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<tr class="v-firstrow"><td> </td><td>'''Disease'''</td><td>'''No disease''' </td></tr>
<tr class="v-firstrow"><td> </td><td>'''Disease'''</td><td>'''No disease''' </td></tr>
<tr><td>'''Positive test''' </td><td> [[True positive]]</td><td>[[False negative]]</td></tr>
<tr><td>'''Positive test''' </td><td> [[True positive]]</td><td>[[False negative]]</td></tr>
<tr><td>'''Negative test'''</td><td>[[False negative]]</td><td> [[True negative]]</td></tr>
<tr><td>'''Negative test'''</td><td>[[False positive]]</td><td> [[True negative]]</td></tr>


</table>
</table>
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</table>
</table>
|AnswerA=90/100
|AnswerA=90/100
|AnswerAExp=The [[sensitivity]] is the probability of a positive test result when the subjects actually have the disease. The sensitivity can be calculated as follows:
|AnswerAExp=The [[sensitivity]] is the probability of a positive test result when the subjects actually have the disease. The sensitivity can be calculated as follows:


[[Sensitivity]]= [[True positive]]/([[True positive]] + [[false negative]])
[[Sensitivity]]= [[True positive]]/([[True positive]] + [[false negative]])


In this case, sensitivity=90/(90+10)= 90/100
In this case, sensitivity=90/(90+10)= 90/100
|AnswerB=320/330
|AnswerB=320/400
|AnswerBExp=The [[specificity]] is the probability of a negative test result when the subjects do not have the disease. The specificity can be calculated as follows:
|AnswerBExp=The [[specificity]] is the probability of a negative test result when the subjects do not have the disease. The specificity can be calculated as follows:


[[Specificity]]= [[True negative]]/([[True negative]]+ [[false positive]])
[[Specificity]]= [[True negative]]/([[True negative]]+ [[false positive]])
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|AnswerCExp=The positive predictive value (PPV) is the proportion of individuals with a positive test result who actually have the disease. The PPV can be calculated as follows:
|AnswerCExp=The positive predictive value (PPV) is the proportion of individuals with a positive test result who actually have the disease. The PPV can be calculated as follows:


[[PPV]]= [[True positive]]/([[True positive]] + [[false negative]])
[[PPV]]= [[True positive]]/([[True positive]] + [[false positive]])


In this case, PPV= 90/(90+80)= 90/170
In this case, PPV= 90/(90+80)= 90/170
|AnswerD=320 / 400
|AnswerD=320/330
|AnswerDExp=The negative predictive value is the proportion of individuals without the disease who test negative using the specified testing modality. The NPV can be calculated as follows:
|AnswerDExp=The negative predictive value is the proportion of individuals without the disease who test negative using the specified testing modality. The NPV can be calculated as follows:


[[NPV]]= [[True negative]]/([[True negative]]+ [[false negative]])
[[NPV]]= [[True negative]]/([[True negative]]+ [[false negative]])


In this case, NPV= 320/(320+80)= 320/400
In this case, NPV= 320/(320+10)= 320/330
|AnswerE=410/500
|AnswerE=410/500
|AnswerEExp=The accuracy of a test is the proportion of true results (both true positives and true negatives) in the population.
|AnswerEExp=The accuracy of a test is the proportion of true results (both true positives and true negatives) in the population.
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In this case, accuracy=90+320/500= 410/500
In this case, accuracy=90+320/500= 410/500
|EducationalObjectives=The [[positive predictive value]] (PPV) is the proportion of individuals with a positive test result who actually have the disease. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition of interest.
|EducationalObjectives=The [[positive predictive value]] (PPV) is the proportion of individuals with a positive test result who actually have the disease.  
[[PPV]]= [[True positive]]/([[True positive]] + [[false positive]])
|References=First Aid 2014 page 51
|References=First Aid 2014 page 51
|RightAnswer=C
|RightAnswer=C

