How Real-World Data Improves Clinical Research

📮 #RWE Sharing a recently published and compelling commentary by Nicolle M. Gatto and Ulka B. Campbell: "Hope is Not a Strategy: Using Robust Real‑World Evidence to Make Better Clinical Development Decisions.” 📌 Key takeaway: Too often, clinical development strategies rely on assumptions, expert opinion, or outdated precedent, leading to delays, missed opportunities, or even failed programs. The authors argue for a systematic, phased integration of RWE, starting early and aligned with development investment. 💡 Why this matters: -RWE offers critical insights on patient characteristics, care pathways, disease progression, and treatment patterns, long before trial results are available. -A small upfront investment in RWE can dramatically improve design decisions, regulatory outcomes, and patient access. -The piece outlines a blueprint for RWE leaders and senior decision-makers alike, making the case that RWE isn't just a "nice to have" but a must-have for smarter development. 🔍 Whether you're in R&D, regulatory, medical affairs, market access, or data science, this is a must-read on how we can ground development in real-world truth, not hopeful guesswork. 📄 Read the full article here: https://lnkd.in/gFVSqzgK #RealWorldEvidence #ClinicalDevelopment #DrugDevelopment #Epidemiology #Biopharma #RWE #EvidenceBasedMedicine #RealWorldData #IntegratedEvidenceGeneration #IntegratedRWE #RegulatoryDecisionMaking #FDA #EMA #MedTech

A recent study published in Nature Medicine delves into the challenges faced by randomized controlled trials (RCTs) in the field of oncology. The study points out that the stringent eligibility criteria of RCTs often fail to capture the diversity of the oncology patient population. To address this issue, a novel machine learning framework has been introduced to mimic RCTs using real-world data. By categorizing patients into low, medium, and high-risk phenotypes, this framework aims to evaluate how well RCT results can be generalized across different prognostic groups. An analysis of 11 pivotal RCTs focusing on advanced solid malignancies reveals that patients categorized as low and medium-risk tend to experience survival times and treatment benefits similar to those observed in traditional RCTs. However, high-risk patients demonstrate notably lower survival times and treatment benefits. This study emphasizes the significance of acknowledging prognostic diversity among real-world oncology patients. It suggests that leveraging machine learning frameworks can not only enhance individual patient-level decision-making but also refine the design of clinical trials. By integrating real-world data and machine learning, the research highlights the potential to improve the relevance of clinical trial findings, ultimately leading to more informed treatment choices and better patient outcomes in the field of oncology.

What if your data analysis could help shape the future of cancer treatments? It’s not just about crunching numbers—it’s about saving lives. Here is how: Clinical trials generate massive amounts of data, and we can use it! Take T-Cell Engaging Therapy therapy, a life saving cancer treatment. But it can also cause Cytokine Release Syndrome (CRS). It is when your immune system goes into overdrive and releases too many chemical signals (called cytokines) all at once, which can make you feel really sick with fever, fatigue, or even life threatening consequences. During a project with Sanofi, we developed a model to predict the pre-infusion risk of significant CRS (sCRS) for patients treated with TCE therapies. We used aggregated and anonymzed data from the Medidata Enterprise Data Store. Our results? • Patients with the highest risk quartile developed sCRS at >4 times the rate in the lowest risk quartile.  • CRS risk stratification may facilitate patient selection for TCE therapy and tailored pre-treatment and monitoring of CRS, with potential to maximize treatment efficacy, patient safety, and resource allocation. 💡Aggregated data like this helps us understand patterns and improve treatment outcomes. CC: Pénélope Lafeuille , William A. Blumentals, Claire Brulle-Wohlhueter, Weixi Chen , Chao Sang, Sydney Manning, Silvy Saltzman, Jan Canvin, Susan Richards , Cris Kamperschroer , Giovanni Abbadessa, Aniketh Talwai , Caroline Der-Nigoghossian, Yahav Itzkovich, Vibhu Agarwal, Rahul Jain, Tanmay Jain, Jacob Aptekar, Stephen Grupp, Sheila Diamond, MS, CGC #MedidataResearchAlliance  #clinicalresearch ----------------------------------------- 👉 Interested in collaborating or learning more? Reach out to me directly or email researchalliance@medidata.com!

NEW PUBLICATION - Results from our latest Real-World Evidence Pilot demonstrate how treatment response rate can be measured using real-world data. Data obtained from clinical practice, or real-world data (#RWD), can provide valuable insights about treatment outcomes - particularly for patient populations not fully represented in prior clinical studies or for exploring potential other uses for new medicines. However, use of RWD as a research tool can require different methods and study considerations. In our new study published in JCO CCI, we show that different sources of data can be used to implement a common approach and consistently evaluate response rates. This aligned method and reproducibility in results show that rwResponse can be a valuable metric to assess treatment effectiveness outside of traditional clinical trials. Full publication: https://lnkd.in/diTaFg2Z RWE project page: https://lnkd.in/eEaJqjH9 Receive regular updates: https://lnkd.in/dCvFvkeX Many thanks to our collaborators: American Society of Clinical Oncology (ASCO), ConcertAI, COTA, FDA, Flatiron Health, Friends of Cancer Research, Guardian Research Network, IQVIA, Memorial Sloan Kettering Cancer Center, Ontada, Syapse, Syneos Health, and Tempus AI #RWEFriends #cancerresearch

