Purpose Designs That Bridge Research and Practice.

Real-world evidence (RWE) is being used to provide information on diverse groups of patients who may be highly impacted by disease but are not typically studied in traditional randomized clinical trials (RCT) and to obtain insights from everyday care settings and real-world adherence to inform clinical practice. RWE is derived from so-called real-world data (RWD), ie, information generated by clinicians in the course of everyday patient care, and is sometimes coupled with systematic input from patients in the form of patient-reported outcomes or from wearable biosensors. Studies using RWD are conducted to evaluate how well medical interventions, services, and diagnostics perform under conditions of real-world use, and may include long-term follow-up. Here, we describe the main types of studies used to generate RWE and offer pointers for clinicians interested in study design and execution. Our tactical guidance addresses (1) opportunistic study designs, (2) considerations about representativeness of study participants, (3) expectations for transparency about data provenance, handling and quality assessments, and (4) considerations for strengthening studies using record linkage and/or randomization in pragmatic clinical trials. We also discuss likely sources of bias and suggest mitigation strategies. We see a future where clinical records - patient-generated data and other RWD - are brought together and harnessed by robust study design with efficient data capture and strong data curation. Traditional RCT will remain the mainstay of drug development, but RWE will play a growing role in clinical, regulatory, and payer decision-making. The most meaningful RWE will come from collaboration with astute clinicians with deep practice experience and questioning minds working closely with patients and researchers experienced in the development of RWE.

Cognitive and other neuropsychiatric symptoms in COVID-19: analysis of person-generated longitudinal health data from a community-based registry.

OBJECTIVE: To describe cognitive symptoms in people not hospitalised at study enrolment for SARS-CoV-2 infection and associated demographics, medical history, other neuropsychiatric symptoms and SARS-CoV-2 vaccination. DESIGN: Longitudinal observational study. SETTING: Direct-to-participant registry with community-based recruitment via email and social media including Google, Facebook and Reddit, targeting adult US residents. Demographics, medical history, COVID-19-like symptoms, tests and vaccinations were collected through enrolment and follow-up surveys. PARTICIPANTS: Participants who reported positive COVID-19 test results between 15 December 2020 and 13 December 2021. Those with cognitive symptoms were compared with those not reporting such symptoms. MAIN OUTCOME MEASURE: Self-reported cognitive symptoms (defined as 'feeling disoriented or having trouble thinking' from listed options or related written-in symptoms) RESULTS: Of 3908 participants with a positive COVID-19 test result, 1014 (25.9%) reported cognitive symptoms at any time point during enrolment or follow-up, with approximately half reporting moderate/severe symptoms. Cognitive symptoms were associated with other neuropsychiatric symptoms, including dysgeusia, anosmia, trouble waking up, insomnia, headache, anxiety and depression. In multivariate analyses, female sex (OR, 95% CI): 1.7 (1.3 to 2.2), age (40-49 years (OR: 1.5 (1.2-1.9) compared with 18-29 years), history of autoimmune disease (OR: 1.5 (1.2-2.1)), lung disease (OR: 1.7 (1.3-2.2)) and depression (OR: 1.4 (1.1-1.7)) were associated with cognitive symptoms. Conversely, black race (OR: 0.6 (0.5-0.9)) and COVID-19 vaccination before infection (OR: 0.6 (0.4-0.7)) were associated with reduced occurrence of cognitive symptoms. CONCLUSIONS: In this study, cognitive symptoms among COVID-19-positive participants were associated with female gender, age, autoimmune disorders, lung disease and depression. Vaccination and black race were associated with lower occurrence of cognitive symptoms. A constellation of neuropsychiatric and psychological symptoms occurred with cognitive symptoms. Our findings suggest COVID-19's full health and economic burden may be underestimated. TRIAL REGISTRATION NUMBER: NCT04368065.

H5N1 avian influenza in children.

BACKGROUND: Avian influenza continues to pose a threat to humans and maintains the potential for greater transmissibility. Understanding the clinical presentation and prognosis in children will help guide effective diagnosis and treatment. METHODS: A global patient registry was created to enable systematic collection of clinical, exposure, treatment, and outcomes data on confirmed cases of H5N1. Bivariate and multivariate statistical tools were used to describe clinical presentation and evaluate factors prognostic of survival. RESULTS: Data were available from 13 countries on 193 children <18 years who were confirmed as having been infected with H5N1; 35.2% of cases were from Egypt. The case fatality rate (CFR) for children was 48.7%, with Egypt having a very low pediatric CFR. Overall, children aged ≤5 years had the lowest CFR and were brought to hospitals more quickly and treated sooner than older children. Children who presented for medical care with a complaint of rhinorrhea had a 76% reduction in the likelihood of death compared with those who presented without rhinorrhea, even after statistical adjustment for age, having been infected in Egypt, and oseltamivir treatment (P = .02). Delayed initiation of treatment with oseltamivir increases the likelihood of death, with an overall 75% increase in the adjusted odds ratio for death for each day of delay. CONCLUSIONS: The presence of rhinorrhea appears to indicate a better prognosis for children with H5N1, with most patients surviving regardless of age, country, or treatment. For individuals treated with oseltamivir, early initiation of treatment substantially enhances the chance of survival.

COVID-19 Vaccination Breakthrough Infections in a Real-World Setting: Using Community Reporters to Evaluate Vaccine Effectiveness.

