Are epidemological studies almost worthless?

Browsing through Nassim Nicholas Taleb’s diary I noticed this quote:

At the AAAS conference in San Francisco I was a discutant of session in which John Ioannidis showed that 4 out of 5 epidemiological “statistically significant” studies fail to replicate in controlled experiments.

NNT crows that this is what he has already come to describe as the narrative fallacy. If you look hard enough at enough data, you will see a pattern emerge.

Anyway I have looked up John Ioannidis’s research and found this interesting paper Why Most Published Research Findings Are False
, which unfortunately I haven’t had the chance to read in full.

The outline of his idea is simple enough, if you look at enough data (particularly small data sets) you will find statistically significant relationships. The part I thought was interesting was this.

As has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true (before doing the study), the statistical power of the study, and the level of statistical significance.

Which is kind of obvious. If I correlate enough astrological data with some disease I will inevitably find some correlation, but because the prior probability of it being true is essentially zero there is still very little chance of the study being true.

Some of the corollaries of this cited in the paper are interesting

Corollary 6: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally see major excitement followed rapidly by severe disappointments in fields that draw wide attention. With many teams working on the same field and with massive experimental data being produced, timing is of the essence in beating competition. Thus, each team may prioritize on pursuing and disseminating its most impressive “positive” results. “Negative” results may become attractive for dissemination only if some other team has found a “positive” association on the same question. In that case, it may be attractive to refute a claim made in some prestigious journal. The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations. Empirical evidence suggests that this sequence of extreme opposites is very common in molecular genetics.

Obviously there are a great many fields that data mine to find relationships. With such an approach a 95% confidence interval tells you almost nothing if you didn’t already suspect a relationship between data.

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