Spectrum bias
Encyclopedia
Initially identified in 1978, spectrum bias refers to the phenomenon that the performance of a diagnostic test may change between different clinical settings owing to changes in the patient case-mix thereby affecting the transferability of study results in clinical practice. In the statistical sense it is not truly a bias and has led some authors to refer to the effect(s) as 'spectrum effects', whilst others maintain it is bias if the true performance of the test differs from that which is 'expected'. Usually the performance of a diagnostic test is measured in terms of its sensitivity and specificity and it is changes in these that are considered when referring to spectrum bias. However, other performance measures such as the likelihood ratios may also be affected by spectrum bias.
Generally it is considered to have three causes. The first is due to a change in the case-mix of those patients with the target disorder (disease) and this affects the sensitivity. The second is due to a change in the case-mix of those without the target disorder (disease-free) and this affects the specificity. The third is due to a change in the prevalence, and this affects both the sensitivity and specificity. This final cause is not widely appreciated, but there is mounting empirical evidence as well as theoretical which suggest that it does indeed affect a test's performance.
Examples where the sensitivity and specificity change between different sub-groups of patients may be found with the carcinoembryonic antigen test and urinary dipstick tests.
Diagnostic test performances reported by some studies may be artificially overestimated if it is a case-control design where a healthy population ('fittest of the fit') is compared with a population with advanced disease ('sickest of the sick') .
If properly analyzed, recognition of heterogeneity
of subgroups can lead to insights about the test's performance in varying populations.
Generally it is considered to have three causes. The first is due to a change in the case-mix of those patients with the target disorder (disease) and this affects the sensitivity. The second is due to a change in the case-mix of those without the target disorder (disease-free) and this affects the specificity. The third is due to a change in the prevalence, and this affects both the sensitivity and specificity. This final cause is not widely appreciated, but there is mounting empirical evidence as well as theoretical which suggest that it does indeed affect a test's performance.
Examples where the sensitivity and specificity change between different sub-groups of patients may be found with the carcinoembryonic antigen test and urinary dipstick tests.
Diagnostic test performances reported by some studies may be artificially overestimated if it is a case-control design where a healthy population ('fittest of the fit') is compared with a population with advanced disease ('sickest of the sick') .
If properly analyzed, recognition of heterogeneity
Homogeneity (statistics)
In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets. They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part...
of subgroups can lead to insights about the test's performance in varying populations.