Purpose: Coronavirus disease 2019 (COVID-19) has highlighted the need for new methods of pharmacovigilance. Here, we use adult community volunteers to obtain systematic information on vaccine effectiveness and the nature and severity of breakthrough infections. Methods: Between December 15, 2020 and September 16, 2021, 11,826 unpaid community-based volunteers reported the following information to an on-line registry: COVID-19 test results, vaccination (Pfizer, Moderna, or Johnson & Johnson) and COVID-19 symptoms. COVID-19 infections were described based on vaccination status at the time of infection: 1) fully vaccinated, 2) partially vaccinated (received first of two-dose vaccines or were <14 days post-final dose), or 3) unvaccinated. Results: Among 8554 participants who received any COVID-19 vaccine, COVID-19 infections were reported by 74 (1.0%) of those who were fully vaccinated and 198 (2.3%) of those who were partially vaccinated at the time of infection. Among the 74 participants who reported a breakthrough infection after full vaccination, the median time from vaccination to reported positive test result was 104.5 days (interquartile range: 77-135 days), with no difference among vaccine manufacturers. One quarter (25.7%) of breakthrough infections in the fully vaccinated cases were asymptomatic and most (>97%) fully vaccinated participants reported no symptoms or only mild symptoms compared to 89.3% of the unvaccinated cases. Only 1.4% of fully vaccinated participants reported experiencing at least 3 moderate-to-severe symptoms compared to 7.8% in the unvaccinated. Conclusion: Person-generated health data, also referred to as patient-reported outcomes, is a useful approach for quantifying breakthrough infections and their severity and for comparing vaccines. Trial Registration: Clinicaltrials.gov NCT04368065, EU PAS Register EUPAS36240.

Evaluating real-world COVID-19 vaccine effectiveness using a test-negative case-control design.

Aim: It is important to assess if clinical trial efficacy translates into real-world effectiveness for COVID-19 vaccines. Materials & methods: We conducted a modified test-negative design (TND) to evaluate the real-world effectiveness of three COVID-19 vaccines. We defined cases in two ways: self-reported COVID-19-positive tests, and self-reported positive tests with ≥1 moderate/severe COVID-19 symptom. Results: Any vaccination was associated with a 95% reduction in subsequently reporting a positive COVID-19 test, and a 71% reduction in reporting a positive test and ≥1 moderate/severe symptom. Conclusion: We observed high effectiveness across all three marketed vaccines, both for self-reported positive COVID-19 tests and moderate/severe COVID-19 symptoms. This innovative TND approach can be implemented in future COVID-19 vaccine and treatment real-world effectiveness studies. Clinicaltrials.gov identifier: NCT04368065.

When Can We Trust Real-World Data To Evaluate New Medical Treatments?

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. RWE studies often rely on real-world data (RWD), including data extracted from healthcare records or data captured by mobile phones or other consumer devices. Global assessments of RWD sources are not helpful in assessing whether any specific RWD element is fit for any specific purpose. Instead, evidence generators and evidence consumers should clearly identify the specific health state or clinical phenomenon of interest and then consider each step between that clinical phenomenon and its representation in a research database. We propose specific questions regarding potential error or bias affecting each of those steps: Would a person experiencing this clinical phenomenon present for care in this setting or interact with this recording device? Would this clinical phenomenon be accurately recognized or assessed? How might the recording environment or tools affect accurate and consistent recording of this clinical phenomenon? Can data elements from different sources be harmonized, both technically (same format) and semantically (same meaning)? Can the original data elements be consistently reduced to a useful clinical phenotype? Addressing these questions requires a range of clinical, organizational, and technical expertise. Transparency regarding each step in the creation of RWD is essential if evidence consumers are to rely on RWE studies.

When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?

The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real-world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design-related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high-quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions.

Uncontrolled Extensions of Clinical Trials and the Use of External Controls-Scoping Opportunities and Methods.

Increased interest in real-world evidence (RWE) for clinical and regulatory decision making and the need to evaluate long-term benefits and risks of pharmaceutical products raise the importance of understanding the use of external controls (ECs) for uncontrolled extensions of randomized controlled trials (RCTs). We searched clinicaltrials.gov from 2009 to 2019 for uncontrolled extensions and assessed the use of ECs in the trial protocol registry and PubMed. We present characteristics of identified uncontrolled extensions, their adoption of ECs, and a qualitative appraisal of published uncontrolled extensions with ECs according to good pharmacoepidemiologic practice. The number of uncontrolled extensions increased slightly across the study period, resulting in a total of 1,115 studies. Most originated from phase III RCTs (62.2%) and specified safety outcomes (61.9% among those with specified outcomes). Most uncontrolled extensions incorporated no control group with only 7 out of 1,115 (0.6%) employing ECs. For those studies with ECs, all involved treatments for rare conditions and assessment of effectiveness. Attempts to balance comparison groups varied from none mentioned to propensity score matching. We noted consistent deficiencies in outcome ascertainment methods and approaches to address attrition bias. The contrast of the large and growing number of uncontrolled extensions with the small number of studies that utilized ECs showed clear opportunities for enhancement in design, measurement, and analysis of uncontrolled extensions to allow causal inferences on long-term treatment effects. As extensions continue to expand within RWE regulatory frameworks, development of guidelines for use of EC with uncontrolled extensions is needed.