Episodi

  • Testing Ebola Vaccines in an Epidemic

    Testing Ebola Vaccines in an Epidemic

    · Research Notes

    What does it take to run a vaccine trial in the middle of an Ebola outbreak?

    In this episode of Research Notes, I talk with Dr. Ana Maria Henao-Restrepo, former WHO scientist and one of the leaders of the groundbreaking Ebola ring vaccination trial conducted during the 2014–2015 West Africa outbreak. The trial ultimately demonstrated that the rVSV-ZEBOV vaccine was highly effective and helped establish a new model for evaluating vaccines during public health emergencies.

    Our conversation goes far beyond the published results. Dr. Henao-Restrepo takes us behind the scenes of designing a clinical trial while an epidemic was actively unfolding. We discuss why traditional randomized trial designs were not a good fit for Ebola transmission, how the team adapted concepts from smallpox eradication to create a ring vaccination strategy, and how they balanced scientific rigor with ethical and logistical realities on the ground.

    Along the way, we explore questions that are relevant far beyond Ebola: How do researchers identify the right unit of randomization? What happens when policymakers reject a placebo-controlled design? How do you build research infrastructure in places with little prior clinical trial experience? And how do interim analyses influence decisions when lives are at stake?

    Dr. Henao-Restrepo also reflects on her career at the World Health Organization, lessons learned from polio eradication and outbreak response, and how the success of this trial changed global expectations for integrating research into emergency responses.

    If you're interested in clinical trials, causal inference, vaccine development, outbreak response, or the practical realities of conducting research under extreme conditions, this is a fascinating case study in how science works when time is running out.

    Research note: https://ghrbook.com/notes/ebola-trial-methods-vaccines.html

  • Bayesian adaptive trial design in a Kenyan eye care program

    Bayesian adaptive trial design in a Kenyan eye care program

    · Research Notes

    What happens when researchers stop a clinical trial early—not because something went wrong, but because the evidence became convincing faster than expected?

    In this episode of Research Notes, I talk with statistician David Macleod about a fascinating Bayesian adaptive trial embedded directly inside a mobile eye-screening program in Kenya.

    The project began with a practical global health problem: people were being screened for eye disease using a smartphone app called Peek Acuity, but many patients who were referred for additional care never showed up at the clinic. Researchers worked with communities to understand why and developed a simple intervention: brief motivational counseling and reminder messages delivered at the time of screening.

    Rather than running a traditional fixed-sample randomized controlled trial, the team embedded a Bayesian adaptive trial directly into the app itself. Every week, the researchers updated their estimates using incoming data and evaluated whether the intervention appeared effective enough to stop early.

    We discuss:

    How Bayesian adaptive trials differ from traditional randomized trials What “priors” are and why they matter Why the study stopped after only four weeks The tradeoff between speed and certainty in real-world evidence generation The risk of false positives in adaptive designs How low-cost interventions may justify different statistical thresholds Why rigorous methods still matter in implementation science and global health

    We also talk about David’s unconventional career path from electrical engineering and telecommunications into epidemiology and biostatistics at the London School of Hygiene and Tropical Medicine.

    Read the companion research note on Global Health Research in Practice: https://ghrbook.com/notes/adaptive-trial.html

    Research Notes is a podcast and video series exploring how health research actually works: the methods, reasoning, tradeoffs, and decisions behind published studies.

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  • Creating a synthetic South Africa to study tobacco taxes

    Creating a synthetic South Africa to study tobacco taxes

    · Research Notes

    In this episode of Research Notes, I talk with economist Dr. Grieve Chelwa about his paper using the synthetic control method to estimate the impact of cigarette excise taxes on smoking in South Africa. We start with a simple question: did higher taxes actually reduce cigarette consumption, or were other forces—economic change, cultural shifts, or declining trends already underway—doing the work? Chelwa explains why answering that question rigorously matters for both public health policy and causal inference. We then walk through the core methodological challenge: cigarette consumption in South Africa was already declining before the major tax increases began in the mid-1990s. That makes simple before-and-after comparisons misleading. Chelwa introduces synthetic control as a way to construct a credible counterfactual—an estimate of what would have happened in South Africa had the tax policy never been implemented. The method builds a “synthetic” version of South Africa by combining data from comparable countries that did not enact similar tax changes, allowing for a more defensible causal comparison.Chelwa describes how he constructed the donor pool of countries, emphasizing both the “science” (data completeness, predictors of smoking behavior like income and prices) and the “art” (whether the comparison countries make intuitive sense). From an initial pool of roughly two dozen middle-income countries, the method ultimately selected a small weighted subset—countries like Brazil and Argentina—to construct the synthetic control. The result is a counterfactual trend that closely matches South Africa before the policy, then diverges afterward.We discuss the key finding: while smoking was already declining, the decline accelerated substantially after the tax increases compared to the synthetic control. This creates a growing gap over time, illustrating a dynamic treatment effect rather than a single static estimate. Chelwa highlights this as one of the strengths of synthetic control—it allows researchers to see how policy effects evolve year by year.The conversation also covers robustness checks, including “leave-one-out” analyses to ensure results are not driven by any single country in the donor pool. Chelwa emphasizes that while the results can feel almost “too good to be true,” careful validation and alignment with existing literature help build confidence in the findings.We close with Chelwa reflecting on his career trajectory—from a PhD student immersed in the “credibility revolution” in economics to a more interdisciplinary scholar thinking broadly about development and policy. He shares a memorable moment from the project: running the model for the first time, doubting the result, and coming back the next day to confirm it held. As he puts it, that moment captures something essential about research—the mix of skepticism, rigor, and excitement that defines the scientific process.

