![]() Random error increases variation and decreases the ability to detect a difference between groups – if a difference exists. The reliability of a test, that is, the extent that the same test yields the same value or very close value on repeated testing, is affected by the extent of random error. Source: Adapted, with permission, from Atkins 1 (2000). A well-designed and well-implemented study protocol will avoid systematic errors by (1) setting inclusionary and exclusionary criteria that result in unbiased selection and follow-up of study participants (2) making collection, storage, and processing of specimens from all participants as similar as possible and (3) arranging laboratory procedures so that any effects of storage or testing equally impact specimens from cases and controls or exposed and unexposed participants. For example, if values are always higher for cases, it will appear that there is an association with being a case, even if the effect is due solely to systematic error in measurement. This difference can lead to an erroneous association. However the primary concern is if the systematic error occurs differentially between cases and controls or exposed and unexposed individuals. Depending on the direction of the bias, a systematic error can lead to the overestimation or underestimation of the frequency of exposure or disease. Though a systematic error can be corrected post hoc if detected, it is best avoided by regularly calibrating instruments. Systematic error is an error that occurs in one direction, for example, a scale that shows the weight as two grams higher than the true value. The extent that a test result reflects the true value, that is, it is valid, depends on minimizing two major classes of error: systematic error (also known as bias) and random error ( Figure 8.1 Once a test is selected and the validity and reliability determined in the hands of the research team, the continued validity and reliability is assured by quality control and quality assurance procedures (discussed in Chapter 10). The validity and reliability of a test result depend on everything from whether the specimen was collected correctly to whether the results were recorded accurately. ![]() Regardless of the reason for measurement, a test is only useful if it is reliable and valid, and interpreted appropriately. New technologies have identified genetic, transcript, and protein variants whose association with disease is unknown. Alternatively, the measure might detect a marker in search of an association. The test might detect a known or suspected marker of disease prognosis, a known or suspected marker of exposure that modifies disease risk, or one that is known or suspected to interact with a defined exposure disease relationship. The test might be used to distinguish between disease types, identify microbes, or differentiate among microbes of the same species. It might characterize how the host responds to disease or how microbes interact with each other or with the host. The test might be used to confirm or exclude disease, assess disease severity, or identify the precise location of disease. There are many reasons for using a molecular test in an epidemiologic study. This suggests that assessments of reliability must be done over a short time, and that loss over 2-week period could as easily reflect true loss as sampling error. There are some estimates for group B Streptococcus colonization is very dynamic, with an average duration of carriage of ∼14 weeks among women. Also unknown is the average duration of carriage. Currently there are few estimates in the literature of how frequently there is a change in the bacterial strains (or other colonizing microbes) that commonly colonize the human gut, mouth, vaginal cavity, and skin. The dynamics of colonization of human body sites by microbes are essentially unknown. The extent that this impacts the reliability will dictate if the protocol should stipulate timing of specimen collection. ![]() Repeated samples from the same individual will indicate if the measure varies with time of day, menstrual cycle, or consumption of food or liquids. For proper interpretation of study results, further reliability assessment is required to determine the variability from repeated samples from the same individual, and variation among individuals.
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