Epi
Test Specificity
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Test Specificity
, Specificity, False Positive Rate, Diagnostic Specificity, False Positive
See Also
Screening Test
Contingency Grid
or
Cross Tab
(includes
Statistics Example
)
Bayes Theorem
(
Bayesian Statistics
)
Fagan Nomogram
Experimental Error
(
Experimental Bias
)
Lead-Time Bias
Length Bias
Selection Bias
(
Screening Bias
)
Likelihood Ratio
(
Positive Likelihood Ratio
,
Negative Likelihood Ratio
)
Number Needed to Screen
(
Number Needed to Treat
,
Absolute Risk Reduction
,
Relative Risk Reduction
)
Negative Predictive Value
Positive Predictive Value
Pre-Test Odds
or
Post-Test Odds
Receiver Operating Characteristic
Test Sensitivity
(
False Negative Rate
)
U.S. Preventive Services Task Force Recommendations
Definitions
Test Specificity
Screening Test
correctly negative in absence of disease
A test with high Specificity has few False Positives
Independent of disease
Prevalence
in the community
Specific Tests allow user to RULE IN or confirm a condition (mnemonic "SPin")
Calculation
Test Specificity
True negative tests per unaffected patients tested
Expressed as a percentage
Test Specificity = P(negative test | no disease)
Where P (A | B) = Probability of A given B
False Positive Rate
Test positive despite absence of condition
False Positive Rate = 1 - Test Specificity
Example
A new
Screening Test
for
Crohn's Disease
Patients without
Crohn's Disease
tested: 255
Patients without
Crohn's Disease
who have a negative test: 230
Specificity = 230/255 or 90%
Precaution
Test Specificity can be misleading
Example
Condition A is actually present in 150 patients (5%) of the 3000 patients tested
Therefore 2850 patients do not have condition A
Test Specificity of 90% would result in a 10% False Positive Rate (of 2850) or 285 patients
In this case a 90% Test Specificity would result in a False Positive result in 285 patients, when only 150 actually had the condition
Conclusion
The lower the
Prevalence
of disease in the cohort tested, the higher the Test Specificity must be to give a reasonable likelihood of correctness
Positive Predictive Value
may be a more valuable measure as it takes the condition
Prevalence
into account
Risk stratifying a group prior to testing can concentrate patients more likely to be positive without missing a significant number
Example: Limit
D-Dimer
testing to the intermediate likelihood of
Pulmonary Embolism
group (based on Wells Score)
This increases the
Prevalence
in the tested group and reduces the number of patients with False Positive results
References
Hennekens (1987) Epidemiology Medicine, p.327-47
Majoewsky (2012) EM:RAP 12(1): 9-11
Gates (2001) Am Fam Physician 63(3):513-22 [PubMed]
MacLean (1996) Med Clin North Am 80(1):1-14 [PubMed]
Nielsen (1999) Med Clin North Am 83(6):1323-37 [PubMed]
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