Two major statistical fallacies—the prosecutor’s fallacy and Simpson’s paradox—systematically distort causality judgments in pharmaceutical regulation, yet remain largely unaddressed in current oversight frameworks. A comprehensive analysis published in Pharmaceutical Research documents how these reasoning errors lead regulators to opposite safety conclusions from identical datasets, depending on analytical approach and subgroup stratification.
The prosecutor’s fallacy causes regulators to confuse rare safety signals with proof of drug safety, while Simpson’s paradox obscures age-stratified and dose-dependent harms that disappear in aggregate data. Neither the FDA nor the EMA mandate formal causal logic auditing before approval decisions, leaving the global drug safety system vulnerable to systematic misinterpretation.
To address this gap, researchers propose implementing AI-powered causal inference models in pharmacovigilance workflows. These systems could identify hidden confounding variables and correctly calculate conditional probabilities, potentially reducing false negatives and false positives in safety assessment.
Read the full article on GMJ Newsroom.
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