1 epidemiological measures i screening for disease

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Epidemiological Measures I

Screening for Disease

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Terminology• ReliabilityReliability ≡ agreementagreement of

ratings/diagnoses, “reproducibility”– Inter-rater reliabilityInter-rater reliability ≡

agreement between two independent raters

– Intra-rater reliabilityIntra-rater reliability ≡ agreement of the same rater with him/herself

• ValidityValidity ≡ ability to discriminate without error

• AccuracyAccuracy ≡ a combination of reliability and validity

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Validity• Compare screening test results to a gold

standard (“definitive diagnosis”)• Each patient is classified as either true positive

(TP), true negative (TN), false positive (FP), or false negative (FN)

Test D+ D− Total

T+ TP FP TP+FP

T− FN TN FN+TN

Total TP+FN FP+TN N

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SensitivityTest D+ D− Total

T+ TP FP TP+FP

T− FN TN FN+TN

Total TP+FN FP+TN N

SEN ≡ proportion of cases that test positive or the proportion of those with the disease of interest

that our test is able to detect.

Sensitivity

• Sensitivity = TP/TP+FN• Sensitivity =Test positive/Those with

disease

• SnOUT; A very sensitivity test is useful because it allows us to rule out diseases. If you test negative on a very sensitive test then you are probably disease free.

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Specificity

SPEC ≡ proportion of noncases that test negative

Test D+ D− Total

T+ TP FP TP+FP

T− FN TN FN+TN

Total TP+FN FP+TN N

FPTN

TN

disease w/out those

TNSPEC

Specificity

• sPIN• ;a very specific test enables us to rule in

those with a disease. A positive outcome on a specific test is most likely indicative of disease.

Attributes

• Both sensitivity and specificity are intrinsic attributes of a test.

• They do not change with the disease prevalence.

PREDICTIVE VALUES

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Predictive Value PositiveTest D+ D− Total

T+ TP FP TP+FP

T− FN TN FN+TN

Total TP+FN FP+TN N

PVP ≡ proportion of positive tests that are true cases

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Predictive Value NegativeTest D+ D− Total

T+ TP FP TP+FP

T− FN TN FN+TN

Total TP+FN FP+TN N

PVN ≡ proportion of negative tests that are true non-cases

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Prevalence

• [True] prevalence = (TP + FN) / N

• Apparent prevalence = (TP + FP) / N

Test D+ D− Total

T+ TP FP TP+FP

T− FN TN FN+TN

Total TP+FN FP+TN N

Predictive value attributes

• Both the positive and negative predictive values are affected by prevalence.

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Conditional Probability Notation

• Pr(A|B) ≡ “the probability of A given B”

• For example Pr(T+|D+) ≡ “probability test positive given disease positive” = SENsitivity

• SPEC ≡ Pr(T−|D−)

• PVP = Pr(D+|T+)

• PVN= Pr(D−|T−)

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ExampleLow Prevalence Population

D+ D− Total

T+

T−

Total 1000 1,000,000

Conditions: N = 1,000,000; Prevalence = .001

Prevalence = (those with disease) / N

Therefore:

(Those with disease) = Prevalence × N

= .001× 1,000,000 = 1000

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Example: Low Prevalence Population

D+ D− Total

T+

T−

Total 1000 999,000 1,000,000

Number of non-cases, i.e., TN + FP

1,000,000 – 1,000 = 999,000

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Example: Low Prevalence Population

D+ D− Total

T+ 990

T−

Total 1000

TP = SEN × (those with disease) = 0.99 × 1000 = 990

Assume test SENsitivity = 99% i.e., Test will pick up 99% of those with disease

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Example: Low Prevalence Population

D+ D− Total

T+ 990

T− 10Total 1000

FN = 1000 – 990 = 10

It follows that:

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Example: Low Prevalence Population

D+ D− Total

T+

T− 989,010Total 999,000

TN = SPEC × (those without disease) = 0.99 × 999,000 = 989,010

Suppose test SPECificity = .99i.e., it will correctly identify 99% of the noncases

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Example: Low Prevalence Population

D+ D− Total

T+ 9,990T− 989,010

Total 999,000

FPs = 999,000 – 989,010 = 9,900

It follows that:

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Example: Low Prevalence Population

D+ D− Total

T+ 990 9,990 10,980

T− 10 989,010 989,020

Total 1000 999,000 1,000,000

PVPT = TP / (TP + FP) = 990 / 10,980 = 0.090

Low PVP

It follows that the Predictive Value Positive is :

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Example: Low Prevalence Population

D+ D− Total

T+ 990 9,990 10,980

T− 10 989,010 989,020

Total 1000 999,000 1,000,000

PVNT= TN / (all those who test negative) = 989010 / 989020 = .9999

It follows that the Predictive Value Negative is:

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Example: High prevalence population

D+ D− Total

T+ 99,000 9,000 108,000

T− 1,000 891,000 892,000

Total 100,000 900,000 1,000,000

SEN = 99000 / 100,000 = 0.99

SPEC = 891,000 / 900,000 = 0.99

Prev = 100000 / 1,000,000 = 0.10

Same test parameters but used in population with true prevalence of .10

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Example: High prevalence population

D+ D− Total

T+ 99,000 9,000 108,000

T− 1,000 891,000 892,000

Total 100,000 900,000 1,000,000

PVP = 99,000 / 108,000 = 0.92

PVN = 891,000 / 892,000 = 0.9989

Prevalence = 100000 / 1,000,000 = 0.10

An HIV screening test is used in one million people. Prevalence in population is now 10%. SEN and SPEC are again 99%.

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PVPT and Prevalence• As PREValence

increases PVPT also increases

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Screening Strategy• First stage

high SENS (don’t want to miss cases)

• Second stage high SPEC (sort out false positives from true positives)

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Selecting a Cutoff Point• There is often an overlap in test results

for diseased and non-diseased population

• Sensitivity and specificity are influenced by the chosen cutoff point used to determine positive results

• Example: Immunofluorescence test for HIV based on optical density ratio (next slide)

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Low Cutoff High sensitivity and low specificity

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High Cutoff Low sensitivity and high specificity

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Intermediate Cutoff moderate sensitivity & moderate specificity

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Again ,Snout and Spin• Best strategy is to start with a very

sensitive screening test.

• A negative test on a sensitive test rules out the disease

• You then proceed to rule in or confirm the disease by doing a test with high specificity.

• A positive test on a specific test rules in or confirms the diagnosis.