Changes

'''Systematic error''', or 'bias' is of particular importance in any epidemiological investigation, and should be avoided wherever possible. Biases will reduce the '''validity''' of any results obtained, whether it be by overestimating or underestimating the frequency of disease in a population or the association between an exposure and disease. The forms of bias covered here can only be minimised through careful study design and execution - they cannot be accounted for in the analysis. Although [[confounding]] is considered by many authors as a form of bias, it can be accounted for during analysis, and so is covered separately.<br>

'''Systematic error''', or 'bias' is of particular importance in any epidemiological investigation, and should be avoided wherever possible. Biases will reduce the '''validity''' of any results obtained, whether it be by overestimating or underestimating the frequency of disease in a population or the association between an exposure and disease. The forms of bias covered here can only be minimised through careful study design and execution - they cannot be accounted for in the analysis. Although [[confounding]] is considered by many authors as a form of bias, it can be accounted for during analysis, and so is covered separately.<br>

Bias can be introduced into a study through the selection of participants ('''selection bias'''), or through errors made in the classification of measurement of exposures/outcomes of interest ('''information bias''').  

Bias can be introduced into a study through the selection of participants ('''selection bias'''), or through errors made in the classification of measurement of exposures/outcomes of interest ('''information bias'''). Many of the examples given here relate to [[Study design|observational studies]] rather than [[Study design|experimental studies]], as the process of randomisation of treatment in experimental studies is intended to minimise biases. However, it should be noted that even experimental studies may be affected by bias (for example, through failure of participants in the treatment arm of a randomised controlled trial to continue to take their allocated medication).

==Validity==

==Validity==

===Analytic studies===

===Analytic studies===

Sources of selection bias which are applicable to all analytic studies are:

Sources of selection bias which are applicable to all analytic studies are:

* non-response bias (if the association between exposure and outcome differs between responders and non-responders)

* non-response bias (if the association between exposure and outcome differs between responders and non-responders)

* inappropriate selection of comparison groups (both groups should be from the same source population)

* inappropriate selection of comparison groups (both groups should be from the same source population)

However, individual study designs may be associated with other sources of selection bias:

Some examples of additional types of selection bias which may affect particular study designs are described below. Note that case-control studies are not mentioned here as the predominant source of selection bias in these cases is non-response bias.

====Case-control studies====

====Case-control studies====

Case-control studies are particularly susceptible to selection bias. A major source of selection bias in these studies is through the selection of the '''control group'''. As mentioned above, it is of vital importance that the control group comes from the same population as the case group (that is, if they happened to experience the outcome of interest during the study period, they would have been classified as a case instead of a control). ''Admission risk bias'' is a type of selection bias associated with hospital-based studies where cases and controls are selected from hospitals (or similar establishments), and so may have a different exposure profile to the general population (to which the results are generally intended to be extrapolated). However, selection of controls from other sources can also introduce biases. Detection bias is a form of selection bias which results from differential classification of disease status according to exposure status (and so is closely related to '''information bias''').

Case-control studies are particularly susceptible to selection bias. A major source of selection bias in these studies is through the selection of the '''control group'''. As mentioned above, it is of vital importance that the control group comes from the same population as the case group (that is, if they happened to experience the outcome of interest during the study period, they would have been classified as a case instead of a control). ''Admission risk bias'' is a type of selection bias associated with hospital-based studies where cases and controls are selected from hospitals (or similar establishments), and so may have a different exposure profile to the general population (to which the results are generally intended to be extrapolated). However, selection of controls from other sources can also introduce biases (for example, these controls may not be members of the same population as the hospital-based cases). ''Detection bias'' is a form of selection bias which results from differential classification of disease status according to exposure status (and so is closely related to '''information bias''').

====Cohort studies====

====Cohort studies====

Selection bias in cohort studies is generally less likely than in case-control studies, as selection for participation in the study generally precedes the development of the outcome of interest. However, non-response may result in some biases, and losses to follow-up during the study may also introduce selection bias. For example, a study may be conducted in order to investigate the effect of farm biosecurity on introduction of disease. It is plausible that farmers with poor biosecurity will be less likely to remain in the study, meaning that these farms individuals (which may be more likely to experience disease problems) will be lost from the study.

Selection bias in cohort studies is generally less likely than in case-control studies, as selection for participation in the study generally precedes the development of the outcome of interest. However ''losses to follow-up'' during the study can introduce selection bias. For example, a study may be conducted in order to investigate the effect of farm biosecurity on introduction of disease. It is plausible that farmers with poor biosecurity will be less likely to remain in the study, meaning that these farms individuals (which may be more likely to experience disease problems) will be lost from the study. Another type of selection bias may be seen in 'occupational' cohort studies in human epidemiology. In these studies, members of a particular occupation (who are easier to identify and recruit) are followed up over time and monitored for disease and death. However, these people are from a distinct subpopulation of 'healthy workers', and so may not be comparable to members of the general population. This 'healthy worker effect' is also seen in veterinary epidemiology - as distinct selection processes are in place in any commercial industry (whether it be a farm or a horse racing yard) in order to remove less healthy animals (both through culling and through death).

==Information bias==

==Information bias==

Information bias results from errors in measurement or classification of exposures or outcomes of interest amongst the individuals included in the study. In the case of analytic studies, this may be classified as '''differential''' or '''non-differential''', depending on whether the likelihood of bias is affected by an individual's exposure to factors of interest, or experience of the outcome of interest.

Information bias results from errors in exposure/outcome measurement or classification amongst the individuals included in the study. In the case of analytic studies, this may be further classified as '''differential''' or '''non-differential''', depending on whether the bias is associated with an individual's exposure to factors of interest, or experience of the outcome of interest.<br>

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===Non-differental information bias/Measurement error===

===Non-differental information bias===

Non-differential bias occurs when the chance of bias is not affected by the group the individuals belong to. This type of bias in analytic studies will tend to reduce the strength of any association present, and will increase the probability of a [[Random variation#Hypothesis testing and study power|type II error]]. Errors in measurement (known as '''measurement error''' in the case of continuous variables, and '''misclassification bias''' in the case of binary or categorical variables) is a common example of non-differential bias - for example, if scales are not correctly calibrated, they will incorrectly record the weight of all animals weighed, regardless of their 'exposure' status.  

Non-differential bias occurs when the chance of bias is not affected by the group the individuals belong to. This type of bias in analytic studies will tend to reduce the strength of any association present, and will increase the probability of a [[Random variation#Hypothesis testing and study power|type II error]]. Errors in measurement (known as '''measurement error''' in the case of continuous variables, and '''misclassification bias''' in the case of binary or categorical variables) is a common example of non-differential bias - for example, if scales are not correctly calibrated, they will incorrectly record the weight of all animals weighed, regardless of their 'exposure' status.