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|SubCategory=Infectious Disease
|SubCategory=Infectious Disease
|Prompt=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 individuals who were followed up for the occurrence of the symptoms.  In addition, blood was collected for PCR analysis, the gold standard test for this infectious disease.  The infectious organism is detected in 100 individuals by PCR.  The pre-test probabilities of the novel test are determined as follows: the sensitivity is 90% and the specificity is 80%.  What is the probability that a subject with a positive result in the new diagnostic test actually has the disease?
|Prompt=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 individuals who were followed up for the occurrence of the symptoms.  In addition, blood was collected for PCR analysis, the gold standard test for this infectious disease.  The infectious organism is detected in 100 individuals by PCR.  The pre-test probabilities of the novel test are determined as follows: the sensitivity is 90% and the specificity is 80%.  What is the probability that a subject with a positive result in the new diagnostic test actually has 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.
|Explanation=In order to calculate the positive predictive value, the 2x2 table containing the [[true positive]], [[false positive]], [[true negative]] and [[false negative]] need 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]] while 10 are [[false negative]]. Similarly, since the specificity is 80%, 320 subjects out of the 400 are the [[true negative]] and 80 (400-320) are [[false negative]].


'''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.
The positive predictive value (PPV) , or precision rate, is the proportion of individuals with a positive test result who actually have preclinical disease. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition of interest. The PPV can be calculated as follows:
 
[[PPV]]= [[True positive]]/([[True positive]] + [[false negative]])
 
In this case, PPV= 90/(90+80)= 90/170


<table>
<table>
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|AnswerDExp=The negative predictive value is the proportion of individuals without preclinical 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 preclinical 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]]= [[True negative]] / ([[True negative]]+ [[false negative]])
|AnswerE=90 + 320 / 500
|AnswerEExp=[[Accuracy]] “Trues divided by the sample size” or (TN + TP) / (TP + TN + FP + FN)
|EducationalObjectives=The [[positive predictive value]] (PPV) , or precision rate, is the proportion of individuals with a positive test result who actually have preclinical 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) , or precision rate, is the proportion of individuals with a positive test result who actually have preclinical disease. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition of interest.
|RightAnswer=C
|RightAnswer=C
|WBRKeyword=Infectious disease, biostatistics, Positive predictive value
|WBRKeyword=Infectious disease, biostatistics, positive predictive value
|Approved=Yes
|Approved=Yes
}}
}}

Revision as of 17:35, 15 March 2014
Author [[PageAuthor::Associate Editor-In-Chief: Gonzalo A. Romero, M.D. [1]]]
Exam Type ExamType::USMLE Step 1
Main Category MainCategory::Biostatistics/ Epidemiology
Sub Category SubCategory::Infectious Disease
Prompt [[Prompt::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 individuals who were followed up for the occurrence of the symptoms. In addition, blood was collected for PCR analysis, the gold standard test for this infectious disease. The infectious organism is detected in 100 individuals by PCR. The pre-test probabilities of the novel test are determined as follows: the sensitivity is 90% and the specificity is 80%. What is the probability that a subject with a positive result in the new diagnostic test actually has the disease?]]
Answer A AnswerA::90/100
Answer A Explanation [[AnswerAExp::The sensitivity of a test is the ability of a test to be positive 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 of a test is the ability of a test to be negative 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+80)= 320/400]]

Answer C AnswerC::90/170
Answer C Explanation [[AnswerCExp::The positive predictive value (PPV) , or precision rate, is the proportion of individuals with a positive test result who actually have preclinical disease. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition of interest. The PPV can be calculated as follows:

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

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 preclinical disease who test negative using the specified testing modality. The NPV can be calculated as follows:

In this case, NPV= True negative / (True negative+ false negative)]]

Answer E AnswerE::
Answer E Explanation AnswerEExp::
Right Answer RightAnswer::C
Explanation [[Explanation::In order to calculate the positive predictive value, the 2x2 table containing the true positive, false positive, true negative and false negative need 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 while 10 are false negative. Similarly, since the specificity is 80%, 320 subjects out of the 400 are the true negative and 80 (400-320) are false negative.

The positive predictive value (PPV) , or precision rate, is the proportion of individuals with a positive test result who actually have preclinical disease. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition of interest. The PPV can be calculated as follows:

PPV= True positive/(True positive + false 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) , or precision rate, is the proportion of individuals with a positive test result who actually have preclinical disease. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition of interest.
References: ]]

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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