EBMcalc Medical Calculator

Bayesian Statistics I MultiCalc

Input

Prevalence

Sensitivity

Specificity

Results

Important: Inputs must be complete to perform calculation.

True Pos

False Pos

True Neg

False Neg

Pos Pred Value

Neg Pred Value

LR Pos

LR Neg

Pre Test Odds

Post Odds Pos

Post Prob Pos

Post Odds Neg

Post Prob Neg

False Pos Rate

False Neg Rate

Overall Acc

Decimal Precision

Data action

Include literature references: Yes No

Equations used

TruePos = Sensitivity * Prevalence

FalsePos = (1 - Specificity) * (1 - Prevalence)

TrueNeg = Specificity * (1 - Prevalence)

FalseNeg = (1 - Sensitivity) * Prevalence

PosPredValue = 100 * TruePos / (TruePos + FalsePos)

NegPredValue = 100 * TrueNeg / (TrueNeg + FalseNeg)

LRPos = Sensitivity / (1-Specificity)

LRNeg = (1-Sensitivity) / Specificity

PreTestOdds = Prevalence / (1 - Prevalence)

PostOddsPos = PreTestOdds * LRPos

PostProbPos = PostOddsPos / (1 + PostOddsPos)

PostOddsNeg = PreTestOdds * LRNeg

PostProbNeg = PostOddsNeg / (1 + PostOddsNeg)

FalsePosRate = 100 * FalsePos / (FalsePos + TrueNeg)

FalseNegRate = 100 * FalseNeg / (TruePos + FalseNeg)

OverallAcc = 100 * (TruePos + TrueNeg)

References

Perera R, Heneghan C. Making sense of diagnostic test likelihood ratios. ACP J Club. 2007 Mar-Apr;146(2):A8-9. PubMed ID: 17335149
Page J, Attia J. Using Bayes' nomogram to help interpret odds ratios. ACP J Club. 2003 Sep-Oct;139(2):A11-2. PubMed ID: 12954046

Associated Medical Subject Headings [MeSH]

Bayes Theorem, Diagnostic Techniques and Procedures, Evidence-Based Medicine, Humans, Likelihood Functions, Odds Ratio

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