9.4.5 Sensitivity, Specificity and Likelihood Ratios
Warning: Attempt to read property "ID" on null in /home/990584.cloudwaysapps.com/hvcgdwcmdt/public_html/wp-content/plugins/sfwd-lms/themes/ld30/templates/topic.php on line 80
For Studies Evaluating Diagnostic Accuracy, Estimates the Characteristics of a Test: Sensitivity, Specificity and Likelihood Ratios (Positive and Negative)
In studies evaluating diagnostic accuracy, it is important to estimate the characteristics of a test. These characteristics include sensitivity, specificity, and likelihood ratios (positive and negative).
| Actual Condition | Positive | Negative |
| Test Result Positive | True Positive (a) | False Positive (b) |
| Test Result Negative | False Negative (c) | True Negative (d) |
The table shows the four possible outcomes of a diagnostic test based on the actual condition of the patient and the test result. The cells represent the following:
- a: True Positive – the number of individuals with a positive test result who actually have the condition.
- b: False Positive – the number of individuals with a positive test result who do not have the condition.
- c: False Negative – the number of individuals with a negative test result who actually have the condition.
- d: True Negative – the number of individuals with a negative test result who do not have the condition.
This table is commonly used in studies evaluating diagnostic accuracy to calculate measures such as sensitivity, specificity, and likelihood ratios.
Sensitivity:
Sensitivity is defined as the proportion of true positives (people with the disease who test positive) out of all people with the disease. It is calculated as:
Where a is the number of true positives and c is the number of false negatives.
Specificity:
Specificity is defined as the proportion of true negatives (people without the disease who test negative) out of all people without the disease. It is calculated as:
Where d is the number of true negatives and b is the number of false positives.
Likelihood ratios:
Likelihood ratios (LR) indicate how much a given test result changes the odds of having the disease.
The positive likelihood ratio (LR+ve) is calculated as:
The negative likelihood ratio (LR-ve) is calculated as:
An LR+ve greater than 1 indicates that a positive test result increases the likelihood of having the disease, while an LR+ve less than 1 indicates that a positive test result decreases the likelihood of having the disease.
An LR-ve less than 1 indicates that a negative test result increases the likelihood of not having the disease, while an LR-ve greater than 1 indicates that a negative test result decreases the likelihood of not having the disease.
It is important to note that sensitivity and specificity are affected by the choice of cut-off values for a test. Different cut-off values may result in different estimates of sensitivity and specificity. Additionally, likelihood ratios are less affected by the choice of cut-off values and are considered more robust measures of test performance.
Overall, estimating the characteristics of a test is crucial for evaluating its usefulness in diagnosing a disease. Sensitivity, specificity, and likelihood ratios can provide valuable information for clinicians and researchers when making decisions about testing and treatment.
References:
- Deeks JJ. Systematic reviews in health care: Systematic reviews of evaluations of diagnostic and screening tests. BMJ. 2001;323(7305):157-62. doi: 10.1136/bmj.323.7305.157.