Matthew Reynolds’ Post

Real-World Data in Clinical Trials: A Game-Changer 🌍💡 Recent insights from a conversation with Craig White, Chief Data Officer at CuriMeta, Inc., shed light on the transformative potential of integrating real-world data (RWD) into clinical trials: Emerging Frontier: Integrating RWD into clinical trials offers a revolutionary approach to trial designs, recruitment, and analysis. CuriMeta's Edge: Founded in collaboration with renowned academic medical centers, CuriMeta's dataset captures intricate nuances of patient care across all specialties, setting it apart from typical RWD datasets. Applications of RWD: Identifying Unmet Needs: Pinpoint areas lacking effective treatments. Streamlining Recruitment: Locate specific sites and patients fitting trial criteria. Enhancing Trial Designs: Design more relevant and efficient trials based on real-world patient profiles. Achieving Representation: RWD reflects the population, ensuring diverse trial representation. The Future: Given the rapid evolution in the use of RWD in the past years, it's anticipated that most clinical trials will integrate RWD in the coming decades, especially in areas like oncology and rare diseases. The integration of RWD into clinical trials is not just a trend but a transformative shift that promises to revolutionize the future of clinical research. 🔗 https://ow.ly/8Yec50PPSo3 #ClinicalTrials #RealWorldData #HealthcareInnovation #CuriMeta

How do we navigate the real challenges of using #RealWorldData (#RWD) in clinical research, such as #MissingData and unmeasured #Confounding? Can Quantitative #Bias Analysis (#QBA) guide our approach to constructing external control arms using RWD? I'm excited to announce a #publication I had the good fortune to collaborate on, led by Kristian Thorlund, "Quantitative Bias Analysis for External Control Arms using Real World Data in Clinical Trials – A Primer for Clinical Researchers.". In this paper, we review the methods and applications of QBA, a tool that helps us understand and correct #errors in #data. QBA stands at the forefront of addressing #bias and #confounding issues in scenarios where traditional data sources are inadequate or unavailable. Our paper specifically delves into the complexities of creating #ExternalControlArms in #ClinicalTrials. This becomes crucial when standard data adjustments aren't possible due to the limitations of #RWD. We've introduced concepts like tipping-point analysis and E-values in an accessible manner, requiring minimal #StatisticalExpertise. By employing these tools, we provide a robust framework for enhancing the credibility of externally-controlled #clinicaltrial results, particularly in cases of potential unknown confounding factors. Our case study, comparing #pralsetinib and #pembrolizumab in treating RET fusion-positive advanced non-small cell #lungcancer, exemplifies the practical application of QBA. It showcases how our methodology can transparently address data limitations, increasing the integrity of RWE. I would like to extend my heartfelt gratitude to my co-authors Stephen J. Duffield, Sanjay Popat, Sreeram Ramagopalan, Alind Gupta, Grace Hsu, and Vivek Subbiah, MD. Their dedication and insight have been invaluable in bringing this paper to fruition. This publication not only reinforces the significance of QBA in constructing #externalcontrol arms but also opens up new possibilities for its application in validating other sources of #realworldevidence (#RWE), such as #PatientSupportProgram data. This ensures greater confidence in the reliability of such sources and, ultimately, contributes to better #PatientOutcomes. I look forward to sharing this work with my colleagues and students at the Dalla Lana School of Public Health, University of Toronto this semester! Read our full article to explore these exciting developments in the world of clinical research: https://lnkd.in/gfPrDUbm. #Biostatistics #Biotech #CADTH #Cancer #ClinicalResearch #ClinicalTrials #DataAnalysis #DataScience #DLSPH #DrugDevelopment #Epidemiology #ExternalControlArms #HealthcareAnalytics #HealthcareScience #HEOR #Immunotherapy #ISPOR #LungCancer #MedicalAffairs #MedicalResearch #MedComms #NSCLC #Oncology #Pharma #Pharmacoepidemiology #PublicHealth #QuantitativeBiasAnalysis #RealWorldData #ResearchInnovation #SER

Very insightful and informative paper!