🌍 The landscape of clinical trials is rapidly evolving, with real-world data (RWD) emerging as a game-changer in trial design. As the need for ethical and feasible research solutions grows, our latest paper dives deep into how RWD can complement traditional randomized controlled trials (RCTs). We present a cutting-edge Bayesian divide-and-conquer approach that not only enhances the estimation of treatment effects but also integrates external control data seamlessly—what we call hybrid control trials. By introducing innovative methods for borrowing data and assessing its impact over time, we aim to set a new standard in clinical research. With practical insights drawn from the Alzheimer’s Disease Neuroimaging Initiative, our work highlights the critical role RWD plays in advancing patient-centric research. Discover how you can leverage these findings to enhance your own clinical trials! 🌟 #RealWorldData #ClinicalTrials #InnovationInResearch #PatientCentricCare. #Bayesian https://lnkd.in/eUBnASfb Jian Zhu @lin min @ming-hui chen

How Pfizer used wearables to tap into the promise of an experimental drug: ⌚Pfizer's experimental drug, ponsegromab, is showing promise for treating cachexia, a condition associated with cancer that causes severe weight loss. In the study, patients taking the drug gained around 3 kg more than those given a placebo ⌚Beyond weight gain, wearables were used to track physical activity. Patients taking ponsegromab had about 72 extra minutes of non-sedentary activity per day compared to the placebo group, suggesting improvements in everyday functions like showering and light chores ⌚Wearable devices allowed Pfizer to capture data on physical activity in a passive and “real-world” way, providing more insight than traditional clinic assessments. The study found improvements in overall movement, but individual types of activity, like light or vigorous exercise, didn’t reach statistical significance. However, trends in those areas were positive ⌚Pfizer encountered challenges with wearable data collection, such as incomplete or unusable data due to technical issues, inconsistent patient use, and data capture problems. These experiences provided valuable insights for improving the large-scale deployment of these technologies in future studies ⌚Regulatory bodies, such as the FDA and EMA, are showing interest in digital health technologies as potential future endpoints for clinical trials, though importantly no drug has yet been approved with a qualified digital primary endpoint ⌚Wearable digital endpoints have been widely explored by pharma companies to gain insights into medical conditions and treatment impacts. Pfizer's recent results with ponsegromab are among the most advanced efforts revealed by a major drugmaker ⌚Pfizer's vision is to eventually combine digital measures (like wearables) with biological data, which could provide a fuller understanding of patients' health and treatment outcomes 👇Link to full article in comments (paid) #DigitalHealth #AI #Pharma

High-quality, fit-for-purpose RWD/RWE can, and should be used more frequently, because it is needed for the creation of evidence packages capable of addressing scientific questions, to inform regulatory, payer, physician, and patient decision making, and to reduce burden on patients, investigators, and healthcare systems. A key element to achieve high-quality credible research is, of course, to seek excellence in scientific rigor – but ensuring the research is then transposed into guidance. However, transparency, reproducibility, and critical assessment of study limitations are also key elements of trust in research findings, and an understanding of previous decisions is central to this. Ultimately, the intended use of RWE, its impact, and its complexity should drive the level of pre-alignment between the different agents when agreeing the RWE to be generated. Multi-stakeholder and cross-geography collaborative partnerships are needed to align on best practices to optimize the evidence that needs to be generated to satisfy all stakeholders’ needs – no single stakeholder will have all the necessary insights and knowledge to cover all aspects to address the current (and future) challenges with RWD acceptance and use, so negotiation and collaboration between the different parties will be needed. Early multi-stakeholder engagement creates opportunities to co-create #RWD and #RWE, maximize learning via best practice sharing, while minimizing burden for patients. #realworlddata #healthoutcomes #hta #payer #realworldevidence #rwe #clinicalevidence #patientoutcomes #populationhealth #realworldoutcomes #patientaccess #healthtechnologyassessment #reimbursement #heor #healtheconomics #marketaccess #datascience #epidemiology International Society for Pharmacoepidemiology

If you've been following the rapid advancements in GLP-1's or curious about the potential of real-world data, this #NEWS is fascinating.... New results released this week from the SURMOUNT-5 clinical trial comparing tirzepatide (Zepbound) to semaglutide (Wegovy) are closely aligned to Truveta Research’s GLP-1 comparative effectiveness study, initially shared more than a year ago on November 27, 2023 and published earlier this year in @JAMA Internal Medicine. Truveta Research was able to use real-world data to explore a larger, more diverse patient population more than 10 times the size of the SURMOUNT-5 study (8,823 patients with overweight or obesity and without type 2 diabetes vs. 751). Both studies explored the comparative effectiveness of semaglutide and tirzepatide for weight loss among patients with overweight or obesity. This example shows the power of real-world data to unlock timely insights exponentially faster and with larger, more diverse populations than previously possible by traditional methods. “It’s thrilling to see how closely the trial results match those in Truveta Data, despite some differences in study design,” said Tricia Rodriguez, PhD, MPH, principal applied research scientist, Truveta. “It’s also exciting to know that with real-world data, we were able to provide a glimpse into these clinical trial results over a year sooner. It helps to build confidence in the use of regulatory-grade, real-world data for scientifically rigorous research.” #GLP1 #RWD #healthcare #clincialtrials