Test Accuracy Formulas

A diagnostic test has a sensitivity of 90% and a specificity of 80%. What is the positive likelihood ratio?

In a population, a new diagnostic test identifies 95 out of 100 people with a disease as positive (True Positive). However, it also wrongly identifies 5 out of 100 healthy people as positive (False Positive). Which calculation determines the Positive Predictive Value (PPV) of this test?

A certain test has a specificity of 90%. What does this tell us about the false positive rate?

If a diagnostic test has a sensitivity of 95%, what is the false negative rate?

If a test has a high negative predictive value, what does this indicate about the population being tested?

In a diagnostic test, what does a high positive likelihood ratio suggest?

A study evaluates a new diagnostic test for a rare disease. The test has a sensitivity of 90% and a specificity of 95%. What is the most likely reason for using this test in a population with very low prevalence of the disease?

What does the 'prevalence' of a disease measure in a population?

If the overall inaccuracy of a test is calculated as 0.15, what is the overall accuracy?

The false positive error rate is given by the equation $FP/(FP+TN)$. Which of the following represents an equivalent expression?

What does the acronym 'TP' stand for in the context of diagnostic testing?

Which of the following formulas represents specificity?

A diagnostic test has a high number of false negatives. Which of the following would be directly affected by this?

If a test has a sensitivity of 90% and a specificity of 90%, how does its negative predictive value change with increasing disease prevalence?

The positive likelihood ratio is calculated using which of the following?

Which of the following is the formula for Negative Likelihood Ratio?

What does a higher negative likelihood ratio (approaching infinity) suggest about a diagnostic test?

In the formula for overall accuracy, what do TN and TP represent, respectively?

How is overall inaccuracy calculated?

What is the relationship between the false positive rate and specificity?

If a diagnostic test has a specificity of 95%, what is its false positive error rate?

Which of the following represents the formula for the false negative error rate?

How is the false negative rate related to sensitivity?

If a diagnostic test has a sensitivity of 85%, what is its false negative error rate?

In the context of diagnostic testing, what does 'prevalence' refer to?

Which formula correctly calculates prevalence?

A disease has a prevalence of 10% in a population. If a diagnostic test identifies 80 true positives and 10 false negatives, what other information is needed to calculate the positive predictive value?

If a test has a very high specificity, what is the likely impact on the test's negative predictive value (NPV)?

Given a test with 90% sensitivity and 80% specificity, how would increasing the cut-off value for a positive result impact these values?

A new screening test is introduced for a rare disease. Which measure is most crucial for determining the test's effectiveness in a population?

A clinic is evaluating a new test for a disease with low prevalence. What should be prioritized when assessing the usefulness of this test?

A study finds that a new diagnostic test has a very high sensitivity but a low specificity. What is a likely consequence of using this test widely?

A new test has a sensitivity of 95% and specificity of 60%. If used on a general population, what would be the main concern?

If a diagnostic test is intended to 'rule out' a disease, which characteristic is most important?

For a disease with very low prevalence, which would be the most important goal when designing a screening program?

What is the relationship between a test's sensitivity and the false negative rate?

Suppose a new, inexpensive test for a rare disease has only moderate sensitivity and specificity. In what scenario might it still be considered valuable?

A hospital implements a new diagnostic test for a common ailment. Initially, the test shows high sensitivity and specificity in trials. However, post-implementation, clinicians notice an unexpectedly high number of false positives. What is the MOST likely explanation?

Two tests are available for a disease: Test A (95% sensitivity, 70% specificity) and Test B (70% sensitivity, 95% specificity). If the primary goal is to ensure that nearly all affected individuals are identified in an initial screening, which test is more appropriate?