  • Psychedelics, blinding, and causal inference methods

    Psychedelics, blinding, and causal inference methods

    · Research Notes

    In this episode of Research Notes, I talk with Dr. Gabe Loewinger of the National Institute of Mental Health about a core challenge in psychedelic clinical trials: participants often know whether they received the treatment. This “functional unblinding” means that people in different trial arms can develop very different expectations about what the treatment will do, and those expectations may themselves influence outcomes.

    We explore why common approaches—like adjusting for what participants believe or expect—can actually make things worse from a causal inference perspective. Expectancy is not just a nuisance variable to control for; it sits on the pathway between treatment and outcome. Instead, we discuss an alternative framework based on estimating controlled direct effects, which aims to put treatments on a more equal footing by explicitly accounting for how expectations operate. Along the way, we use causal diagrams and thought experiments to clarify why this is such a difficult problem.

  • When an algorithm encodes inequity

    When an algorithm encodes inequity

    · Research Notes

    In this episode of Research Notes, I talk with Dr. Rohan Khazanchi about his recent paper in JAMA Internal Medicine examining what happened when race was removed from equations used to estimate kidney function.For years, these equations overestimated kidney function for Black patients, delaying access to kidney transplant waitlists and reducing the likelihood of receiving a transplant. We discuss how that happened, what changed, and how a national policy attempted to repair the harm by giving patients time back on the waitlist.We also focus on the methods behind the study—specifically why the team used an interrupted time series design to evaluate the policy and why a difference-in-differences approach ultimately didn’t fit the data.Topics covered:-How race became embedded in kidney function equations-Why removing race changed transplant eligibility-The policy that allowed patients to receive time back on the waitlist-Why the team chose interrupted time series over difference-in-differences-What the data showed after the policy was implemented-Who benefited—and who may have been left outKey finding: Patients received a median of about 1.7 years of additional waitlist time—substantial given typical wait times of 3–5 years.

  • What do we mean by 'clinically meaningful'?

    What do we mean by 'clinically meaningful'?

    · Research Notes

    What does “clinically meaningful” actually mean in psychiatry?

    Compass Pathways recently reported Phase 3 results for COMP360, a synthetic psilocybin treatment for treatment-resistant depression. The company said 39% of treated patients achieved a “clinically meaningful” reduction in symptoms.

    But who decides what counts as meaningful? And how should we interpret a 3–4 point difference on a scale like MADRS?

    In this episode of Research Notes, I talk with Dr. Jerrold “Jerry” Rosenbaum, Stanley Cobb Professor of Psychiatry at Harvard Medical School and director of the Massachusetts General Hospital Center for the Neuroscience of Psychedelics.

    Dr. Rosenbaum was not involved in the Compass study, but he has been closely watching the field and was quoted in STAT News saying the results “probably meet the bar for approval” but do not “shout out to you that this is miraculous.”

    We discuss:

    What makes a treatment effect clinically meaningful in psychiatryHow clinicians think about response, remission, and symptom scales like MADRSWhy Compass introduced a new category of “clinically meaningful” improvementHow restrictive trial criteria can make psychiatric studies hard to interpretWhy average effects may hide meaningful benefit in subgroupsWhether a 3–4 point difference on MADRS matters clinicallyWhy durability, cost, and functional unblinding matter for psychedelic treatments

    A key point from Dr. Rosenbaum: psychiatric trial outcomes are not just numbers on a page. They are consensus-based tools meant to approximate something much messier and more human — whether a person is suffering less, functioning better, and able to live their life again.

    For more:
    https://ghrbook.com/notes/clinically-meaningful.html

  • Episiotomy, Hemorrhage & DAGs

    Episiotomy, Hemorrhage & DAGs

    · Research Notes

    In this interview, I speak with Dr. Judith Lieber (London School of Hygiene & Tropical Medicine) about her recent paper in The Lancet Global Health examining episiotomy and postpartum haemorrhage in women with moderate or severe anaemia.I originally came across this paper while searching for a real-world example to teach directed acyclic graphs (DAGs). It turned out to be a perfect case: clinically important, analytically rigorous, and explicit about how a DAG guided the study design and adjustment strategy.The study draws on data from the WOMAN-2 trial — a large, international trial of tranexamic acid conducted in Pakistan, Nigeria, Tanzania, and Zambia, focused on postpartum bleeding in women with moderate or severe anaemia. Judy joined the trial team toward the end to conduct exploratory analyses using this rich dataset of over 15,000 women.In this conversation, we focus primarily on methods:-How drawing the DAG clarified the causal question-How it determined what to adjust for — and what to avoid adjusting for-The challenge of distinguishing confounders from mediators-Using proxies when key confounders (like shoulder dystocia) are unmeasured-Conducting a quantitative bias analysis to bound potential unmeasured confounding-Balancing complexity and readability when building a DAGWe also discuss Judy’s pathway into epidemiology, her work at LSHTM’s Clinical Trials Unit, and her current project tackling time-varying treatment decisions with another (even more complicated) DAG.This is a practical, applied conversation about how causal diagrams are actually used in real research — not as theoretical exercises, but as tools for clarifying assumptions, structuring models, and understanding limitations.If you teach causal inference, work with observational data, or are trying to move beyond “control for everything” regression thinking, this is a great example of DAGs in action.