Sensitivity, Specificity, and Predictive Values Flashcards
Sensitivity and specificity are independent of . . .
. . . prevalence of disease and patient pre-test probability.
Positive predictive value
True +
_________
True + and False +
Negative predictive value
True -
________________
False - and True -
Post-test probability negative
Likelihood of having disease if I have a negative test
1 - NPV
Predictive values vary depending on . . .
. . . prevalence within a given population at a given time
Likelihood ratio
LR = Probability of patient with a condition having a test result
____________________________________________
Probability of patient without condition having test result
To rule something out, you want a test with a ____.
To rule something out, you want a test with a low negative likelihood ratio.
To rule something in, you want a test with a ___.
To rule something in, you want a test with a high positive likelihood ratio.
Setup of Fegan’s nomogram
Left: Pre-test probability
Middle: Likelihood ratio
Right: Post-test probability
Calculating odds from probability
Odds = p / (1 - p)
Calculating probability from odds
p = odds / ( 1 + odds)
Test reference standard or gold standard
Test to which potential screening or diagnostic tests are compared to calculate sensitivity and specificity
Threshold probability
The probability above or below which you will take action (e.g., order an additional test, prescribe a treatment)
Receiver Operating Characteristic (ROC) Curves
An ROC curve is a plot of the true positive rate (sensitivity) against the false positive rate (1-specificity; recall that specificity reflects true negatives) for the possible cutpoints of a diagnostic test. The closer the curve follows the left-hand border and the top border of the ROC space, the more accurate the test.
If we change our cutoff to increase sensitivity, then ___. If we change our cutoff to increase specificity, then ___.
If we change our cutoff to increase sensitivity, then specificity goes down. If we change our cutoff to increase specificity, then sensitivity goes down
Lead time bias
People with the disease who get screened and then get treatment survive longer than people with the disease who do not get screened.
You may mistakenly conclude that screening + treatment cause improved survival, when what is really going on is that patients diagnosed earlier have more time to survive, even if treatment isn’t helpful and their actual life expectancy may be no different.
Length bias
Cases of a particular condition that are progressing more slowly will be more prevalent in a screened population than those that are rapidly progressing. So if some patients with the disease spend more time “having it without being sick from it,” they are the ones you’ll find by screening.
In contrast, patients with rapid disease progression spend less time “having it without being sick from it.” That means that patients with positive screening tests appear to have better outcomes, but it is not because screening + treatment is better – instead it’s because the disease in patients detected by screening is different from that in patients who present with symptoms
Which test parameters are stable with changes in disease prevalence?
Sensitivity
Specificity
Likelihood ratio
Can long-term follow-up be used as a gold standard when assessing the validity of a diagnostic test?
Yes!!!
LR+ and LR-
