How to Use Real-World Evidence in Drug Development

Excited to share insights from Sunhee K. Ro et al.'s paper, offering a comprehensive review on the use of RWD/RWE in oncology drug approval submissions. The review highlights key issues and concerns raised by regulatory agencies. Key takeaways: 1.     Value of RWE in Oncology Drug Development: RWE from RWD can improve efficiency and clinical development, especially for rare diseases where large-scale RCTs are impractical. 2.     Challenges and Limitations: Utilizing RWD as external control faces issues like data quality, missing data, and biases. 3.     Case Studies: The review includes successful and unsuccessful case studies of RWD/RWE in oncology drug approvals. 4.     Recommendations: Best practices for using RWD to generate evidence for comparative effectiveness in oncology studies are outlined. This publication serves as a valuable resource for grasping the operational aspects of RWD/RWE study design and analysis. Additionally, it provides recommendations for future endeavors aimed at generating robust RWE for regulatory approvals. #Oncology #DrugApproval #RWD #RWE #ClinicalResearch #Biostatistics #Pharma

📮 #RWE 📃 This review by Rosen et al. focuses on methods for determining causality and quantifying bias in healthcare intervention decision-making, with a particular emphasis on their application to regulatory and HTA decisions. 🔹 A key strength of the review is its integrative approach, which allows for comparison across methods and discusses their strengths, weaknesses, and applicability in various scenarios. 🔹 The authors note that while a variety of methods exist to assess uncertainty, there is a lack of empirical evidence on their combined use. They recommend using multiple methods together to strengthen regulatory decision-making. 🔹 For example, conducting a bias analysis to quantify the impact of potential biases, followed by Bayesian adjustment in a weight of evidence assessment, could provide more robust and reliable findings. Additionally, combining a priori sensitivity analyses with other methods would enhance the robustness of conclusions drawn from RWD/RWE. 🔗 https://lnkd.in/dGJJcMvt #RealWorldEvidence #RWE #RealWorldData #RWD #EvidenceGeneration #RWEDesign #RWEMethods #ObservationalResearch #Epidemiology #Pharmacoepidemiology #Causal_Inference #Causality #EffectEstimation #Causation #Bias #Confounding #ResidualBias #UnmeasuredConfounding #Misclassification #SelectionBias #QuantitativeBiasAnalysis #QBA #HTA

Studies using RWD for nonrandomised comparisons require important methodological considerations to minimize potential sources of bias and confounding, which need to be addressed through appropriate study designs and analytical methods. As an example, the DARWIN EU® catalogue includes the use of active comparator designs, which compare treatment alternatives commonly used for the same indication. This design mitigates confounding by indication and is restricted to new users whenever possible to minimize the potential for other biases. Self-controlled designs including self-controlled case series and self-controlled risk interval are also included for drug safety assessments. In such designs, comparisons are made by looking at different treatment periods within the same person, eliminating all time invariant confounding by design. Analytical strategies to assess potential bias due to measured or unmeasured confounding are also considered. Examples include the use of large-scale propensity scores as an adjustment approach to balance all measured covariates between treatments compared, and the use of negative control outcomes to inform the risk of systematic error and to enable the empirical calibration of estimates and p-values. Over its first three years, DARWIN EU® has played a pivotal role in advancing the EU regulators' vision to enable the use of RWE and establish its value for regulatory decision-making in Europe. Achieving this vision will improve the timeliness, accuracy and relevance of regulatory decisions, with the ultimate goal to better support the development and evaluation of medicines for patients. #DARWINEU #regulatory #realworldevidence #rwe #realworlddata #rwd #dataquality #dataanalytics #patientjourney #patientoutcomes #realworldoutcomes #EMA #clinicaleffectiveness #claims #ehr #clinicaltrials #datascience