Epi
Screening Test
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Screening Test
See Also
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
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
)
Test Specificity
(
False Positive Rate
)
U.S. Preventive Services Task Force Recommendations
Definitions
Factors affecting
Positive Predictive Value
Prevalence
of disease
Accuracy of test
Types
Bias
Screening Bias
Lead-Time Bias
Length-Time Bias
Criteria
Ideal Screening Test
Disease Features
Disease significantly impacts public health
Intermediate probability of disease
Detection occurs before a critical point
Critical point occurs before clinical diagnosis
Screened patient is still asymptomatic
Diagnosis would not otherwise occur this early
Endometrial Cancer
's critical point is too late
Critical point occurs in time to affect outcome
Disease must be detected early enough for cure
Lung Cancer
's critical point occurs too early
Test Features
Screening Test tolerated by patients
High
Test Sensitivity
to detect asymptomatic disease
Best quality for Screening Test
Test criterion or threshold is set low to minimize false negatives (at the expense of increased false positives)
High
Test Specificity
Best quality for confirmatory test
Test criterion or threshold is set high to minimize false positives (at the expense of increased false negatives)
Screened Population Features
Disease has high enough
Prevalence
to allow screening
Medical care available if Screening Test positive
Patient willing to undergo further evaluation
Evaluation
Assume you know the disease state
Test Sensitivity
Given the patient has the disease,
What is the probability of a true positive test
Test Specificity
Given the patient does not have the disease,
What is the probability of a true negative test
Probability of test given disease
P(test | disease) =
Test Sensitivity
/
Test Specificity
Where P (A | B) = Probability of A given B
Assume you know the test result
Positive Predictive Value
(PPV)
Given a positive test,
What is the probability that the patient has the disease
Negative Predictive Value
(NPV)
Given a negative test
What is the probability that the patient does not have the disease
Probability of disease given test
P(disease | positive test) = PPV / NPV
P(disease | positive test) via
Bayes Theorem
= P(positive test | disease) * P(disease) / P(positive test)
where P(positive test | disease) =
Test Sensitivity
where P(disease) =
Prevalence
of disease in the tested cohort
where P(positive test) = Probability of positive test in the tested cohort
References
Desai (2014) AMIA Board Review, Clinical Decision Making
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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