The DIA Global Forum's article, "Data Distortions: When Statistics Can Lead Us Astray in Drug Safety," brought attention to a common pitfall: the practice of performing individual hypothesis tests for each adverse event (AE), calculating p-values, and then concluding statistical significance based on an arbitrary threshold like . This mirrors a concern I raised earlier in my blog article, "Should hypothesis tests be performed and p-values be provided for safety variables in efficacy evaluation clinical trials?"
It's a widely held statistical consensus that conducting hypothesis tests for individual AEs is unsound, and the p-values derived from them are prone to misinterpretation. Despite arguments that such tests could be performed for "exploratory purposes" with a disclaimer, the inherent risk is that these p-values will inevitably be misused to draw misleading conclusions from the data. Unfortunately, we've seen journal articles, often at the insistence of editors, include p-values for individual AEs, perpetuating this problematic practice.
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