[A short history of the concept of bias in epidemiology]

Bias is a major methodological issue for epidemiology. However, only a few studies have been dedicated to the past and present formulations of the concept of bias. Moreover, the classical definition of bias as systematic deviation from the truth of results or inferences, definition which can be found in dictionaries of epidemiology, does not seem to either match the way epidemiologists use it in practice, or correspond to the different definitions given throughout its history. It is consequently important to elucidate this paradox.

In this historical and conceptual article, we study the different uses of the word "bias" in epidemiological literature, from classic articles in the 1950's about the link between smoking and lung cancer to the most recent epidemiology textbooks, the objective being to analyze the ways in which epidemiologists have defined, applied and modified this concept over time.

We show that D.L. Sackett's article on bias in analytic research, published in 1979, put an end, at least temporarily, to reflection in populational epidemiology that started thirty years before. More precisely, we show that Sackett's definition of bias corresponds more to the needs and goals of clinical epidemiology than to those of populational epidemiology. Concomitantly, populational epidemiologists such as K.J. Rothman redefined bias as a threat to the internal validity of a study, and epidemiological study as an "exercise in measurement of an effect rather than as a criterion-guided process for deciding whether an effect is present or not".

It is thereby important to draw a distinction between two notions pertaining to bias: an epidemiological concept of bias, viewed as the lack of internal validity of an observational study; and a medical concept of bias, defined as deviation from the truth. The former concerns the design and methodology of epidemiological studies; the latter is more general and impels epidemiologists and physicians to be skeptical, and even critical, towards their own inferences.