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When the Denominator Disappears

Why FDA's Next Evolution Should Be Causal Inference

By

Peter Pitts

Robert Goldberg

August 05, 2026

AP

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The FDA advisory committee's recent vote against Capricor Therapeutics' cell therapy for Duchenne muscular dystrophy looked like a decision about one product. It may ultimately be remembered as something much larger. Beneath the discussion of endpoints, missing data, and statistical analyses lay a larger question that will not disappear after the next advisory committee meeting. Precision medicine has changed the way diseases are understood. Regulatory science now must decide whether its methods are changing quickly enough to keep up.

No one should dismiss the committee's conclusion. FDA reviewers raised legitimate questions about how the trial should be interpreted, and the advisory committee concluded that the evidence fell short of demonstrating efficacy. Fair enough. The more interesting question is whether the tools used to judge today's therapies were designed for yesterday's medicine.

FDA has been here before. The debate has never been whether to lower scientific standards. It has been whether better science demands better tools. HIV forced that conversation. Oncology did too. Rare diseases are forcing it again. Accelerated approval, orphan-drug incentives, adaptive trial designs, Bayesian methods, model-informed drug development, and real-world evidence all grew from the same principle: scientific rigor does not require methodological rigidity. Capricor may represent the next chapter in that story.

Judea Pearl's work on causal inference helped us recognize what we now call denominator collapse. The phrase sounds technical, but the idea is remarkably simple. Precision medicine succeeds by revealing biological differences that once went unseen. Every newly discovered mutation, every validated biomarker, every molecular subtype narrows the group of patients eligible for a therapy. Better biology produces smaller denominators.

That is exactly what biomedical research has been trying to accomplish for decades. The surprise is that success has created a new problem. Every important biological discovery leaves fewer patients available to generate the evidence regulators have traditionally relied upon.

Duchenne muscular dystrophy is only one example. ALS has followed much the same path. What physicians once regarded as a single neurodegenerative disease is increasingly understood as a collection of biologically distinct disorders driven by different genetic mutations, molecular pathways, and rates of progression. Inherited retinal disorders, pediatric epilepsies, and much of modern oncology are moving in the same direction. Precision medicine has not made these diseases more complicated. It has revealed how complicated they always were.

That creates an uncomfortable statistical reality. A therapy may produce substantial benefit in one biologically defined subgroup, little effect in another, and perhaps none in a third. Average those outcomes across a trial enrolling only a few dozen patients and the treatment effect begins to disappear. Biology has not failed. Arithmetic has.

Randomization remains the single most effective protection against bias ever devised. It is indispensable. But randomization is not magic. It depends on numbers. Once trials become very small, chance alone can produce important imbalances in disease severity, progression rates, biomarker profiles, treatment discontinuation, or missing observations. The familiar response is to recommend another, larger study. Increasingly, that recommendation assumes a patient population that no longer exists.

The problem is hardly unique to Capricor. Only a day later another FDA advisory committee wrestled with Replimune's investigational melanoma therapy, RP1. There the challenge was almost the reverse. Reviewers were less concerned about whether patients improved than about what caused the improvement. How much benefit came from RP1 itself? How much reflected concomitant immunotherapy? How much resulted from patient selection or the natural history of the disease? Different therapeutic area. Different study design. The same fundamental question: what caused the observed outcome?

Classical statistics is extraordinarily good at measuring association. Causal inference asks something harder. Why did this happen? What would likely have happened had treatment never occurred? Those counterfactual questions become increasingly important as biology shrinks clinical trials beyond the point where...