For example, in a case-control study of the association between smoking and lung cancer the inclusion of controls being treated for a condition related to smoking e.
Table 1 provides a summary of different types of bias, when they occur, and how they might be avoided. Sometimes it will be necessary to interview patients about potential factors such as history of smoking, diet, use of traditional eye medicines, etc.
For all potential sources of bias, it is important to consider the likely magnitude and the likely direction of the bias. In reporting bias, individuals may selectively suppress or reveal information, for similar reasons for example, around smoking history.
In case-control studies where cases are hospital based, it is common to recruit controls from the hospital population. As a consequence, the estimated association is not that same as the true effect of exposure X on the outcome.
Volunteers tend to be more health conscious than the general population. Therefore, the ideal control group would comprise a random sample from the general population that gave rise to the cases. These calculations are usually made with computer programmes e.
For example, if cases are selected from a defined population such as a GP register, then controls should comprise a sample from the same GP register. As far as possible, studies should be designed to control for this - for example, by testing for diabetes at one time of day.
Controlling for confounding Confounding can be addressed either at the study design stage, or adjusted for at the analysis stage providing sufficient relevant data have been collected.
TEM might cause corneal ulcers but it is also possible that the presence of a corneal ulcer leads some people to use TEM. Issues in the design of case-control studies 2. Bias in Epidemiological Studies While the results of an epidemiological study may reflect the true effect of an exposure s on the development of the outcome under investigation, it should always be considered that the findings may in fact be due to an alternative explanation1.
Understanding research bias allows readers to critically and independently review the scientific literature and avoid treatments which are suboptimal or potentially harmful. Bias Bias may be defined as any systematic error in an epidemiological study that results in an incorrect estimate of the true effect of an exposure on the outcome of interest.
Detection bias occurs where the way in which outcome information is collected differs between groups. This section also covers: Observers may underestimate the blood pressure in those who have been treated, and overestimate it in those in the control group.
They are comparatively quick, inexpensive, and easy. Repeatability When there is no satisfactory standard against which to assess the validity of a measurement technique, then examining its repeatability is often helpful.
For example, the use of both hospital and neighbourhood controls. Many residents are not home, but they persist and eventually enroll enough controls.
Alternatively, the bias within a survey may be neutralised by random allocation of subjects to observers. This may include factors with a direct causal link to the disease, as well as factors that are proxy measures for other unknown causes, such as age and socioeconomic status.
Its success in this respect depends on fulfilling several interrelated processes. As a result, the interpretation of results based on prevalent cases may prove more problematic, as it may be more difficult to ensure that reported events relate to a time before the development of disease rather than to the consequence of the disease process itself.
Where possible, observers should be blinded to the exposure and disease status of the individual Blind observers to the hypothesis under investigation.
This is known as recall bias.In this paper, we will define bias and identify potential sources of bias which occur during study design, study implementation, and during data analysis and publication.
This can be a particular problem with case-control and retrospective cohort studies where exposure and outcome have already occurred at the time individuals are selected.
Introduction In case-control studies, the role of adjustments for bias, and in particular the role of matching, has been extensively debated (). However the absence of a formal statement of the problem has led to disagreements, confusion, and occasionally to erroneous conclusions.
Selection bias arises either when cases in the study sample are not representative of cases arising from the source population (“study base”) or when controls are not representative of corresponding noncases in the study base.
Bias in case-control studies. Areview JacekAKopec, JohnMEsdaile It has been widely accepted that one reason for inconsistent or contradictory results of epidemiologic studies is bias.
Therefore, an appreciation of potential sources of bias has becomea critical issue in epidemiology. Alarge number of different sources and possible mechanisms of. For example, if all methodological limitations of studies were expected to bias the results towards a lack of effect, and the evidence indicates that the intervention is effective, then it may be concluded that the intervention is effective even in the presence of these potential biases.
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