Understanding Predictive Value of Positive Test

The question asks for the correct statement regarding the predictive value of a positive test. This value is crucial in interpreting medical test results.

Analyzing Predictive Value Statements

Let's examine each statement:

• Statement 1: It does not tell about diagnostic power of test
  This is incorrect. The predictive value of a positive test (Positive Predictive Value, PPV) is a key measure of a test's diagnostic utility or power. It quantifies how likely a positive result accurately indicates the presence of the disease.
• Statement 2: The more prevalent the disease, the less accurate the test is
  This is incorrect. The relationship between disease prevalence and the predictive value of a positive test is inverse to accuracy. Specifically, a higher disease prevalence generally leads to a *higher* Positive Predictive Value (PPV), assuming the test's intrinsic characteristics (sensitivity and specificity) remain constant. It does not mean the test becomes less accurate overall, but rather the probability of a positive result being true increases.
• Statement 3: It tells the probability that a patient with positive test has the disease in question
  This statement accurately defines the Positive Predictive Value (PPV). PPV is formally defined as the probability that an individual has the disease given that they have tested positive. Mathematically, $PPV = P(\text{Disease} | \text{Positive Test})$.
• Statement 4: It tells the probability that a patient with positive test does not have the disease in question
  This is incorrect. This statement describes the complement of the PPV, i.e., $P(\text{No Disease} | \text{Positive Test}) = 1 - PPV$. The predictive value of a positive test specifically addresses the likelihood of *having* the disease.

Therefore, the only true statement is the one defining the Positive Predictive Value correctly.