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{{WBRQuestion
{{WBRQuestion
|QuestionAuthor=Gonzalo Romero
|QuestionAuthor=[[User:Gonzalo Romero|Gonzalo A. Romero, M.D.]] [mailto:gromero@wikidoc.org] (Reviewed by  {{YD}})
|ExamType=USMLE Step 1
|ExamType=USMLE Step 1
|MainCategory=Behavioral Science/Psychiatry
|MainCategory=Biostatistics/Epidemiology
|SubCategory=Infectious Disease
|SubCategory=General Principles
|MainCategory=Behavioral Science/Psychiatry
|Prompt=CG-23 is a bloodborne infectious organism that causes a self-limited acute infection among healthy adults. A group of investigators has developed a new diagnostic test for the detection of a serum marker, TAK, which is a structural component of the CG-23 capsule. A study is evaluating the performance of the new test among 500 healthy subjects who are followed-up for the development of symptoms. Blood is also withdrawn for real-time polymerase chain reaction (RT-PCR) analysis, the gold standard test for the detection of CG-23 DNA. The infectious organism's DNA is detected in 100 individuals by RT-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?
|SubCategory=Infectious Disease
|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 have [[true positive]] results; while 10 have [[false negative]] results. Similarly, since the specificity is 80%, 320 subjects out of the 400 have [[true negative]] results, and 400-32=80 have [[false negative]] results.
|MainCategory=Behavioral Science/Psychiatry
|SubCategory=Infectious Disease
|MainCategory=Behavioral Science/Psychiatry
|MainCategory=Behavioral Science/Psychiatry
|SubCategory=Infectious Disease
|MainCategory=Behavioral Science/Psychiatry
|SubCategory=Infectious Disease
|MainCategory=Behavioral Science/Psychiatry
|SubCategory=Infectious Disease
|MainCategory=Behavioral Science/Psychiatry
|SubCategory=Infectious Disease
|MainCategory=Behavioral Science/Psychiatry
|MainCategory=Behavioral Science/Psychiatry
|SubCategory=Infectious Disease
|Prompt=A study is trying to prove different parameters of a new test used in diagnosing an infectious endemic disease in developing countries. Underserved areas’ tribes have  have called the disease “Ztika”. For years physicians have struggled to diagnose it early enough to treat it soon so the infectious organism is controlled with antimicrobial therapy with the final goal of decreasing mortality. The disease is asymptomatic until severe symptoms develop and is exactly when the immune response is triggered making the final diagnosis possible through IgM specific serologic testing. A group of investigators had the initiative to develop a new test X-22231 which screens for the serum marker Z-AWS (a component of the infectious organism), present in early phases of this infectious disease called “Ztika” and decide to run a Phase I trial which proved to have promising results. The researchers move onto a Phase II trial recruiting more patients. The Institutional Review Board meets with the Sponsors and Data Monitoring Safety Committee in order to establish that a proper study design is being created and conducted. The study starts having an initial sample which involves 500 subjects and followed overtime which helped gathering data. The pre-test probabilities were determined; Sensitivity 90% and Specificity 80%. It was determined that 100 patients did have the disease “Ztika”. What is the probability that a patient with a positive result from the X-22231 test really had the disease?
|Explanation=Test taking strategy tip: It is important to read throughly this question scanning for the data that is vital which will help in answering the question correctly. USMLE Step 1 more often is including long stems, so being able to read quickly, yet carefully will enable the examinee to screen for the information necessary to aswer correctly. This question tests for basic statistical knowledge which is commony tested on STEP 1 and other USMLE examinations. A great tip is to read at the final lines to find what the question is really looking for.


