Aaron Kenna

A central aim of medical research is causal inference. Does this drug have harmful side effects? Is this medical intervention effective? Does this chemical cause cancer? To provide evidence that bears on these important questions, many sorts of measurements are made in a variety of types of studies. These measurements generate a plethora of data, and these data must be quantitatively summarized so they are rendered relevant to causal hypotheses. That is, to render measurements made in medical research into evidence for a causal hypothesis, those measurements must be transformed into summary quantifications, called "outcome measures." This chapter has two aims. First, we argue for the superiority of one form of outcome measure, called absolute measures. Second, we argue against a widely held myth in epidemiology. The myth is that in observational methods, such as case-control studies, only the relative outcome measure called the odds ratio can be calculated, and we argue that there is no justification for this myth.