Revision as of 18:13, 30 July 2014
Author [[PageAuthor::Gonzalo A. Romero, M.D. [1] (Reviewed by Rim Halaby and Yazan Daaboul)]]
Exam Type ExamType::USMLE Step 1
Main Category MainCategory::Biostatistics/ Epidemiology
Sub Category SubCategory::General Principles
Prompt [[Prompt::CG-23 is a bloodborne infectious organism that causes an acute infection in healthy adults. A group of investigators developed a new diagnostic test for the detection of the serum marker Z-AWS, a structural component of the infectious organism CG-23. A study is evaluating the performance of the new test among 500 healthy subjects who are followed-up for the development of symptoms. In addition, blood is collected for real-time polymerase chain reaction (PCR) analysis, the gold standard test for the detection of the CG-23 DNA. The infectious organism's DNA is detected in 100 individuals by real-time PCR. The sensitivity and specificity of the novel test are 90% and 80%, respectively. What is the probability that a subject with a positive result in the new diagnostic test truly has the disease?]]
Answer A AnswerA::90/100
Answer A Explanation [[AnswerAExp::The sensitivity is the probability of a positive test result when the subjects actually have the disease. The sensitivity can be calculated as follows:

Sensitivity= True positive/(True positive + false negative)

In this case, sensitivity=90/(90+10)= 90/100]]

Answer B AnswerB::320/400
Answer B Explanation [[AnswerBExp::The specificity is the probability of a negative test result when the subjects do not have the disease. The specificity can be calculated as follows:

Specificity= True negative/(True negative+ false positive)

In this case, specificity=320/(320+10)= 320/330]]

Answer C AnswerC::90/170
Answer C Explanation [[AnswerCExp::The positive predictive value (PPV) is the proportion of individuals with a positive test result who actually have the disease. The PPV can be calculated as follows:

PPV= True positive/(True positive + false positive)

In this case, PPV= 90/(90+80)= 90/170]]

Answer D AnswerD::320/330
Answer D Explanation [[AnswerDExp::The negative predictive value is the proportion of individuals without the disease who test negative using the specified testing modality. The NPV can be calculated as follows:

NPV= True negative/(True negative+ false negative)

In this case, NPV= 320/(320+10)= 320/330]]

Answer E AnswerE::410/500
Answer E Explanation [[AnswerEExp::The accuracy of a test is the proportion of true results (both true positives and true negatives) in the population.

Accuracy= (True positive + True negative)/ Total number of subject tested

In this case, accuracy=90+320/500= 410/500]]

Right Answer RightAnswer::C
Explanation [[Explanation::In order to calculate the positive predictive value (PPV), a 2x2 table containing the true positive, false positive, true negative and false negative needs to be constructed. The sample size is 500 and the total number of subjects with disease is 100. Therefore, the total number of subjects without the disease is 400. Since the sensitivity is 90%, 90 subjects of the 100 who have the disease are true positive subjects; while 10 are false negative. Similarly, since the specificity is 80%, 320 subjects out of the 400 are the true negative subjects; and 80 (400-320) are false negative.

The positive predictive value (PPV) is the proportion of individuals with a positive test result who actually have the disease. The PPV can be calculated as follows:

PPV= True positive/(True positive + false negative)

DiseaseNo disease
Positive test True positiveFalse negative
Negative testFalse positive True negative

In this case, PPV= 90/(90+80)= 90/170

DiseaseNo disease Total
Positive test 90 80 170
Negative test10 320330
Total100 400 500

Educational Objective: The positive predictive value (PPV) is the proportion of individuals with a positive test result who actually have the disease. PPV= True positive/(True positive + false positive)
References: First Aid 2014 page 51]]

Approved Approved::Yes
Keyword WBRKeyword::Infectious disease, WBRKeyword::biostatistics, WBRKeyword::positive predictive value
Linked Question Linked::
Order in Linked Questions LinkedOrder::
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