Selection bias case study

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In case-control studies , selection bias can occur in the selection of cases if they are not representative of all cases within the population, or in the selection of controls if they are not representative of the population that produced the cases [1]. Example: in a hospital-based case-control study looking at the relationship between alcohol consumption and development of liver cirrhosis, in the first instance we select our controls from patients hospitalised due to trauma Controls A. But, how representative are hospitalised trauma patients of the population which gave rise to the cases? In the trauma ward, where we have selected our controls, there may be a higher proportion of patients who report heavy alcohol use compared to those who report heavy drinking in the population which produced the cases, leading to an underestimation of the odds ratio OR.

Selection bias and case-control studies

Selection bias is the bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed. The phrase "selection bias" most often refers to the distortion of a statistical analysis , resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may be false. Sampling bias is systematic error due to a non- random sample of a population, [2] causing some members of the population to be less likely to be included than others, resulting in a biased sample , defined as a statistical sample of a population or non-human factors in which all participants are not equally balanced or objectively represented. A distinction of sampling bias albeit not a universally accepted one is that it undermines the external validity of a test the ability of its results to be generalized to the rest of the population , while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand. In this sense, errors occurring in the process of gathering the sample or cohort cause sampling bias, while errors in any process thereafter cause selection bias. It is closely related to the survivorship bias , where only the subjects that "survived" a process are included in the analysis or the failure bias , where only the subjects that "failed" a process are included.

Berkson's Bias

We are currently in the process of updating this chapter and we appreciate your patience whilst this is being completed. 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 explanation 1. Such alternative explanations may be due to the effects of chance random error , bias or confounding which may produce spurious results, leading us to conclude the existence of a valid statistical association when one does not exist or alternatively the absence of an association when one is truly present 1. Observational studies are particularly susceptible to the effects of chance, bias and confounding and these factors need to be considered at both the design and analysis stage of an epidemiological study so that their effects can be minimised. 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.
Background: In most case control studies the hardest decision is the choice of the control group, as in the ideal control group the proportion exposed is the same as in the population that produced the cases. Methods: A comparison of two control groups in a case control study of the efficacy of BCG revaccination. One group was selected from subjects presenting to the heath unit the case attended for routine prevention and care; the second group was selected from the neighbourhood of cases. Efficacy estimated in a randomized control trial of BCG revaccination was used to establish that the neighbourhood control group was the one that gave unbiased results. Results: The proportion of controls with scars indicating BCG revaccination was higher among the control group selected from Health Unit attenders than among neighbourhood controls.
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