Bias - Revision history

Bara at 14:07, 6 January 2011

2011-01-06T14:07:28Z

Gauche at 15:36, 15 December 2010

2010-12-15T15:36:25Z

Gauche at 15:03, 15 December 2010

2010-12-15T15:03:32Z

Gauche at 14:23, 15 December 2010

2010-12-15T14:23:30Z

Gauche at 14:11, 15 December 2010

2010-12-15T14:11:06Z

Gauche at 13:36, 15 December 2010

2010-12-15T13:36:06Z

Gauche at 12:17, 15 December 2010

2010-12-15T12:17:34Z

Gauche at 11:05, 15 December 2010

2010-12-15T11:05:33Z

Gauche at 10:35, 15 December 2010

2010-12-15T10:35:23Z

Gauche at 10:34, 15 December 2010

2010-12-15T10:34:06Z

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 ====Non-differental information bias====
-Non-differential bias occurs when the chance of bias is not affected by the group the individuals belong to. Measurement error and misclassification bias are common examples of non-differential bias. An example of measurement error may be seen if the scales used in a study are not correctly calibrated, resulting in the incorrect recording of the weight of all animals sampled. As this form of bias affects both exposed and unexposed (or diseased and non-diseased, depending on the study type) similarly, it will reduce the strength of any associations present, and will increase the probability of a [[Random variation#Hypothesis testing and study power|type II error]].
+Non-differential bias occurs when the chance of bias is not affected by the group the individuals belong to. Measurement error and misclassification bias are common examples of non-differential bias. An example of measurement error may be seen if the scales used in a study are not correctly calibrated, resulting in the incorrect recording of the weight of all animals sampled. As this form of bias affects both exposed and unexposed (or diseased and non-diseased, depending on the study type) similarly, it will reduce the strength of any associations present, and will increase the probability of a [[Random error#Hypothesis testing and study power|type II error]].
 ====Differential information bias====

@@ Line 39: / Line 39: @@
 ====Differential information bias====
-Differential bias occurs when the misclassification/measurement error for exposures or outcomes is different for the different groups being compared. This may take place through a number of stages in the investigation - common examples are incorrect results given by the participants, by the investigator or by other people involved in the study. ''Recall bias'' refers to the purposeful misrepresentation of the truth by participants (i.e. lying), whereas ''recall bias'' may be seen in relation to exposure classification for retrospective studies (where participants may have differential recollection of exposures, dependant on whether the outcome of interest was experienced).
+Differential bias occurs when the misclassification/measurement error for exposures or outcomes is different for the different groups being compared. This may take place through a number of stages in the investigation - common examples are incorrect results given by the participants, by the investigator or by other people involved in the study. ''Reporting bias'' refers to the purposeful misrepresentation of the truth by participants (i.e. lying), whereas ''recall bias'' may be seen in relation to exposure classification for retrospective studies (where participants may have differential recollection of exposures, dependant on whether the outcome of interest was experienced or not). ''Observer bias'' may result from differences in the classification of exposures or outcomes by the investigator or person assigning the classification, and can be reduced through the use of ''blinding'' techniques. For example, Boxer dogs may be more likely than other dog breeds to be diagnosed by vets as having mast cell tumours (even if they do not have them), due to a postulated breed predisposition. Differential information bias may lead to either a strengthening or a weakening of the estimated strength of association between exposure and outcome.
 [[Category:Veterinary Epidemiology - General Concepts|I]]

