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The Stanford Population Health Sciences Data Ecosystem was created to facilitate the use of large datasets containing health records from hundreds of millions of individuals. This necessitated technical solutions optimized for an academic medical center to manage and share high-risk data at scale. Through collaboration with internal and external partners, we have built a Data Ecosystem to host, curate, and share data with hundreds of users in a secure and compliant manner. This platform has enabled us to host unique data assets and serve the needs of researchers across Stanford University, and the technology and approach were designed to be replicable and portable to other institutions. We have found, however, that though these technological advances are necessary, they are not sufficient. Challenges around making data Findable, Accessible, Interoperable, and Reusable remain. Our experience has demonstrated that there is a high demand for access to real-world data, and that if the appropriate tools and structures are in place, translational research can be advanced considerably. Together, technological solutions, management structures, and education to support researcher, data science, and community collaborations offer more impactful processes over the long-term for supporting translational research with real-world data.

Ideally, real-world data (RWD) collected to generate real-world evidence (RWE) should lead to impact on the care and health of real-world patients. Deriving from care in which clinicians and patients try various treatments to inform therapeutic decisions, N-of-1 trials bring scientific methods to real-world practice. These single-patient crossover trials generate RWD and RWE by giving individual patients various treatments in a double-blinded way in sequential periods to determine the most effective treatment for a given patient. This approach is most often used for patients with chronic, relatively stable conditions that provide the opportunity to make comparisons over multiple treatment periods, termed Type 1 N-of-1 trials. These are most helpful when there is heterogeneity of treatment effects among patients and no a best option. N-of-1 trials also can be done for patients with rare diseases, potentially testing only one treatment, to generate evidence for personalized treatment decisions, designated as Type 2 N-of-1 trials. With both types, in addition to informing individual's treatments, when uniform protocols are used for multiple patients with the same condition, the data collected in the individual N-of-1 trials can be aggregated to provide RWD/RWE to inform more general use of the treatments. Thereby, N-of-1 trials can provide RWE for the care of individuals and for populations. To fulfill this potential, we believe N-of-1 trials should be built into our current healthcare ecosystem. To this end, we are building the needed infrastructure and engaging the stakeholders who should receive value from this approach.

Our drug development process has produced many life-saving medications, but patients experiencing rare diseases and similar conditions often are left with limited options for treatment. For an approved treatment to be developed, research on a new candidate or existing drug must validate safety and efficacy based on contemporary research expectations. Randomized clinical trials are conducted for this purpose, but they are also costly, laborious, and time-consuming. For this reason, The 21 st Century Cures Act mandates that the US Food and Drug Administration look for alternative methods for approving drugs, in particular exploring the uses of real-world data and evidence. Expanded access (“compassionate use”) is a pathway for the clinical treatment of patients using drugs that are not yet approved for prescribing in the United States. Using real-world evidence generated from expanded-access patients presents an opportunity to provide critical data on patient outcomes that can serve regulatory approval in conjunction with other observational datasets or clinical trials, and in limited circumstances may be the best data available for regulatory review. In doing so, we may also support and encourage patient-centered care and a personalized medicine approach to drug development.

Background The increased adoption of the internet, social media, wearable devices, e-health services, and other technology-driven services in medicine and healthcare has led to the rapid generation of various types of digital data, providing a valuable data source beyond the confines of traditional clinical trials, epidemiological studies, and lab-based experiments. Methods We provide a brief overview on the type and sources of real-world data and the common models and approaches to utilize and analyze real-world data. We discuss the challenges and opportunities of using real-world data for evidence-based decision making This review does not aim to be comprehensive or cover all aspects of the intriguing topic on RWD (from both the research and practical perspectives) but serves as a primer and provides useful sources for readers who interested in this topic. Results and Conclusions Real-world hold great potential for generating real-world evidence for designing and conducting confirmatory trials and answering questions that may not be addressed otherwise. The voluminosity and complexity of real-world data also call for development of more appropriate, sophisticated, and innovative data processing and analysis techniques while maintaining scientific rigor in research findings, and attentions to data ethics to harness the power of real-world data.

The United States Congress passed the 21st Century Cures Act mandating the development of Food and Drug Administration guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data conducted a meeting with various stakeholder groups to build consensus around best practices for the use of real-world data (RWD) to support regulatory science. Our companion paper describes in detail the context and discussion of the meeting, which includes a recommendation to use a causal roadmap for study designs using RWD. This article discusses one step of the roadmap: the specification of a sensitivity analysis for testing robustness to violations of causal model assumptions. We present an example of a sensitivity analysis from a RWD study on the effectiveness of Nifurtimox in treating Chagas disease, and an overview of various methods, emphasizing practical considerations on their use for regulatory purposes. Sensitivity analyses must be accompanied by careful design of other aspects of the causal roadmap. Their prespecification is crucial to avoid wrong conclusions due to researcher degrees of freedom. Sensitivity analysis methods require auxiliary information to produce meaningful conclusions; it is important that they have at least two properties: the validity of the conclusions does not rely on unverifiable assumptions, and the auxiliary information required by the method is learnable from the corpus of current scientific knowledge. Prespecified and assumption-lean sensitivity analyses are a crucial tool that can strengthen the validity and trustworthiness of effectiveness conclusions for regulatory science.

Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to assess and ensure that analyses meet their intended goals. The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence. In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative - a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping. These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.

Real-world data privacy is a complex yet underexplored topic. To date, few studies have reported adult perspectives around real-world data privacy and willingness to share real-world data with researchers. Relevant survey items were identified in the literature, adapted and pilot tested among a small convenience sample, and finalized for distribution. The survey was distributed electronically in April 2021 among adults (≥18 years of age) registered in ResearchMatch (www.researchmatch.org). Microsoft Excel was used to assess descriptive statistics across demographical items and four privacy-related items. Of 402 completed responses received, half of respondents (∼50%) expressed willingness to share their prescription history data and music streaming data with researchers and unwillingness to share real-world data from several other sources. Most (53-93%) of participants expressed concern with five statements reflecting the sharing and use of their digital data online. Most participants (71-75%) agreed with four statements focused on individual measures taken to protect their personal privacy and disagreed (77-85%) with two statements centered on not being concerned about sharing or 3 party access to their personal data online. Our observations indicate an important yet unmet need to further explore and address real-world data privacy concerns among US adults engaging as prospective research participants.

Recruiting underrepresented people and communities in research is essential for generalizable findings. Ensuring representative participants can be particularly challenging for practice-level dissemination and implementation trials. Novel use of real-world data about practices and the communities they serve could promote more equitable and inclusive recruitment. We used a comprehensive primary care clinician and practice database, the Virginia All-Payers Claims Database, and the HealthLandscape Virginia mapping tool with community-level socio-ecological information to prospectively inform practice recruitment for a study to help primary care better screen and counsel for unhealthy alcohol use. Throughout recruitment, we measured how similar study practices were to primary care on average, mapped where practices' patients lived, and iteratively adapted our recruitment strategies. In response to practice and community data, we adapted our recruitment strategy three times; first leveraging relationships with residency graduates, then a health system and professional organization approach, followed by a community-targeted approach, and a concluding approach using all three approaches. We enrolled 76 practices whose patients live in 97.3% (1844 of 1907) of Virginia's census tracts. Our overall patient sample had similar demographics to the state for race (21.7% vs 20.0% Black), ethnicity (9.5% vs 10.2% Hispanic), insurance status (6.4% vs 8.0% uninsured), and education (26.0% vs 32.5% high school graduate or less). Each practice recruitment approach uniquely included different communities and patients. Data about primary care practices and the communities they serve can prospectively inform research recruitment of practices to yield more representative and inclusive patient cohorts for participation.

INTRODUCTION Emerging preclinical evidence suggests that semaglutide, a glucagon‐like peptide receptor agonist (GLP‐1RA) for type 2 diabetes mellitus (T2DM) and obesity, protects against neurodegeneration and neuroinflammation. However, real‐world evidence for its ability to protect against Alzheimer's disease (AD) is lacking. METHODS We conducted emulation target trials based on a nationwide database of electronic health records (EHRs) of 116 million US patients. Seven target trials were emulated among 1,094,761 eligible patients with T2DM who had no prior AD diagnosis by comparing semaglutide with seven other antidiabetic medications. First‐ever diagnosis of AD occurred within a 3‐year follow‐up period and was examined using Cox proportional hazards and Kaplan–Meier survival analyses. RESULTS Semaglutide was associated with significantly reduced risk for first‐time AD diagnosis, most strongly compared with insulin (hazard ratio [HR], 0.33 [95% CI: 0.21 to 0.51]) and most weakly compared with other GLP‐1RAs (HR, 0.59 [95% CI: 0.37 to 0.95]). Similar results were seen across obesity status, gender, and age groups. DISCUSSION These findings support further studies to assess semaglutide's potential in preventing AD. Highlights Semaglutide was associated with 40% to 70% reduced risks of first‐time AD diagnosis in T2DM patients compared to other antidiabetic medications, including other GLP‐1RAs. Semaglutide was associated with significantly lower AD‐related medication prescriptions. Similar reductions were seen across obesity status, gender, and age groups. Our findings provide real‐world evidence supporting the potential clinical benefits of semaglutide in mitigating AD initiation and development in patients with T2DM. These findings support further clinical trials to assess semaglutide's potential in delaying or preventing AD.

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.