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      Exposure-wide epidemiology: revisiting Bradford Hill.

      1 , 2 , 3 , 4

      Statistics in medicine

      bias, causation, epidemiology

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          Fifty years after Bradford Hill published his extremely influential criteria to offer some guides for separating causation from association, we have accumulated millions of papers and extensive data on observational research that depends on epidemiologic methods and principles. This allows us to re-examine the accumulated empirical evidence for the nine criteria, and to re-approach epidemiology through the lens of exposure-wide approaches. The lecture discusses the evolution of these exposure-wide approaches and tries to use the evidence from meta-epidemiologic assessments to reassess each of the nine criteria and whether they work well as guides for causation. I argue that of the nine criteria, experiment remains important and consistency (replication) is also very essential. Temporality also makes sense, but it is often difficult to document. Of the other six criteria, strength mostly does not work and may even have to be inversed: small and even tiny effects are more plausible than large effects; when large effects are seen, they are mostly transient and almost always represent biases and errors. There is little evidence for specificity in causation in nature. Biological gradient is often unclear how it should it modeled and thus difficult to prove. Coherence remains usually unclear how to operationalize. Finally, plausibility as well as analogy do not work well in most fields of investigation, and their invocation has been mostly detrimental, although exceptions may exist.

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          Author and article information

          Stat Med
          Statistics in medicine
          May 20 2016
          : 35
          : 11
          [1 ] Department of Medicine, Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, U.S.A.
          [2 ] Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, U.S.A.
          [3 ] Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA, U.S.A.
          [4 ] Meta-Research Innovation Center at Stanford (METRICS), Stanford, CA, U.S.A.
          Copyright © 2015 John Wiley & Sons, Ltd.

          epidemiology, causation, bias


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