QuestionSBA
QuestionChoice
This value corresponds to the negative predictive value - the chance that the patient does not have the condition if the test is negative. This is calculated as the true negative/(true negative + false negative) = 640/(640+10) = 98.5%
33046
d
98.5%
QuestionChoice
This value is the positive predictive value. Positive predictive value is the chance that the patient has the condition if the test is positive. This corresponds to true positive/(true positive + false positive) = 280/(280+70) = 80%
33045
c
80%
QuestionChoice
The specificity is the proportion of patients without the condition with a negative test result. This is calculated as the true negative/(true negative + false positive) = 640/(640+70) = 90.1%
33047
e
69.6%
QuestionChoice
Splitting the table up into true positive (280), false positive (70), false negative (10), and true negative (640) can make answering these tables easier to understand. The specificity is the proportion of patients without the condition with a negative test result. This is calculated as the true negative/(true negative + false positive) = 640/(640+70) = 90.1%
33043
a
90.1%
QuestionChoice
This value is the sensitivity. The sensitivity is the proportion of patients with the condition who have a positive test result. This is calculated as the true positive/(true positive + false negative) = 280/(280+10) = 96.6%
33044
b
96.6%
Concept
Chapter
# Overview 

Several key parameters can be calculated to help describe how effective a certain test is at diagnosing a condition.

|                    | Patients with colon cancer | No cancer |
| ------------------ | :------------------------: | --------: |
| Biomarker positive |             a              |         b |
| Biomarker negative |             c              |         d |

# Sensitivity

Sensitivity is the proportion of people with the condition who will have a positive result. If, for example, the sensitivity of a test is 80%, out of a hundred people with the condition, only 80 will have a positive test result.

Sensitivity = a/(a+c)

# Specificity

Specificity describes the proportion of people without the disease who will have a negative test. A specificity of 90% can be interpreted as, out of 100 people without the disease, 90 will have a negative test result.

Specificity = d/(b+d)

# Positive Predictive Value

Positive predictive value (PPV) is the proportion of people with a positive test who actually have the disease. PPV varies with prevalence of disease in a population. The lower the prevalence, the lower the positive predictive value. A PPV of 78% will mean that if 100 people tested positive, 78 people will have the disease.

PPV = a/(a+b)

# Negative Predictive Value

Negative predictive value (NPV) is the proportion of people with a negative test who truly do not have the disease.

NPV varies with prevalence of disease in a population. The lower the prevalence, the higher the negative predictive value. An NPV of 97% will mean that if 100 people tested negative, 97 people will not have the disease.

NPV = d/(c+d)

# False positive rate

The false positive rate is the proportion of those without the condition who will test positive.

False positive rate = 1 - specificity

False negative rate = 1 - d/(b+d)

# False negative rate

The false negative rate is the proportion of those with the condition who will test negative.

False negative rate = 1 - sensitivity

False negative rate = 1 - a/(a+c)

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Evaluation of a diagnostic test
Topic
51
Medical Statistics
6609
Specificity is a measure of a diagnostic test's ability to correctly identify those without the condition (true negatives), calculated as the ratio of true negatives to the total number of individuals without the condition: Specificity = (True Negatives / (True Negatives + False Positives)) × 100.
A novel screening test has been developed to detect breast cancer early in high-risk individuals. It is tested on 1000 individuals with risk factors for breast cancer:

|                   | Breast cancer present | Breast cancer absent |
| ----------------- | --------------------- | -------------------- |
| **Test positive** | 280                   | 70                   |
| **Test negative** | 10                    | 640                  |

What is the specificity of the new test?
a