@@ Line 30: / Line 30: @@
 ===Descriptive studies===
 ===Analytic studies===
 ====Non-differental information bias====
-Non-differential bias occurs when the chance of bias is not affected by the group the individuals belong to. Errors in measurement of exposures and outcomes (known as '''measurement error''' in the case of continuous variables, and '''misclassification bias''' in the case of binary or categorical variables) are a common example of non-differential bias. An example of measurement error may be seen if the scales used in a study are not correctly calibrated, resulting in the incorrect recording of the weight of all animals sampled. Similarly, an example of misclassification bias may be seen if a test with imperfect [[Evaluation of diagnostic tests|sensitivity]] is used in a study, resulting in some diseased animals being incorrectly classified as non-diseased. This bias affects both exposed and unexposed (or diseased and non-diseased, depending on the study type) similarly, and 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]].
+Non-differential bias occurs when the chance of bias is not affected by the group the individuals belong to. Measurement error and misclassification bias are common examples of non-differential bias. An example of measurement error may be seen if the scales used in a study are not correctly calibrated, resulting in the incorrect recording of the weight of all animals sampled. As this form of bias affects both exposed and unexposed (or diseased and non-diseased, depending on the study type) similarly, it will reduce the strength of any associations present, and will increase the probability of a [[Random variation#Hypothesis testing and study power|type II error]].
 ====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.
+Differential bias occurs when the misclassification/measurement error for exposures or outcomes is different for the different groups being compared. This may take place through a number of stages in the investigation - common examples are incorrect results given by the participants, by the investigator or by other people involved in the study. ''Recall bias'' refers to the purposeful misrepresentation of the truth by participants (i.e. lying), whereas ''recall bias'' may be seen in relation to exposure classification for retrospective studies (where participants may have differential recollection of exposures, dependant on whether the outcome of interest was experienced).
 [[Category:Veterinary Epidemiology - General Concepts|I]]

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 '''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'''). 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).
+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.
 ==Validity==
@@ Line 29: / Line 29: @@
 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>
-===Non-differental information bias===
+===Descriptive studies===
-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.
-===Differential information bias===
+====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.
 [[Category:Veterinary Epidemiology - General Concepts|I]]

@@ Line 1: / Line 1: @@
 '''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==
@@ Line 14: / Line 14: @@
 ===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:
+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 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====
-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 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>
-===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.

@@ Line 1: / Line 1: @@
-'''Systematic error''', or 'bias' is of particular importance in any epidemiological investigation, and should be avoided wherever possible. As with random error, biases will reduce the '''accuracy''' of any results obtained, but they also have the potential to reduce the '''validity''' of results. 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'''). These biases may also 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.
 ==Validity==
-As mentioned [[Sampling strategies#Populations and samples|earlier]], the '''validity''' of an estimate is a measure of how well it can be extrapolated to the population in question (or, in the case of ''external validity'', to other populations). Therefore, both selection and information biases will tend to reduce the validity of an estimate, which is of particular concern for any epidemiological study.
+As mentioned [[Sampling strategies#Populations and samples|earlier]], the '''validity''' of an estimate is a measure of how well it can be extrapolated to the population in question (or, in the case of ''external validity'', to other populations). The same concept applies to analytic studies, although the validity here relates to the ability to estimate the association between an exposure and an outcome of interest. Both selection and information biases can reduce the validity of an estimate.<br>
 ==Selection bias==
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 ===Cross sectional studies===
-Although non-response is also the main route of selection bias entering these studies, it should be noted here that analytic studies differ from descriptive studies in how bias can occur, due to the aim of the study. In these cases, both exposure and outcome should be considered - if selection is associated with both of these (meaning that selection of 'diseased' individuals is affected by their exposure category), then bias will result. If selection is 'only' associated with 'either' exposure status or outcome, then bias will not result.
+Non-response is also the main route of selection bias entering these studies,
 ===Case-control studies===

@@ Line 9: / Line 9: @@
 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. The mechanisms by which selection bias can enter a study will depend on the [[study design]]:
-===Descriptive or cross sectional studies===
+===Descriptive studies===
 In these studies, lack of compliance ('non-response') may result in selection bias, as participation amongst selected individuals is very rarely 100%. It is for this reason that compliance should be maximised as much as possible, through the use of reminders or incentives to participate.
 ===Case-control studies===