{| class="wikitable" border="2"
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:
|-style="background:silver; color:black" align="center"
| || '''Diseased''' || '''Healthy''' || '''Total'''
|-style="background:white; color:black"
| Positive Test Result || TP:90 || FP:80 || 170
|-style="background:white; color:black"
| Negative Test Result || FN:10 || TN:320 || 330
|-style="background:white; color:black"
| || 100 || 400 || 500
|}


Explanation: At the end of the long stem the data is provided directly in a way that will enable construct the table which will orient the test taker to calculate what’s being asked; the Positive Predictive Value. The sample size is 500, it is given the number of people with the disease 100, which is the True Positive + False Negative (TP + FN). Also the sensitivity or the proportion of truly diseased persons is given 90%. From there it can be assumed that the True positives were 90 and False negatives were 10. The remainder 400 did not have the disease. The specificity (proportion of truly non-diseased persons who are identified as non diseased) is also given in the stem which is 80% so it can be established that the True Negatives (TN) is the 80% of this 400 or 320 and the False Positives were 80. The [[Positive Predictive Value]] is the probability that a positive test result is actually positive, being TP/(TP + FP) being calculated as 90 / 170.
[[PPV]]= [[True positive]]/([[True positive]] + [[false negative]])
 
<table>
<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>'''Negative test'''</td><td>[[False positive]]</td><td> [[True negative]]</td></tr>
 
</table>
 
In this case, PPV= 90/(90+80)= 90/170
 
<table>
<tr class="v-firstrow"><td> </td><td>'''Disease'''</td><td>'''No disease''' </td><td>'''Total'''</td></tr>
<tr><td>'''Positive test''' </td><td> 90 </td><td>80 </td><td>170</td></tr>
<tr><td>'''Negative test'''</td><td>10 </td><td> 320</td><td>330 </td></tr>
<tr><td>'''Total'''</td><td>100 </td><td> 400</td><td> 500</td></tr>
</table>
|AnswerA=90/100
|AnswerA=90/100
|AnswerAExp=[[Sensitivity]] TP/ (TP + FN)
|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:
|AnswerB=320/ 400
 
|AnswerBExp=[[Specificity]] or TN / (TN + FP)
[[Sensitivity]]= [[True positive]] / ([[True positive]] + [[False negative]])
 
In this case, sensitivity=90/(90+10)= 90/100
|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:
 
[[Specificity]]= [[True negative]] / ([[True negative]]+ [[False positive]])
 
In this case, specificity=320/(320+10)= 320/330
|AnswerC=90/170
|AnswerC=90/170
|AnswerCExp=PPV. Correct answer. See the explanation above.
|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:
|AnswerD=320 / 330
 
|AnswerDExp=[[Negative Predictive Value]] (NPV); or the probability that a negative test result is truly negative. TN / (TN + FN)
[[PPV]]= [[True positive]] / ([[True positive]] + [[False positive]])
|AnswerE=90 + 320 / 500
 
|AnswerEExp=[[Accuracy]] “Trues divided by the sample size” or (TN + TP) / (TP + TN + FP + FN)
In this case, PPV= 90/(90+80)= 90/170
|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:
 
[[NPV]]= [[True negative]] / ([[True negative]] + [[False negative]])
 
In this case, NPV= 320/(320+10)= 320/330
|AnswerE=410/500
|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
|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
|RightAnswer=C
|RightAnswer=C
|WBRKeyword=Biostatistics, Positive predictive value
|Approved=Yes
|Approved=Yes
}}
}}

Latest revision as of 23:07, 27 October 2020
Author [[PageAuthor::Gonzalo A. Romero, M.D. [1] (Reviewed by Yazan Daaboul, M.D.)]]
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 a self-limited acute infection among healthy adults. A group of investigators has developed a new diagnostic test for the detection of a serum marker, TAK, which is a structural component of the CG-23 capsule. A study is evaluating the performance of the new test among 500 healthy subjects who are followed-up for the development of symptoms. Blood is also withdrawn for real-time polymerase chain reaction (RT-PCR) analysis, the gold standard test for the detection of CG-23 DNA. The infectious organism's DNA is detected in 100 individuals by RT-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 have true positive results; while 10 have false negative results. Similarly, since the specificity is 80%, 320 subjects out of the 400 have true negative results, and 400-32=80 have false negative results.

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::Biostatistics, WBRKeyword::Positive predictive value
Linked Question Linked::
Order in Linked Questions LinkedOrder::
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