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Lead time bias happens when survival time appears longer because diagnosis was done earlier (for instance, by screening), irrespective of whether the patient lived longer. Lead time is the duration of time between the detection of a disease (by screening or based on new experimental criteria) and its usual clinical presentation and diagnosis ...
Length time bias in cancer screening. Screening appears to lead to better survival even when actually no one lived any longer. Length time bias (or length bias) is an overestimation of survival duration due to the relative excess of cases detected that are asymptomatically slowly progressing, while fast progressing cases are detected after giving symptoms.
Selection bias may also make a test look better than it really is. If a test is more available to young and healthy people (for instance if people have to travel a long distance to get checked) then fewer people in the screening population will have negative outcomes than for a random sample, and the test will seem to make a positive difference.
For more information, see Screening (medicine)#Length time bias. This long-standing model has a hidden assumption: namely, that all cancers inevitably progress. But some pre-clinical cancers will not progress to cause problems for patients. And if screening (or testing for some other reason) detects these cancers, overdiagnosis has occurred.
If the cancer screening does not change the treatment outcome, the screening only prolongs the time the individual lived with the knowledge of their cancer diagnosis. This phenomenon is called lead-time bias. [14] A useful screening program reduces the number of years of potential life lost and disability-adjusted life years lost. However ...
Examples of sampling bias include self-selection, pre-screening of trial participants, discounting trial subjects/tests that did not run to completion and migration bias by excluding subjects who have recently moved into or out of the study area, length-time bias, where slowly developing disease with better prognosis is detected, and lead time ...
The healthy user bias or healthy worker bias is a bias that can damage the validity of epidemiologic studies testing the efficacy of particular therapies or interventions. Specifically, it is a sampling bias or selection bias : the kind of subjects that take up an intervention, including by enrolling in a clinical trial , are not representative ...
In educational measurement, bias is defined as "Systematic errors in test content, test administration, and/or scoring procedures that can cause some test takers to get either lower or higher scores than their true ability would merit." [16] The source of the bias is irrelevant to the trait the test is intended to measure.