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Clinical Laboratory Sciences
Epidemiology (Lecture 17) Screening tests
Terms in this set (44)
Info on screening for disease
• Early identification of people at a point when there is still time to prevent the consequences of a disease
• Screening tests identify affected people BEFORE they have signs or symptoms of the disease
Types of screening programs
Application of screening test to total population regardless of risk status
- Example: Newborn blood spots for sickle cell disease
Application of screening test to high risk groups
- Example: Mammograms for breast cancer in women over 50
Three main requirements of when to screen for disease
1) The disease is an important cause of morbidity (ill health) or mortality (death)
2) Need a proven, acceptable screening test
3) Must have an effective, acceptable treatment or intervention for disease
What makes a test a proven, acceptable test
• Simplicity - easy to perform?
• Time commitment - how long does test take?
• Cost - inexpensive?
• Safety - serious side effects?
• Acceptability-tolerableto population?
• Reliability - same results if repeat test?
• Validity - can the test accurately discriminate between people with and without the disease?
How do we determine accuracy of test?
• Does the test correctly discriminate between patients who truly have and do not have the target condition.
• A test's accuracy is determined by comparing the results from the screening test ("Index test") to the results obtained from a more definitive test ("gold standard").
What is a gold standard
"Gold Standard" = the best, clinically accepted, error-free procedure available to diagnose the condition.
Tells us whether or not the patient truly has the disease
What is used as a gold standard?
• Most definitive diagnostic procedure
- e.g. microscopic examination of a tissue specimen for cancer
• Best available laboratory test
- e.g. polymerase chain reaction (PCR) for HIV virus
• Comprehensive clinical evaluation
- e.g. developmental
evaluation for autism
What do diagnostic accuracy studies do?
Compares the results from the screening test ("Index test") to the results obtained from a more definitive test ("gold standard").
Design of diagnostic accuracy study?
Take sample population
Collect measurements on index test and diagnostic test in sample at the same time
Determine if results of each test are positive or negative
Like cross sectional because measurements for both tests are done at the same time
Design of diagnostic accuracy study
• All patients are given both the gold standard test AND the index test.
• Remember: the "Gold Standard" test indicates whether or not patient truly has the disease.
Patients divided into 4 groups
What are the 4 groups of the diagnostic accuracy study
- Positive on both tests (GOOD)
- Positive on gold standard, negative on index test (BAD - MISTAKE)
- Negative on both tests (GOOD)
- Negative on gold standard, positive on index test (BAD -MISTAKE)
Terminology of diagnostic accuracy study
• Specific terms are used to describe the four possible results of a screening test, compared to the gold standard. The test result may be a:
- True positive
- False positive
- True negative
- False negative
*True/false deals with whether or not they really have the condition
*+/- deals with whether the test was positive or negative
Slide 20 has a graph showing how these occur
Definition of true positive test
• Person with the condition (positive by the gold standard) and positive by the screening test
Definition of false positive test
• Person without the condition (gold standard is negative) but they have a positive screening test
Definition of true negative test
• Person without the condition (gold standard is negative) and a negative screening test
Definition of false negative test
• Person with the condition (gold standard positive) and a negative screening test
Contingency tables of diagnostic accuracy study
Index test result +/- instead of exposure
True disease status on top
Instead of calculating a prevalence ratio, how do we measure accuracy (or validity) of an index test
Measured using four measurements
- Positive Predictive Value
- Negative Predictive Value
• A good test needs to be high on all four measures.
• The ability to detect a disease that is present.
• A person with a more sensitive nose will smell spoiled milk. A person with a less sensitive nose will not detect it and will only find out when it is too late.
Caution on sensitivity
• Sensitivity alone is not adequate for judging whether a test is accurate.
• EX: New screening test for "being male"
• The ability to give a negative result when disease is not present.
Sensitivity and specificity
• The near-sighted biologist will not mistake a cow for a deer. But there may be some deer he does not see. He is like a specific test that is not very sensitive.
