Likelihood Ratio Calculator

LR+ > 10 provides strong evidence for disease. LR+ 5-10 is moderate. LR+ 2-5 is weak but may still be useful. LR− < 0.1 strongly rules out disease. LR− 0.1-0.2 is moderate for exclusion. LR values between 0.5 and 2.0 provide minimal diagnostic information.

LRs combine both metrics into a single number that directly translates to clinical probability changes via Bayesian reasoning. They are independent of disease prevalence and can be applied to individual patients using their specific pre-test probability, unlike sensitivity/specificity which describe test properties in populations.

Draw a straight line from your pre-test probability (left axis) through the likelihood ratio (middle axis) and extend it to the right axis to read the post-test probability. A digital version: convert pre-test probability to odds, multiply by LR, then convert back to probability.

Yes. Instead of a single positive/negative cutoff, you can calculate interval likelihood ratios for each result range. For example, a blood test might have different LRs for low-normal, high-normal, mildly elevated, and markedly elevated results, providing more nuanced interpretation.

Pre-test probability is your estimated probability of disease before performing the test, based on disease prevalence in the relevant population, patient symptoms, clinical examination, and results of any prior tests. It serves as the starting point for Bayesian diagnostic reasoning with likelihood ratios.

Convert pre-test probability to pre-test odds: odds = probability ÷ (1 − probability). Multiply by LR: post-test odds = pre-test odds × LR. Convert back: post-test probability = post-test odds ÷ (1 + post-test odds). Example: 20% pre-test, LR+ = 6: odds = 0.25, post-odds = 1.5, post-probability = 60%.

LR+ applies to positive test results and indicates how much more likely the disease is after a positive test. LR− applies to negative test results and indicates how much less likely the disease is after a negative test. Both are needed for complete test evaluation.

Yes, if the tests are independent (measure different aspects of the disease). Multiply the LRs sequentially: post-test odds = pre-test odds × LR₁ × LR₂. This is the strength of Bayesian reasoning — each independent test further refines the diagnostic probability.

An LR of 1.0 is completely uninformative — the test result does not change the probability of disease at all. LRs between 0.5 and 2.0 are generally considered clinically useless because they change probability by too little to affect management decisions.

EBM clinicians use LRs to perform bedside Bayesian reasoning: estimate a pre-test probability from clinical findings, apply the LR from the best available test, and determine whether the post-test probability crosses a treatment threshold. This quantitative approach replaces subjective test interpretation.

Yes, but screening tests are applied to low-prevalence populations, so even good LR+ values produce many false positives (low positive predictive value). Screening programs require extremely high LR+ or multi-stage testing to achieve acceptable PPV. LR− is more relevant for screening since the goal is ruling out disease.