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.
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).
Validity
As mentioned earlier, the validity of an estimate of disease occurrence is a measure of how well it can be extrapolated to the source population (or, in the case of external validity, to other populations). The same concept of validity applies to analytic studies, although in these cases it relates to the estimation of the association between an exposure and an outcome of interest. Both selection and information biases act to reduce the validity of an estimate.
Selection bias
Selection bias affects inclusion of individuals in the study and results in the study sample not being representative of the source population. This may occur in the initial selection process, or may be a result of nonresponse or losses to follow-up during the study period. As alluded to above, the process of bias introduction into descriptive analytic studies differs from that in analytic studies, due to different aims in these investigations. In descriptive studies, any process which results in the selection of a population which is not representative of the source population can bias the results of the study. However, in the case of analytic studies, the representation of the source population is less important, as long as the measure of association between the exposure and outcome is not affected. In these cases, if selection is associated with both exposure and outcome (for example, if 'diseased' individuals who were exposed to the factor of interest were underrepresented in the sample), then bias will result. Note, however, that if selection is 'only' associated with 'either' exposure status or outcome (for example, if diseased individuals in general were underrepresented, regardless of whether they were exposed to the factor or not), then bias will not result. These concepts are similar to the concept of differential and non-differential information bias described below.
Descriptive studies
In descriptive studies, the precise definition of the source population and the use of an appropriate method of selection of individuals from this is of paramount importance in order to minimise bias. However, even when this is achieved effectively, a lack of compliance amongst selected individuals ('non-response') can introduce considerable bias. Increasing compliance, through the use of reminders or incentives to participate, can help reduce this source of error.
Analytic studies
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)
- 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:
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).
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.
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.
Non-differental information bias/Measurement error
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 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.
Differential information bias
Differential bias occurs when the chance of bias is different for the different groups being compared, and may strengthen or weaken the estimated strength of association in an analytic study. For example, Boxer dogs may be more likely than other dog breeds to be diagnosed as having mast cell tumours (even if they do not have them), due to a postulated breed predisposition.