QuestionSBA
QuestionChoice
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) = 600/(600+60) = 90.9%
33062
e
67.4%
QuestionChoice
This value corresponds to the specificity - the proportion of patients without the condition with a negative test result. This is calculated as the true negative/(true negative + false positive) = 290 / (290+60) =82.9%
33060
c
82.9%
QuestionChoice
Splitting the table up into true positive (600), false positive (60), false negative (50), and true negative (290) can make answering these tables easier to understand. 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) = 600/(600+60) = 90.9%
33058
a
90.9%
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) = 290/(290+50) = 85.3%
33061
d
85.3%
QuestionChoice
This value corresponds to sensitivity - the proportion of patients with the condition who have a positive test result. This is calculated as the true positive/(true positive + false negative) = 600/(600+50) = 92.3%
33059
b
92.3%
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
6612
Positive predictive value (PPV) is the probability that individuals with a positive test result truly have the condition, calculated as the ratio of true positives to the total number of positive test results (true positives + false positives).
A new blood test to help diagnose acute myocardial infarction (MI) has been developed. It is tested on 1000 individuals with suspected myocardial infarction in the emergency department :

|                   | MI present | MI absent |
| ----------------- | ---------- | --------- |
| **Test positive** | 600        | 60        |
| **Test negative** | 50         | 290       |

What is the positive predictive value of the new test?
a