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'''). 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==

==Validity==

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>

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===

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-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]].  

===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.

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]]

[[Category:Veterinary Epidemiology - General Concepts|I]]