BBIU WP | The Bayesian Efficacy Integrity Framework (BEIF)
A Proposal for Truth-Preserving Bayesian Drug Regulation
Executive Abstract
The FDA’s transition toward Bayesian methodology and lifecycle-based regulation represents the most profound transformation in drug governance since the birth of randomized controlled trials.
However, without explicit safeguards against population manipulation, selective likelihood construction, and exclusion-driven bias, Bayesian regulation will not produce medical truth. It will produce mathematically legitimized distortion.
This white paper introduces the Bayesian Efficacy Integrity Framework (BEIF) — a regulatory architecture designed to ensure that Bayesian inference remains clinically honest, population-anchored, and epistemically defensible.
BEIF transforms drug evaluation from trial-based judgment into diagnostic probability governance, aligning regulatory science with how real medicine determines truth.
1. The Structural Problem
Bayesian inference does not fail because of mathematics.
It fails because of who and what are allowed into the likelihood.
Under Bayesian FDA:
Posterior∝Prior×LikelihoodPosterior \propto Prior \times LikelihoodPosterior∝Prior×Likelihood
If sponsors are allowed to:
• exclude inconvenient patients
• narrow populations
• filter outcomes
• manipulate endpoints
they can inflate the likelihood without falsifying data.
The posterior becomes optimistic by construction.
This is more dangerous than p-hacking — because it is invisible.
2. From Endpoints to Diagnostic Truth
Medicine never diagnoses disease with one test.
It uses:
• symptoms
• imaging
• biomarkers
• pathology
• evolution over time
Each signal contributes probabilistically.
BEIF applies this diagnostic logic to drug efficacy.
Efficacy is no longer:
“Did it beat placebo?”
It becomes:
“How likely is it that this therapy truly benefits real patients?”
3. The Efficacy Diagnostic Algorithm (EDA)
Every Bayesian efficacy claim must integrate:
• Clinical response
• Biomarkers (e.g., MRD, ctDNA)
• Imaging
• Functional outcomes
• Safety
• Durability
• Real-world evidence
• Manufacturing stability
Each component is weighted by:
• sensitivity
• specificity
• population coverage
The result is:
P(True Clinical Benefit)P(\text{True Clinical Benefit})P(True Clinical Benefit)
Not a p-value.
4. Gold Standard and NID-Anchored Comparison
When a standard therapy exists, BEIF requires:
For each cohort:
• baseline severity
• response to standard
• response to new therapy
• NID (minimum clinically meaningful difference)
The regulatory question becomes:
P(New Therapy beats Standard by ≥ NID)P(\text{New Therapy beats Standard by ≥ NID})P(New Therapy beats Standard by ≥ NID)
This is clinically meaningful probability — not legal separability.
5. Population Integrity as a Mathematical Requirement
Every inclusion/exclusion rule defines the universe to which the posterior applies.
BEIF mandates:
For each exclusion:
• % of real-world population removed
• expected clinical impact
• uncertainty introduced
This yields an Exclusion Penalty (EP).
Posterior=Prior×Likelihood×(1−EP)Posterior = Prior \times Likelihood \times (1 - EP)Posterior=Prior×Likelihood×(1−EP)
The more artificial the population, the less regulatory weight the evidence carries.
No patient left out for free.
6. Three-Layer Efficacy Reporting
All programs must declare:
E1 — Induction Efficacy
Early biological response (e.g., MRD-neg at day 28)
E2 — Durability Efficacy
Sustained effect without rescue
E3 — Clinical Utility
Survival / function / long-term outcome
No therapy may claim E3 if dominated by later interventions (e.g., transplant) without explicit attribution modeling.
7. Bayesian Evidence Declaration (BED)
Every efficacy claim must include:
Point estimate
Credible interval
P(θ>NID)P(\theta > NID)P(θ>NID)
Prior sensitivity (skeptical / neutral / enthusiastic)
Fragility index (how many failures collapse belief)
This replaces “82% efficacy” with epistemically honest medicine.
8. ClinicalTrials.gov as Likelihood Ledger
To prevent posterior corruption, BEIF requires:
• locked endpoints
• locked SAPs
• versioned amendments
• mandatory negative reporting
• audit trails
ClinicalTrials.gov becomes:
the regulatory ledger of what is allowed to influence belief
Not a marketing archive.
9. Why This Protects Everyone
Patients
→ aren’t misled by population-filtered hype
Regulators
→ retain epistemic authority
Investors
→ price real risk instead of frozen illusions
Innovation
→ is rewarded when real, not when engineered
BBIU Final Structural Judgment
Bayesian FDA without population integrity becomes a propaganda machine.
Bayesian FDA with BEIF becomes the first regulatory system in history that governs truth rather than events.
This framework makes belief honest — or mathematically expensive to fake.
BEIF Case Application
A Hypothetical Oncology Monoclonal Antibody
(This maps directly to dozens of real NEJM Phase III programs.)
1. Legacy FDA Reading
A Phase III trial reports:
ORR: 48% vs 32% control
PFS: 6.2 vs 4.8 months
p = 0.03
Conclusion under frequentist FDA:
“The drug is effective.”
Approval granted.
Truth frozen.
2. What Bayesian FDA Actually Asks
Instead of “did it separate from control?”, Bayesian FDA asks:
“How likely is it that this drug produces a clinically meaningful benefit in real patients?”
Define:
NID (minimum meaningful PFS benefit) = 2 months
3. Prior
From biology + similar antibodies:
Probability that this target produces ≥2-month PFS benefit:
≈ 30%
That is the prior.
4. Likelihood (from the Phase III)
The observed 1.4-month median PFS improvement is modest and noisy.
The data are:
compatible with real benefit
but also with statistical fluctuation
This updates the belief, but not decisively.
Posterior becomes:
≈ 55%
Not 95%.
Not certainty.
Just:
slightly more likely than not.
5. Efficacy Diagnostic Algorithm (EDA)
Now BEIF integrates:
ORR signal
PFS
biomarker response
safety
subgroup response
early RWE
Each is a diagnostic test.
The posterior becomes:
≈ 60–65%
6. Exclusion Penalty
Trial excluded:
elderly
renal failure
ECOG ≥2
These are ~40% of real patients.
Penalty = 0.4
Adjusted belief:
0.60 × (1 − 0.40) ≈ 0.36
Meaning:
In real-world patients, there is only ~36% probability this drug delivers clinically meaningful benefit.
7. What Legacy FDA Would Do
Approve.
$5B peak sales.
No one knows the truth until years later.
8. What Bayesian FDA + BEIF Does
The FDA sees:
“This drug probably helps a minority of patients, with weak durability, and uncertain generalizability.”
Regulatory outcomes:
narrow label
RWE-linked continuation
conditional reimbursement
price pressure
9. Why This Is the Correct Reality
The drug is not “bad.”
But it is not what the p-value said.
It is:
a modestly effective, population-limited therapy.
Which is what most oncology antibodies actually are.
BBIU Structural Diagnosis
Monoclonal antibodies expose the truth of Bayesian FDA better than CAR-T:
Most drugs that “pass Phase III”
do not produce strong, portable belief.
They produce weak, unstable posteriors.
And the new FDA will finally reflect that.
