Opinion ID: 1919140
Heading Depth: 1
Heading Rank: 13

Heading: Measuring the Strength of an Association in Epidemiological Studies

Text: When an epidemiological study shows an association, experts often report its strength as the relative risk. [31] The relative risk is one of the cornerstones for causal inferences. [32] It refers to the increased probability for an individual in an exposed population to develop a disease. [33] Experts describe relative risk as a ratio of the incidence rate of disease in the exposed group to the incidence rate in the unexposed group: i.e., the incidence rate in the exposed group divided by the incidence rate in the unexposed group. [34] For example, if a study found that 10 out of 1000 women with breast implants were diagnosed with breast cancer and 5 out of 1000 women without implants (the control group) were diagnosed with breast cancer, the relative risk of implants is 2.0, or twice as great as the risk of breast cancer without implants. This is so, because the proportion of women in the implant group with breast cancer is 0.1 (10/1000) and the proportion of women in the non-implant group with breast cancer is 0.05 (5/1000). And 0.1 divided by 0.05 is 2.0. [35] If both groups have the same incidence rate, the relative risk is 1.0, meaning that no association exists between the agent and the disease. If the study shows a relative risk less than 1.0, the association is negative. This means that the risk to the exposed population is less than the risk to the unexposed population. [36] If the study shows a relative risk greater than 1.0, a positive association exists, which could be causal, because the risk to the exposed population is greater than the risk to the unexposed group. [37] So to support a causal inference, the relative risk must be greater than 1.0. And [t]he higher the relative risk, the greater the likelihood that the relationship is causal. [38] Some studies, however, use different measurements to express a relationship between an agent and disease. [39] For example, in a case-control study, an odds ratio measurement provides essentially the same information as relative risk. [40] A trial judge might also have to consider whether an expert properly relied on a meta-analysis. Researchers and experts sometimes use meta-analyses to pool the results of smaller studies that fail to support definitive conclusions. [41] A meta-analysis combines and analyzes the data from several epidemiological studies to arrive at a single figure to represent all of the studies reviewed. [42] If a study shows a relative risk of 2.0, the agent is responsible for an equal number of cases of disease as all other background causes. [43] This finding implies a 50% likelihood that an exposed individual's disease was caused by the agent. [44] If the relative risk is greater than 2.0, the study shows a greater than 50-percent likelihood that the agent caused the disease. Thus, some courts have permitted a relative risk greater than 2.0 to support an inference of specific causation. [45] Lower relative risks can also reflect general causation, but epidemiologists scrutinize weak associations because they have a greater chance of being explained by another factor or an error in the study. [46] But remember, before experts reach any type of causative conclusion based on observational studies, they rule out potential sources of error in the supporting studies.