Diagnostic Decision Logic - Eppp Knowledge

1. Which decision goal prioritizes specificity: screening or confirmatory diagnosis?

   Confirmatory diagnosis prioritizes specificity. Confirmatory diagnosis seeks to accurately identify true cases, minimizing false positives to prevent unnecessary interventions.

2. Compute sensitivity if $TP=80$ and $FN=20$.

   $\text{Sensitivity}=\frac{80}{80+20}=0.80$. The calculation applies the sensitivity formula, dividing true positives by the sum of true positives and false negatives.

3. What is the formula for false positive rate (FPR) using $FP, TN$?

   $\text{FPR}=\frac{FP}{FP+TN}=1-\text{Specificity}$. False positive rate is the proportion of actual negatives incorrectly identified as positive, equivalent to one minus specificity.

4. What cut score change increases sensitivity but decreases specificity (direction only)?

   Lowering the cut score. Lowering the threshold classifies more cases as positive, capturing more true positives but also increasing false positives.

5. What is the formula for sensitivity (true positive rate) using $TP, FN$?

   $\text{Sensitivity}=\frac{TP}{TP+FN}$. Sensitivity quantifies the test's ability to correctly identify those with the condition as the ratio of true positives to all actual positives.

6. What is the formula for positive predictive value (PPV) using $TP, FP$?

   $\text{PPV}=\frac{TP}{TP+FP}$. Positive predictive value represents the probability that a positive test result is correct, calculated as true positives over all positive results.

7. Identify the best metric for ruling OUT a condition when test is negative (high value desired).

   High sensitivity (few false negatives). High sensitivity ensures that a negative test result is likely true, minimizing false negatives for confident ruling out of the condition.

8. Compute PPV if $TP=30$ and $FP=10$.

   $\text{PPV}=\frac{30}{30+10}=0.75$. The calculation applies the PPV formula, dividing true positives by the sum of true positives and false positives.

9. What is the formula for negative predictive value (NPV) using $TN, FN$?

   $\text{NPV}=\frac{TN}{TN+FN}$. Negative predictive value represents the probability that a negative test result is correct, calculated as true negatives over all negative results.

10. If a test has specificity $0.92$, what is its false positive rate?

    $\text{FPR}=1-0.92=0.08$. False positive rate is derived as the complement of specificity, representing the proportion of incorrect positive classifications.

11. Compute accuracy if $TP=50, TN=130, FP=20, FN=10$.

    $\text{Accuracy}=\frac{50+130}{50+130+20+10}=\frac{180}{210}\approx^0.86$. The calculation sums correct classifications and divides by total cases to determine the proportion of accurate test results.

12. What is the formula for false negative rate (FNR) using $FN, TP$?

    $\text{FNR}=\frac{FN}{FN+TP}=1-\text{Sensitivity}$. False negative rate is the proportion of actual positives incorrectly identified as negative, equivalent to one minus sensitivity.

13. Identify the best metric for ruling IN a condition when test is positive (high value desired).

    High specificity (few false positives). High specificity ensures that a positive test result is likely true, minimizing false positives for confident ruling in of the condition.

14. Compute specificity if $TN=90$ and $FP=10$.

    $\text{Specificity}=\frac{90}{90+10}=0.90$. The calculation applies the specificity formula, dividing true negatives by the sum of true negatives and false positives.

15. If a test has sensitivity $0.90$, what is its false negative rate?

    $\text{FNR}=1-0.90=0.10$. False negative rate is derived as the complement of sensitivity, representing the proportion of missed positive cases.

16. Compute NPV if $TN=160$ and $FN=40$.

    $\text{NPV}=\frac{160}{160+40}=0.80$. The calculation applies the NPV formula, dividing true negatives by the sum of true negatives and false negatives.

17. Which decision goal prioritizes sensitivity: screening or confirmatory diagnosis?

    Screening prioritizes sensitivity. Screening aims to detect as many potential cases as possible, minimizing false negatives to avoid missing affected individuals.

18. What is the formula for specificity (true negative rate) using $TN, FP$?

    $\text{Specificity}=\frac{TN}{TN+FP}$. Specificity quantifies the test's ability to correctly identify those without the condition as the ratio of true negatives to all actual negatives.

19. Which metric depends most directly on prevalence: sensitivity/specificity or PPV/NPV?

    PPV and NPV depend on prevalence. Predictive values incorporate prevalence, unlike sensitivity and specificity which are intrinsic test properties independent of base rates.

20. What cut score change increases specificity but decreases sensitivity (direction only)?

    Raising the cut score. Raising the threshold classifies fewer cases as positive, reducing false positives but also missing more true positives.

21. What is the formula for prevalence (base rate) using $TP, FN, N$?

    $\text{Prevalence}=\frac{TP+FN}{N}$. Prevalence indicates the base rate of the condition in the population as the ratio of actual positives to the total sample size.

22. What is the formula for overall accuracy using $TP, TN, FP, FN$?

    $\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}$. Accuracy measures the overall correctness of the test as the proportion of true results (positive and negative) to the total population.

23. Compute prevalence if $TP=25$, $FN=75$, and $N=500$.

    $\text{Prevalence}=\frac{25+75}{500}=0.20$. The calculation divides actual positives by total population to estimate the condition's base rate in the sample.

24. When prevalence increases (test unchanged), what happens to NPV?

    NPV decreases. Higher prevalence decreases the proportion of true negatives among negative test results, thus reducing negative predictive value.

25. When prevalence increases (test unchanged), what happens to PPV?

    PPV increases. Higher prevalence increases the proportion of true positives among positive test results, thus elevating positive predictive value.

Study these flashcards