Bias Flashcards
Berkson’s bias (aka admission rate bias)
A form of selection bias that causes hospital cases and controls in a case control study to be systematically different from one another because the combination of exposure to risk and occurrence of disease increases the likelihood of being admitted to the hospital. Berkson’s bias (aka admission rate bias): This is a type of bias resulting from case-control studies whereby cases and controls are selected from hospital settings. In such settings, cases can be unrepresentative of the general population and this can lead to confounding factors.
Neyman bias
Neyman Bias is a selection bias where the very sick or very well (or both) are erroneously excluded from a study. The bias (“error”) in your results can be skewed in two directions: Excluding patients who have died will make conditions look less severe.
Publication bias
Publication bias occurs when important evidence has failed to be considered. Funnel plots can be used to check for this.
Selection bias
(when selected sample is not a representative sample of reference population
Subtypes of selection bias
Loss to follow up bias, disease spectrum bias, self selection bias, participation bias, incidence prevalence bias, exclusion bias, publication of dissemination bias, citation bias, berkson’s bias
Subtypes of information bias
Detection bias, recall bias, lead time bias, interviewer/observer bias, verification and workup bias, Hawthorne effect, ecological fallacy
Disease spectrum bias (aka case mix bias)
this can occur when a treatment is studied in more sever forms of a disease. Such results may then not apply to mild forms of the disease.
Self selection bias
Those who volunteer may have shared characteristics resulting in a unrepresentative sample.
Participation bias (non-response bias)
Those who participate may have shared characteristics resulting in a unrepresentative sample.
Incidence prevelance bias
Incidence-Prevalence bias (Survival bias, Neyman bias): Occurs in case-control studies and is attributed to selective survival among the prevalent cases (i.e. mild, clinically resolved, or fatal cases being excluded from the case group).
Exclusion bias
Occurs when certain patients are excluded for example if they are considered ineligible
Publication bias
Many studies may not be published. This may be due to the fact that papers with positive results, and large sample sizes are more likely to get published.
Citation bias
Articles of high citation are easy to reach and have higher chance to be entered into a given study
Information bias
when gathered information about exposure, outcome or both is not correct and there was an error in measurement )
Detection bias
This can occur when exposure can influence diagnosis. For example women taking an oral contraceptive will have more frequent cervical smears than women who are not on the pill and so are more likely to have cervical cancer diagnosed (if they actually have it). Thus, in a case-control study that compared women with cervical cancer and a control group, at least part of any higher pill consumption rates amongst the former group may be due to this effect.