Drug regulators face a critical challenge: they routinely confuse the probability that evidence exists given a drug is safe with the probability the drug is actually safe given observed evidence. This fundamental statistical error—known as the prosecutor’s fallacy—can delay life-saving medications or mask genuine harms, according to a new analysis in Pharmaceutical Research.
The study reveals that causality assessment in pharmacovigilance is systematically compromised by inverted conditional probabilities and Simpson’s paradox, which can reverse safety conclusions depending on how patient subgroups are analyzed. Current regulatory workflows lack mandatory causal logic auditing, leaving decisions vulnerable to these predictable reasoning errors.
Artificial intelligence systems using causal inference models offer a potential safeguard, though validation remains incomplete. The authors argue that integrating AI-assisted oversight into FDA and EMA approval processes could reduce cognitive bias and strengthen evidence interpretation without requiring regulatory overhaul.
Read the full article on GMJ Newsroom.
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