Epidurals are widely used and widely trusted for pain relief during labor. So when a 2020 study reported that they might be linked to autism, it raised a troubling question: could a routine medical decision have long-term consequences? We follow that claim from headline to evidence—and watch what happens when other scientists take a closer look. We dig into the original study, a wave of replication studies from around the world, and a meta-analysis that tries to make sense of it all. Along the way, we unpack hazard ratios, Cox regression, inverse probability weighting, and sibling analyses—and why even sophisticated statistical adjustment can’t eliminate confounding. Plus: why bigger datasets don’t solve everything, what happens when results shrink after adjustment, and how a controversial study turned into a case study in science working as it should. Bonus: our first guest journalist interview!
Statistical topics
Confounding
Cox regression
Hazard ratios
Inverse probability weighting (IPTW)
Multivariable adjustment
Observational studies
Residual confounding
Retrospective cohort studies
Sibling analysis
Statistical adjustment
Statistical significance vs practical significance
Survival analysis
Methodological morals
“Every time you adjust the model and the effect gets smaller, that's the universe whispering, maybe don't build a causal story out of this.”
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