Causation in the presence of weak associations

[mccoy]

There is a part of my job which is beyond routine and this year it's particularly interesting, my office is trying to find a satisfactory dose-response model when exposure to asbestos inhalation occurs. Relationship to apply for workers' compensation. So, I had to delve into relative risk, odds ratio, excess lifetime risk, attributable risk and all the biostatistical lingo. Since the models in literature are often different one from the other, I'm specifically trying to label risk levels compared to the numbers of relative risk and odds ratio. This relates directly to nutrition, since we've been pretty often discussing metanalyses on epidemiological studies and forest plots. I had a hint that the association in nutrition epidemiology tend to be weak, and that absolute risk should also be considered.

This concise article explains the essentials on the causation-association issue when the association is weak. I

It turns out that probably all the associations in nutrition are pretty weak. Also, the classic exhaustive criteria to infer causation from association are listed.

 

Crit Rev Food Sci Nutr. 2010 Dec; 50(s1): 13–16.

Published online 2010 Dec 4. doi: 10.1080/10408398.2010.526842

PMCID: PMC3024843

Causation in the Presence of Weak Associations

Paolo Boffetta

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able 1

Guidelines for causality in epidemiologic studies, according to Hill (1965)

Strength of Association. The stronger the relationship between the independent variable and the dependent variable, the less likely it is that the relationship is because of an extraneous variable.

Temporality. It is logically necessary for a cause to precede an effect in time.

Consistency. Multiple observations, of an association, with different people under different circumstances and with different measurement instruments increase the credibility of a finding.

Theoretical Plausibility. It is easier to accept an association as causal when there is a rational and theoretical basis for such a conclusion.

Coherence. A cause-and-effect interpretation for an association is clearest when it does not conflict with what is known about the variables under study and when there are no plausible competing theories or rival hypotheses. In other words, the association must be coherent with other knowledge.

Specificity in the Causes. In the ideal situation, the effect has only one cause. In other words, showing that an outcome is best predicted by one primary factor adds credibility to a causal claim.

Dose-Response Relationship. There should be a direct relationship between the risk factor (i.e., the independent variable) and people's status on the disease variable (i.e., the dependent variable).

Experimental Evidence. Any related research that is based on experiments will make a causal inference more plausible.

Analogy. Sometimes a commonly accepted phenomenon in one area can be applied to another area.

 

The strength of the association is one of the original criteria that have been maintained in all of the subsequent formulations. The observation of a strong statistical association between a suspected risk (or a protective) factor and a condition or disease, typically determined by a measure of the incidence (or prevalence) of the condition among the exposed relative to that among the unexposed (often loosely defined as “relative risk”), adds credibility to its causal nature (Rothman et al., 2008a). The modern interpretation of this criterion, which has an instinctive appeal, is that chance, bias, and unmeasured confounding are less likely to explain (or at least to completely explain) a relative risk that is further away from the null. Although any measure of risk would follow a continuous distribution and there are no predefined values that separate “strong” from “moderate” or “weak” associations, relative risks below 3 are considered moderate or weak (Wynder, 1987).

 

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In the case of diet and cancer, the results of early studies, mainly of case-control design, pointed toward the existence of relatively strong associations between certain components of diet and cancer risk. However, during the last decade, the analysis of prospective studies, which are less prone to bias but may include populations with limited exposure contrast, has mainly resulted in weak (or null) associations. This is shown by comparing the evaluations of the evidence between fruit and vegetable intake and cancer risk from the World Cancer Research Fund in 1997 and in 2007; with a few notable exceptions, the strength of the evidence for these associations was judged to be weaker in the second report as compared with the first one (World Cancer Research Fund, 1997, 2007). Although it could be argued that recent studies might have underestimated the effect of diet on cancer risk, it is also likely that most of the associations to be identified in relationship to diet are of small magnitude.