• The deer hunter may see and shoot all the deer, but also mistakenly shoot a few cows. He is like a sensitive test that is not very specific.
Sensitivity and specificity and a metal detector
• Suppose there are 20 coins and 10 stones in a field.
• A very sensitive metal detector will find all 20 coins.
• A very specific metal detector will not beep at all for stones.
• If the metal detector finds only 10 of the 20 coins, it is NOT sensitive. That is, it misses a lot of coins.
• If it beeps for some of the stones, it is NOT specific. That is, it is identifying some stones as coins, even though they are not coins.
Definitions of sensitivity and specificity
• Sensitivity: the proportion of those WITH the disease who have a positive screening test.
• Specificity: the proportion of those WITHOUT the disease who have a negative screening test.
Measures test performance in people WITH disease
Sensitivity = (true positive)/(true positive + false negative)
*Denominator is all people with the disease
ALWAYS expressed as a percent
Measures test performance in people WITHOUT disease
Specificity = (true negative)/(true negative + false positive)
*Denominator is all people without the disease
ALWAYS expressed as a percent
Calculating sensitivity and specificity
Sensitivity = true positive/all cases = a/(a+c)
Specificity = true negatives/all non-cases = d/(b+d)
What does poor sensitivity mean (why is sensitivity important)
Many people with the disease are missed. They don't receive treatment and therefore suffer illness and death
What does poor specificity mean (why is specificity important)
Many people without disease are mistakenly told they have it. They suffer anxiety and worry and may undergo painful costly tests, like biopsies, to rule out disease
What is the predictive value(s)
• Positive Predictive Value (PPV): how well a positive test predicts that the condition is present
• Negative predictive value (NPV): how well a negative test predicts that the condition is NOT present.
Conceptually understanding positive predictive value
How likely is it that a beep indicates a coin
• Suppose the metal detector beeps 10 times.
• If all 10 beeps are coins, the test has a high PPV.
• If only 4 of the beeps are coins (the other 6 beeps identify stones), the test has a low PPV. That is, most beeps are NOT coins.
Conceptually understanding negative predictive value
The hunter asks the biologist to screen the animals to identify animals that are NOT deer ('cows').
All of the animals the biologist labels as cows (not deer) are in fact cows. He has high NPV.
Conceptually understanding negative predictive value with metal detector
how likely that no beep indicates no coin
• Suppose the detector does NOT beep for any of the 10 stones (true negatives) but it also does not beep for 5 coins (false negatives) = low NPV
- Only two-thirds (67%) of the 'negative' tests (no beeps) were actually true negatives (stones). The rest were false negatives (coins)
Calculating positive predictive value
Measures test performance in people with POSITIVE tests
PPV = true positive/all positive (true positive + false positive)
ALWAYS expressed as a percent
Calculating negative predictive value
Measures test performance in people with NEGATIVE tests
NPV=true negative/all negatives (true negative + false negative)
ALWAYS expressed as a percent
Definitions of PPV and NPV
• Positive predictive value: proportion of people with a positive test who actually have the disease ('true positives')
• Negative predictive value: proportion of people with a negative test who are actually free of disease ('true negatives')
Why is PPV important
If PPV is low, that means most people who test positive don't truly have the disease.
They will have unnecessary worry. You will waste resources evaluating all those people to see if they really have the disease.
Why is NPV important
• If NPV is low, that means many people who test negative actually have the disease.
They are falsely reassured and don't get preventive measures to avoid illness and death.
Contingency table and PPV & NPV
PPV = true positive/all test positive = a/(a+b)
NPV = true negatives/all test negative = d/(c+d)
Doing all 4 calculations
PPV and NPV go with the rows
Specificity and sensitivity go with the columns
Comparing all tests
Sensitivity looks at having the disease in relation to a positive test
Specificity looks at not having the disease in relation to a negative test
PPV looks at having a positive test in relation to truly having the disease
NPV looks at having a negative test in relation to truly having a disease
Helpful to look at contingency table!
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