Although experimental “interventionist” studies ( as in placebo vs. experimental treatment) are generally considered the most powerful  research design, “observational” study data (as in Ecological populations, Cross section studies, Case control studies and Cohort studies)  is much easier to come by,  often due to cost alone.

In the circumstances it becomes very important to distinguish between association and causation. Some observations on these topics:

1)”Because observational studies are not randomized, they cannot control for all of the other inevitable, often unmeasurable, exposures or factors that may actually be causing the results. Thus, any “link” between cause and effect in observational studies is speculative at best.”

2)”Readers of medical literature need to consider two types of validity, internal and external. Internal validity means that the study measured what it set out to; external validity is the ability to generalize from the study to the reader’s patients. With respect to internal validity, selection bias, information bias, and confounding are present to some degree in all observational research.

• Selection bias stems from an absence of comparability between groups being studied. Information bias results from incorrect determination of exposure, outcome, or both.
• The effect of information bias depends on its type. If information is gathered differently for one group than for another, bias results.
• By contrast, non-differential misclassification tends to obscure real differences.
• Confounding is a mixing or blurring of effects: a researcher attempts to relate an exposure to an outcome but actually measures the effect of a third factor (the confounding variable). Confounding can be controlled in several ways: restriction, matching, stratification, and more sophisticated multivariate techniques.

If a reader cannot explain away study results on the basis of selection, information, or confounding bias, then chance might be another explanation. Chance should be examined last, however, since these biases can account for highly significant, though bogus results. Differentiation between spurious, indirect, and causal associations can be difficult. Criteria such as temporal sequence, strength and consistency of an association, and evidence of a dose-response effect lend support to a causal link.“

Source 1: Healthnewsreview.org

Article: “Observational studies: Does the language fit the evidence? Association vs. causation”

Source2: NCBI.org

Article: “Bias and causal associations in observational research.”

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