Bridging the Gap in Clinical Trial Diversity with Real-Time Data It is no hidden secret that the persistent lack of diversity in clinical trials remains a significant barrier to equitable healthcare outcomes. Sharing an insightful article I read this morning on why harnessing real-time data is essential for truly driving diversity in clinical trials: Why We Need to Harness Real-Time Data to Truly Drive Diversity in Clinical Trials At MaxisIT Inc., we recognize the critical need to enhance trial inclusivity and effectiveness through advanced data solutions. Our platform empowers researchers to: Monitor demographic data efficiently, ensuring diverse enrollment aligns with the epidemiology of the disease under study. Adjust recruitment strategies in real-time, thanks to instant data insights that highlight underrepresentation or recruitment challenges. Utilize data-driven narratives to tailor recruitment efforts, making sure that trial populations reflect the diversity of conditions across different racial and ethnic groups. We are committed to empowering clinical researchers with the tools needed to break down barriers and enhance the inclusivity and efficacy of their studies. Let us help you leverage the power of near real-time data to not only meet but exceed modern clinical trial standards and improve health outcomes for all. #clinicaltrials #clinicalresearch #diversityinresearch #realtimedata #dataanalytics #insights #MaxisIT

I was invited to present an hour long presentation to the Health and Public Policy Director for the U.S. House of Representatives last week. It's always an honor when legislative authority is your requesting audience. Having provided insights into the Cures Act 2.0 legislation, there is still a legislative desire to enhance it. Call it the Cures Act 2.1 for now. Interesting dialogue and questions focused around guidance / legislation in some key areas. I thought you might enjoy seeing what those discussions entailed: - Diversity Action Plans (DAP) shifting from guidance to final guidance and looking at this beyond just Phase III trials. - Patient recruitment simulation, with accuracy, provided in DAP (Race, ethnicity, sex, age) with filing for FDA approval. Shifts from "goals" presented to real world numbers. - Simulation of molecule after animal testing, but before first human testing. - QoL being required to be a secondary endpoint at a minimum, co-primary endpoint in some TA's. - QoL being investigated beyond just intervention of the molecule for oncology development. "Only 3.4% of oncology studies assessed QoL until the time of death". - JAMA. - Requiring "objective" data collection pre (baseline), post, and during "intervention" of the molecule. Example: Actigraphy deployed for Oncology, CNS, and/or Cardiology. Same guidance has already been given here for the PRO / "Subjective" data acquisition (June of 2021). - Overlaying "subjective" data (ePRO's, etc.) with the "objective" data (Wearables, EMR, Imaging, etc) to create robust RWE. Early focus could be for labelling claims, AE's, etc. Objective data overlaid with objective data can lead to all things mortality. - Greater facilitation of ARPA-H for novel trial design with a robust focus in digital bio-marker discovery.

In a recent interview with Clinical Leader, our director of diversity, equity and inclusion in clinical trials, Immunology, Denise N. Bronner, Ph.D., shares her perspectives about the importance of leveraging diverse data sources to address health disparities in clinical research and the complexity of AI in addressing #DEI issues. Read more about Bronner’s discussion and the importance of choosing datasets carefully when designing clinical trial protocols: https://bit.ly/47YwlOq

🌟Insights from Craig White on Real-World Data in Clinical Trials! I recently had the honor of discussing the integration of real-world data into clinical trials with Craig White, Chief Data Officer at CuriMeta, Inc.. Our enlightening conversation shed light on CuriMeta’s innovative approach, distinct edge, and vision for the future of clinical trials in the age of real-world health data integration. Craig shared valuable insights on how real-world data is revolutionizing clinical trials by identifying areas of unmet need, locating specific sites and patients, supporting trial designs, and achieving greater representation. He emphasized the transformative shift in using real-world data in clinical trials, envisioning a future where most clinical trials will leverage it to enhance discovery work, patient and physician recruitment, and data collection methods. #CraigWhite #CuriMeta #RealWorldData #ClinicalTrials #Innovation #HealthDataIntegration #ClinicalResearch #FutureOfClinicalTrials

