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The effect(s) of such misclassification can vary from an overestimation to an underestimation of the true value. [4] Statisticians have developed methods to adjust for this type of bias, which may assist somewhat in compensating for this problem when known and when it is quantifiable. [5]
Recall bias is of particular concern in retrospective studies that use a case-control design to investigate the etiology of a disease or psychiatric condition. [ 3 ] [ 4 ] [ 5 ] For example, in studies of risk factors for breast cancer , women who have had the disease may search their memories more thoroughly than members of the unaffected ...
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Notable bias (spin) has been reported in the interpretation of results of randomized control trials, although these study designs rank top in the level-of-evidence hierarchy. [36] [37] [38] Contrastingly, a study found low prevalence of bias in the conclusions of non-randomized control trials published in high-ranking orthopedic publications. [39]
Information bias (epidemiology), bias arising in a clinical study because of misclassification of the level of exposure to the agent or factor being assessed and/or misclassification of the disease or other outcome itself. Information bias (psychology), a type of cognitive bias, involving e.g. distorted evaluation of information.
The retrospective updating method can lead to a considerable bias in vaccine studies, biasing observed mortality rate ratios towards zero (a large effect), whereas the landmark method leads to a non-specific misclassification and biases the mortality rate ratio towards unity(no effect).
Bradford Hill's criteria had been widely accepted as useful guidelines for investigating causality in epidemiological studies but their value has been questioned because they have become somewhat outdated. [5] In addition, their method of application is debated. [citation needed] Some proposed options how to apply them include:
Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.