Assessing unmeasured confounding via bias analysis

A conversation with Deepa Jahagirdar

Why Listen

✔ 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.

Links:

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🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

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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.

[24:20] – Final takeaways