Want to know more about how we work with data at Arcturis Data? This article from our Lead Medical Statistician, Joseph O'Reilly, sheds light on advanced methodologies in real-world evidence (RWE) analysis following our showcase at the ISPOR—The Professional Society for Health Economics and Outcomes Research European Conference last November.    Titled "A Peek Behind the Poster: Characterization of Time to Treatment Discontinuation as a Proxy for Progression-Free Survival in RWE-Based Analyses of Relapsed/Refractory Multiple Myeloma," Joe shows how we at Arcturis examined the utilisation of time to treatment discontinuation as a surrogate for progression-free survival in RWE studies of relapsed/refractory multiple myeloma.   Through the application of robust statistical methodologies and innovative approaches, our goal is to equip healthcare stakeholders with actionable insights derived from real-world evidence, facilitating informed decision-making and enhancing patient care.   📰 Access the full article here: https://lnkd.in/exBmGE6t   #HealthcareAnalytics #RealWorldEvidence #RealWorldData #ArcturisData #DataScience #HealthcareResearch #MultipleMyeloma #MedicalStatistics #Innovation #DataInsights

Everyone is talking about the power of real-world data to allow outcomes and drug safety research and spark reverse translation. But wait, there's a catch! The chorus of complaints about the quality of care-driven data is loud, with some spicy notes about how us clinicians are too "stubborn" to share our data treasures for the "greater good". And recently, we sometimes hear even stranger ideas, like sending data stewards to all the hospitals to do a better job at coding data. Let me say that healthcare personnel is working at capacity. And that we are already struggling finding trained workers to fill all positions in the hospital. Yes, please pour money and personnel over us! It will work, but make sure it is A LOT. In the meantime, if we want high-quality data from the real-world setting, we describe the need for a new deal in our recent article in Nature Cancer: https://lnkd.in/eF9wvv3e: (i) Electronic patient records need to become more than a data dump, but interactive, social, and smart tools that support patient-centered workflows and reduce extraneous tasks; (ii) next generation documentation tools that automatically record well-coded data during the interaction with the patient; (iii) direct care-related incentives for recording good data: automatic guideline and prescription suggestions, suitable text modules for doctors letters and reports, context-sensitive selection of relevant information for consultations etc.; and (iv) fair recognition of data contributions and data quality in data sharing agreements and scientific publications. Why don't you follow the Digital Institute for Cancer Outcomes Research to watch our progress on some of these issues? https://lnkd.in/e9P7MipH #HealthcareData #RealWorldData #DigitalHealthcare #CancerResearch #DataQuality

I came across this poignant piece by Bess Bess Stillman, M.D. on the challenges of finding a clinical trial for her husband. I don't know Bess but their journey is heart breaking and it really puts a human face to a challenge - one that I was only superficially aware of. If a physician has this much difficulty, I can only imagine what the average person goes through. While technological advancements like AI have promise, they are still in their infancy and that is what their experience bears out. Perhaps we need to rethink the whole system. What if all interested patients contributed to a communal database that principal investigators of trials could review. Then, the investigators could bid for patients that were eligible for their trials and pharma companies could pay for every successful recruitment, covering the costs of running the system. Basically flipping the process upside down so that investigators are coming to patients and not the other way around. This is just a random idea and I don't profess to have the answer. I do empathize with the challenge and am posting here so that others much smarter than me can think about this issue and maybe come up with better solutions. https://lnkd.in/girrMid6 Part 2 https://lnkd.in/gdUppNEs Part 3 https://lnkd.in/gbQsTU_t