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===Descriptive studies===
===Descriptive studies===
Errors in measurement of the outcomes of interest (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. For example, 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 can result in errors in the measurement of the outcome of interest in the population (although it should be noted that techniques are available to adjust for imperfect test sensitivity and specificity, if the values of these test characteristics can be estimated).
===Analytic studies===
===Analytic studies===
As with selection bias, biases introduced into analytic studies differ form those in descriptive studies to some degree due to the aims of the study, although the same concepts are present.
====Non-differental information bias====
====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 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).
Many of the problems of information bias can be reduced through the use of '''blinding'''.
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]]