Join epidemiologist Deepa Jahagirdar from Cytel as she unpacks the world of external control arms (ECAs). Learn when ECAs are appropriate and how they bridge methods from policy research to clinical trials. Deepa shares insights on selecting real-world data sources and the significance of target trial emulation in enhancing causal credibility. The conversation also covers handling confounding using expert knowledge and directed acyclic graphs, along with practical advice for improving study robustness. It's a must-listen for statisticians navigating real-world data!
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insights INSIGHT
ECAs Translate Policy Methods To Clinical Trials
External control arms (ECAs) let you apply causal methods from policy research to clinical questions using observational data.
ECAs aim to emulate randomized trials when true RCTs are impractical or impossible.
question_answer ANECDOTE
Common Use Cases For External Controls
Alexander explains common ECA scenarios like single-arm oncology trials and long open-label extensions after short placebo periods.
He highlights safety pooling and extension studies as frequent use-cases beyond rare-disease settings.
volunteer_activism ADVICE
Match Data Type To Outcome Characteristics
Choose data by matching the disease and endpoint: claims work for hard, well-coded endpoints like death; registries or academic sources fit rarer or poorly coded outcomes.
Evaluate feasibility against outcome frequency and measurement quality before committing to a data source.
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✔ You want a clearer understanding of when and why ECAs make sense.
✔ You’re dealing with real-world data and need a practical framework for selecting the right source.
✔ You’ve heard the term target trial emulation, but want to understand how it’s applied in real projects.
✔ You want to strengthen the causal credibility of your studies without relying solely on randomized trials.
✔ You want simple, actionable principles for handling confounding and unmeasured bias.
Episode Highlights:
[00:00] – Setting the stage I introduce the topic of external control arms and why they’re more widely relevant than many statisticians think.
[01:35] – Introducing Deepa Deepa shares her path from social epidemiology into designing and supporting ECA studies at Cytel.
[03:00] – Why ECAs are fascinating We talk about how methods used to study policies without RCTs translate into clinical research.
[04:00] – Where ECAs show up I walk through common scenarios—from rare diseases to extension studies—where external controls add value.
[07:30] – Choosing the right real-world data Deepa explains how she approaches data selection depending on disease, outcomes, and feasibility.
[10:20] – Target trial emulation We discuss how designing the “ideal RCT” guides everything that follows when constructing an ECA.
[16:30] – Handling confounding Deepa explains the role of expert knowledge, DAGs, and standard adjustment approaches.
[21:20] – Thinking about unmeasured confounding We talk about assessing robustness and understanding how much bias it would take to overturn your results.
[24:20] – Final takeaways Deepa highlights the importance of focusing on the big causal question and overall robustness—not perfection.
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**Episode Highlights:
**
[00:00] – Setting the stage
I introduce the topic of external control arms and why they’re more widely relevant than many statisticians think.
[01:35] – Introducing Deepa
Deepa shares her path from social epidemiology into designing and supporting ECA studies at Cytel.
[03:00] – Why ECAs are fascinating
We talk about how methods used to study policies without RCTs translate into clinical research.
[04:00] – Where ECAs show up
I walk through common scenarios—from rare diseases to extension studies—where external controls add value.
[07:30] – Choosing the right real-world data
Deepa explains how she approaches data selection depending on disease, outcomes, and feasibility.
[10:20] – Target trial emulation
We discuss how designing the “ideal RCT” guides everything that follows when constructing an ECA.
[16:30] – Handling confounding
Deepa explains the role of expert knowledge, DAGs, and standard adjustment approaches.
[21:20] – Thinking about unmeasured confounding
We talk about assessing robustness and understanding how much bias it would take to overturn your results.
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