#250: Real World Data (RWD) Lessons from Healthcare-land with Dr. Lewis Carpenter
Jul 23, 2024
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Lewis Carpenter, Director of Real World Evidence at Arcturus and expert in medical statistics, shares insights on the growing importance of real-world data (RWD) in healthcare analytics. He contrasts RWD with traditional randomized controlled trials (RCTs), emphasizing how complex human behavior generates valuable data. The discussion highlights the challenges of interpreting RCTs and the significance of innovative methodologies in study design. Carpenter also delves into off-label prescriptions, showing how RWD aids in understanding treatment effects and expanding medication labels.
Real World Data (RWD) is becoming increasingly valuable in healthcare analytics, complementing traditional Randomized Controlled Trials (RCTs) by capturing real patient experiences.
The podcast highlights the regulatory challenges and biases associated with RWD, emphasizing the need for a balance between rigorous evaluations and timely access to treatments.
Innovative methodologies, such as propensity score matching, are essential for addressing biases in RWD and ensuring accurate insights into treatment effects.
Deep dives
Measure Camp Experience
Measure Camp is characterized by its unique approach where the event schedule is created on the day of the event, allowing attendees to lead sessions on topics that interest them. This fosters a rich exchange of ideas among participants, enhancing the sense of community. The informal and collaborative nature of Measure Camp is highlighted, where discussions can cover a range of analytics topics in a relaxed atmosphere. Free tickets and convenience of Saturday events further emphasize its accessibility and appeal.
Understanding Real World Data
Real World Data (RWD) refers to information collected from real-world settings, not generated through controlled clinical trials. This data can come from everyday interactions with healthcare providers, electronic health records, and even wearable technologies. Unlike experimental data, RWD represents diverse patient populations and their actual treatment experiences, making it invaluable for understanding drug utilization and effectiveness in the wider population. The distinction between RWD, Real World Evidence (RWE), and Randomized Controlled Trials (RCTs) is crucial, as it highlights the differences in data collection and the implications for healthcare analytics.
Utilization of Real World Evidence
Real World Evidence is increasingly being used to assess treatment effectiveness and inform drug discovery, complementing data from RCTs. The podcast outlines how observational studies can offer insights into patient outcomes, allowing for a larger understanding of drug use in various demographics, including off-label applications. An example discussed includes the challenge of enrolling enough patients for clinical trials in rare diseases, where RWE can provide a more practical solution. The ongoing developments in RWE methodologies aim to enhance analyses and enable more informed decision-making in healthcare.
The Regulatory Landscape
Regulatory bodies often regard RCTs as the gold standard for evaluating drug efficacy, leading to cautious acceptance of RWE due to its inherent biases. This can delay the approval of potentially beneficial treatments, particularly when ethical considerations prevent traditional trials from being conducted. The conversation emphasizes the need for regulators to understand the balance between rigorous safety evaluations and the potential benefits of timely medication access. Innovations like managed access schemes allow for earlier drug access under continuous monitoring, supporting the integration of RWE into standard practices.
Challenges of Bias in Real World Data
Bias in Real World Data presents significant challenges, particularly in understanding treatment effects and health outcomes. Decisions made by clinicians during care can influence the type of data collected, complicating the establishment of causal relationships. The podcast discusses methods like propensity score matching to control for confounding variables that skew results from observational studies. As healthcare analytics evolves, ongoing efforts focus on the design of studies that address these biases and ensure that RWE can effectively complement traditional clinical trial data.
A claim: in the world of business analytics, the default/primary source of data is real world data collected through some form of observation or tracking. Occasionally, when the stakes are sufficiently high and we need stronger evidence, we'll run some form of controlled experiment, like an A/B test. Contrast that with the world of healthcare, where the default source of data for determining a treatment's safety and efficacy is a randomized controlled trial (RCT), and it's only been relatively recently that real world data (RWD) -- data available outside of a rigorously controlled experiment -- has begun to be seen as a useful complement. On this episode, medical statistician Lewis Carpenter, Director of Real World Evidence (there's an acronym for that, too: RWE!) at Arcturis, joined Tim, Julie, and Val for a fascinating compare and contrast and caveating of RWD vs. RCTs in a medical setting and, consequently, what horizons that could broaden for the analyst working in more of a business analytics role. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
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