Likelihood Ratio Calculator
Calculate positive (LR+) and negative (LR−) likelihood ratios from test sensitivity and specificity. Convert pre-test probability to post-test probability using Bayesian reasoning — the essential tool for evidence-based diagnostic interpretation in clinical medicine and medical research.
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What is a Likelihood Ratio?
Likelihood ratios quantify how much a diagnostic test result changes the probability of disease. The positive likelihood ratio (LR+) measures how much the odds of disease increase with a positive test result. The negative likelihood ratio (LR−) measures how much the odds decrease with a negative result. LR+ = Sensitivity ÷ (1 − Specificity), and LR− = (1 − Sensitivity) ÷ Specificity.
Unlike sensitivity and specificity alone, likelihood ratios directly translate into clinically actionable probability changes. An LR+ of 10 means a positive test result makes disease 10 times more likely. An LR− of 0.1 means a negative result makes disease 10 times less likely. Values near 1.0 indicate the test provides no useful diagnostic information. Likelihood ratios are prevalence-independent, making them applicable across different patient populations.
Fórmulas y Ecuaciones Utilizadas
Esta Likelihood Ratio Calculator utiliza 5 ecuaciones principales:
1 Positive Likelihood Ratio ▼
Sensitivity 95%, Specificity 90%: LR+ = 0.95 / (1 - 0.90) = 0.95 / 0.10 = 9.5.
2 Negative Likelihood Ratio ▼
Sensitivity 95%, Specificity 90%: LR- = (1-0.95) / 0.90 = 0.05 / 0.90 = 0.056.
3 Post-Test Odds (Fagan Nomogram) ▼
Pre-test probability 20% → odds = 0.25. LR+ = 9.5 → Post-test odds = 0.25 × 9.5 = 2.375 → probability = 70.4%.
Explore all calculation options on the Calculadora de Relación home page.
Cómo Usar esta Calculadora
Para usar esta Calculadora de Relación, siga 3 pasos:
Ingrese los Valores
Escriba los valores de relación conocidos en los campos de entrada. Deje un campo vacío; ese es el valor desconocido que resuelve la Calculadora de Relación.
Elija el Modo
Seleccione el modo de relación: Resolver, Simplificar o Escalar. Cada modo aplica diferentes ecuaciones a sus valores de entrada.
Obtenga Resultados
Haga clic en Calcular. La pantalla de resultados muestra la respuesta con una barra de relación visual, un gráfico circular y un desglose de la solución paso a paso.
Problemas de Ejemplo y Soluciones Paso a Paso
Aquí hay 3 problemas de ejemplo con soluciones paso a paso usando esta Calculadora de Relación:
Entrada 1 Test with 90% sensitivity, 85% specificity
Entrada 2 Calculate post-test probability
Entrada 3 Highly sensitive test: 99% sensitivity, 50% specificity
Preguntas Frecuentes
What is a good likelihood ratio? ▼
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.
Why are likelihood ratios better than sensitivity and specificity? ▼
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.
How do I use the Fagan nomogram? ▼
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.
Can likelihood ratios be used for tests with multiple result levels? ▼
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.
What is pre-test probability? ▼
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.
How do I calculate post-test probability from a likelihood ratio? ▼
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%.
What is the difference between LR+ and LR−? ▼
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.
Can I combine likelihood ratios from multiple tests? ▼
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.
What is an uninformative likelihood ratio? ▼
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.
How are likelihood ratios used in evidence-based medicine? ▼
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.
Do likelihood ratios work for screening tests? ▼
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.