Untitled Quiz

Study Notes

Diagnostic Tests

Diagnostic tests are commonly used by physicians, encompassing not only laboratory assessments, but also taking medical history, observing signs and symptoms, and performing imaging.
These tests are used to confirm or refute the presence or absence of a disease.
An ideal diagnostic test would accurately identify all patients with the disease (true positives) and correctly identify all patients without the disease (true negatives).

Study Design for Diagnostic Tests

Cross-sectional Study Design is a research method that involves collecting data from a group of participants at a single point in time. It aims to examine relationships between variables.
- Snapshot of a population at a specific time.
- Surveys and measures multiple variables.
- Quick and inexpensive.
- Ethical to measure harm.
- Can’t measure changes over time and causality.
- Can be descriptive or analytical.

Gold Standard Test

A gold standard test or criterion standard test is considered the definitive diagnostic test in medicine.
It serves as a benchmark for diagnosing a disease process or evaluating scientific evidence.

Anatomy of a 2x2 Contingency Table

A 2x2 contingency table is used to analyze diagnostic test results and evaluate their accuracy.
The table compares the results of a surrogate test (the test being evaluated) to the gold standard test.

Table Layout:

Rows: Represent the results of the surrogate test (Positive or Negative).
Columns: Represent the results of the gold standard test (Positive or Negative).

Cells of the Table:

True Positive (a): Test is positive, and the gold standard test is also positive.
False Negative (c): Test is negative, but the gold standard test is positive.
False Positive (b): Test is positive, but the gold standard test is negative.
True Negative (d): Test is negative, and the gold standard test is also negative.

Different Measures of Accuracy

Sensitivity (positive in disease):
- Measures the test’s ability to correctly identify individuals with the disease (true positives).
- Calculated as a / (a+c).
- Represents the probability of a positive test result in someone who actually has the disease.
- Used for screening tests.
Specificity (negative in health):
- Measures the test’s ability to correctly identify individuals without the disease (true negatives).
- Calculated as d / (b+d).
- Represents the probability of a negative test result in someone who does not have the disease.
- Used for confirmatory tests.
Positive Predictive Value (PPV):
- Represents the percentage of patients with a positive test who actually have the disease.
- Calculated as a / (a+b).
- Indicates the probability of having the disease when the test is positive.
Negative Predictive Value (NPV):
- Represents the percentage of patients with a negative test who do not have the disease.
- Calculated as d / (c+d).
- Shows the probability of not having the disease when the test is negative.
Positive Likelihood Ratio:
- Measures the amount of certainty gained after a positive test result.
- Interpreted as the ratio of the likelihood of a positive test in someone with the disease compared to someone without the disease.
Negative Likelihood Ratio:
- Measures the amount of certainty gained after a negative test result.
- Interpreted as the ratio of the likelihood of a negative test in someone with the disease compared to someone without the disease.
- A likelihood ratio close to 1 suggests the test provides little additional information about the disease.

Guide

SNOUT:
- High SeNsitivity helps to RULE OUT a disease.
- A negative test result in a highly sensitive test is more likely to be true.
SPIN:
- High SPecificity helps to RULE IN a disease.
- A positive test result in a highly specific test is more likely to be true.