Likelihood Ratio Calculator
The Likelihood Ratio Calculator computes positive likelihood ratio (LR+) and negative likelihood ratio (LR-) from a diagnostic test's sensitivity and specificity. Evaluate how much a test result shifts the probability of disease for evidence-based clinical decisions.
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What is a Likelihood Ratio?
A likelihood ratio quantifies how much a test result changes the probability of having a condition. The positive likelihood ratio (LR+) measures how much a positive test increases disease probability. The negative likelihood ratio (LR-) measures how much a negative test decreases it. LR+ above 10 or LR- below 0.1 are considered strong evidence.
Likelihood ratios combine sensitivity and specificity into a single metric that is independent of disease prevalence. Clinicians use LRs with pre-test probability (Bayesian reasoning) to calculate post-test probability, making them more useful than sensitivity/specificity alone for clinical decision-making.
Verwendete Formeln & Gleichungen
Dieser Likelihood Ratio Calculator verwendet 5 Kern-Gleichungen:
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%.
Wie man diesen Rechner benutzt
Um diesen Verhältnis Rechner zu nutzen, folgen Sie diesen 3 Schritten:
Werte eingeben
Geben Sie die bekannten Verhältniswerte in die Eingabefelder ein. Lassen Sie ein Feld leer – dies ist der unbekannte Wert, den der Verhältnis Rechner löst.
Modus wählen
Wählen Sie den Verhältnismodus – Lösen, Vereinfachen oder Skalieren. Jeder Modus wendet unterschiedliche Gleichungen auf Ihre Eingabewerte an.
Ergebnisse erhalten
Klicken Sie auf Berechnen. Der Ergebnisbildschirm zeigt die Antwort mit einem visuellen Verhältnisbalken, einem Kreisdiagramm und einer Schritt-für-Schritt-Lösungsaufschlüsselung.
Beispielaufgaben & Schritt-für-Schritt-Lösungen
Hier sind 3 Beispielaufgaben mit Schritt-für-Schritt-Lösungen unter Verwendung dieses Verhältnis Rechners:
Eingabe 1 Test with 90% sensitivity, 85% specificity
Eingabe 2 Calculate post-test probability
Eingabe 3 Highly sensitive test: 99% sensitivity, 50% specificity
Häufig gestellte Fragen
What is a good likelihood ratio? ▼
LR+ > 10 is strong evidence for disease. LR+ 5-10 is moderate. LR+ 2-5 is weak. LR- < 0.1 strongly rules out disease. LR- 0.1-0.2 is moderate. LR values near 1.0 provide no useful information.
Why are likelihood ratios better than sensitivity/specificity? ▼
LRs combine both metrics into a single number and are independent of disease prevalence. They can be applied directly to individual patients using pre-test probability. Sensitivity and specificity are prevalence-independent but don't directly give post-test probability.
How do I use the Fagan nomogram? ▼
Draw a line from your pre-test probability (left axis) through the likelihood ratio (middle axis) to find the post-test probability (right axis). This visual tool quickly converts LRs into clinically useful probability changes.
Can likelihood ratios be used for multi-level test results? ▼
Yes. Interval likelihood ratios can be calculated for different result ranges (e.g., low, moderate, high values) rather than just positive/negative. Each interval has its own LR for more nuanced interpretation.
What is pre-test probability? ▼
Pre-test probability is your estimated chance of disease before conducting the test, based on prevalence, symptoms, and clinical judgment. It's the starting point for Bayesian reasoning with likelihood ratios.