A Case Study to Understand Sensitivity & Specificity (”Screen-It”)

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This is a FAKE case study with FAKE data for training purpose only!

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The Case of the New Diagnostic Test 🧪

A new diagnostic test, Screen-It, is being evaluated for its effectiveness in detecting Hypertonic Pancreatitis (HP), a rare condition with a prevalence of 10% in the target population. A study involving 1,000 participants (a mix of people with and without HP) was conducted to assess the test's performance against the gold standard diagnostic method, a complex and expensive biopsy.

Here are the results of the study:

Test Result	Has HP (Positive Biopsy)	Does NOT Have HP (Negative Biopsy)	Total
Positive Screen-It	80	90	170
Negative Screen-It	20	810	830
Total	100	900	1000

The Calculations 🧮

Using the data from the table above, let's calculate the key epidemiological metrics.

1. Sensitivity

Sensitivity measures the proportion of people with the disease who correctly test positive. It's the ability of the test to identify true positives.

Formula: (True Positives) / (True Positives + False Negatives)
Calculation: 80 / (80 + 20) = 80 / 100 = 0.8 or 80%
Interpretation: This means that 80% of individuals who actually have HP will be correctly identified by the Screen-It test.

2. Specificity

Specificity measures the proportion of people without the disease who correctly test negative. It's the ability of the test to identify true negatives.

Formula: (True Negatives) / (True Negatives + False Positives)
Calculation: 810 / (810 + 90) = 810 / 900 = 0.9 or 90%
Interpretation: This indicates that 90% of individuals who do not have HP will be correctly identified as negative by the Screen-It test.

3. False Negative Rate

False Negative Rate is the proportion of people with the disease who incorrectly test negative.