Epidemiology 2: Chance, bias, confounding

BIAS & CONFOUNDING M . Tevfik DORAK

Introduction In epidemiologic research, it is essential to avoid bias, to control confounding and to undertake accurate replication. Bias, confounding and random variation/chance are the non-causal reasons for an association between an exposure and outcome. These major threats to internal validity of a study should always be considered as alternative explanations in the interpretation. Bias is ...

Assessing bias: the importance of considering confounding

When a difference in an outcome (eg, pain) between exposures (eg, treatment groups) is observed, one needs to consider whether the effect is truly because of exposure or if alternate explanations are possible. As we discussed in the previous issue of EBSJ, to evaluate the validity of a research study, factors that might distort the true association and/or influence its interpretation need to be...

Ecological bias, confounding, and effect modification.

Ecological bias is sometimes attributed to confounding by the group variable (ie the variable used to define the ecological groups), or to risk factors associated with the group variable. We show that the group variable need not be a confounder (in the strict epidemiological sense) for ecological bias to occur: effect modification can lead to profound ecological bias, whether or not the group v...

Antifibrinolytic therapy: evidence, bias, confounding (and politics!).

Cardiac surgery can be complicated by postoperative bleeding and a need for blood transfusion and surgical reexploration. Anti-fibrinolytic drugs such as aprotinin and tranexamic acid may reduce bleeding risks but could possibly increase thrombotic complications. Aprotinin, in particular, has recently been implicated in at least two large observational studies, but this could be because it is m...

Confounding from smoking in occupational epidemiology.