# Likelihood ratios

Likelihood ratios are the ratio of the probability of a specific test result for subjects with the condition against the probability of the same test result for subjects without the condition.

A likelihood ratio of 1 indicates that the test result is equally likely in subjects with and without the condition. A ratio > 1 indicates that the test result is more likely in subjects with the condition than without the condition, and conversely, a ratio < 1 indicates that the test result is more likely in subjects without the condition. The larger the ratio, the more likely the test result is in subjects with the condition than without; likewise, the smaller the ratio, the more likely the test result is in subjects without than with the condition.

The likelihood ratio of a positive test result is the ratio of the probability of a positive test result in a subject with the condition (true positive fraction) against the probability of a positive test result in a subject without the condition (false positive fraction). The likelihood ratio of a negative test result is the ratio of the probability of a negative test result in a subject with the condition (false negative fraction) to the probability of a negative test result in a subject without the condition (true negative fraction).

- What is Analyse-it?
- What's new?
- Administrator's Guide
- User's Guide
- Statistical Reference Guide
- Distribution
- Compare groups
- Compare pairs
- Contingency tables
- Correlation and association
- Principal component analysis (PCA)
- Factor analysis (FA)
- Item reliability
- Fit model
- Method comparison / Agreement
- Measurement systems analysis (MSA)
- Reference interval
- Diagnostic performance
- Measures of diagnostic accuracy
- Sensitivity / Specificity
- Likelihood ratios
- Predictive values
- Youden J
- Estimating sensitivity and specificity of a diagnostic test
- Comparing the sensitivity and specificity two diagnostic tests
- ROC plot
- Plotting a single ROC curve
- Comparing two or more ROC curves
- Area under the curve (AUC)
- Testing the area under the curve
- Difference between the areas under two curves
- Testing the difference between the areas under two curves
- Decision thresholds
- Decision plot
- Finding the optimal decision threshold
- Predicting the decision threshold
- Study design
- Survival/Reliability
- Control charts
- Process capability
- Pareto analysis
- Study Designs
- Bibliography

Version 6.10

Published 21-Jul-2022