Getting Casual about Cause

Chapter 7 explains epidemiologic study designs and presents them in a pyramid format. The closer to

the top of the pyramid, the better the study design is at providing evidence for causal inference,

meaning providing evidence of a causal association between the exposure with the outcome (or in the

case of a clinical trial of an intervention, the intervention and the outcome). At the top of the pyramid

are systematic review and meta-analysis, where the results of similar studies are combined and

interpreted. Because systematic reviews and meta-analyses combine results from other high-quality

studies, they are at the very top of the pyramid — meaning they provide the strongest evidence of a

causal association between the exposure or intervention and outcome.

An international organization called the Cochrane Collaboration organizes the production of

systematic reviews and meta-analyses to help guide clinicians. Their reviews are internationally

renowned for being high-quality and are available at www.cochrane.org.

The study designs on the evidence-based pyramid that could be answered with a regression model

include clinical trial, cohort study, case-control study, and cross-sectional study. If in your final model

your exposure is statistically significantly associated with your outcome, you now have to see how

much evidence you have that the exposure caused the outcome. This section provides two methods by

which to evaluate the significant exposure and outcome relationship in your regression: Rothman’s

causal pie and Bradford Hill’s criteria of causality.

Rothman’s causal pie

Kenneth Rothman described how causes of an outcome are not determinate. In other words, two

people can have the same values of covariates and one will get the outcome, and the other will not. We

can’t say for sure what values of covariates will mean that you will for sure get the outcome. But that

doesn’t mean you can’t make causal inferences. Rothman conceptualized cause as an empty pie tin, and

when the pie tin is filled 100 percent with pieces of risk contributed by various causes, then the

individual will experience the outcome. The exposure and confounders in your regression model

represent these pieces.

For example, cigarette smoking is a very strong cause of lung cancer, as is occupational exposure to

asbestos. There are other causes, but for each individual, these other causes would fill up small pieces

of the causal pie for lung cancer. Some may have a higher genetic risk factor for cancer. However, if

they do not smoke and stay away from asbestos, they will not fill up much of their pie tin, and may

have necessary but insufficient cause for lung cancer. However, if they include both asbestos

exposure and smoking in their tin, they are risking filling it up and getting the outcome.

Bradford Hill’s criteria of causality

Sir Bradford Hill was a British epidemiologist who put forth criteria for causality that can be

useful to consider when thinking of statistically significant exposure–outcome relationships from

final regression models. Although there are more than the criteria we list here, we find the

following criteria to be the most useful when evaluating potential exposure–outcome causal