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  Bayesian Statistics I MultiCalc

 
Input
 
Prevalence  
Sensitivity  
Specificity  

 
Results
 
 
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  

 
     
 
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
  1. Perera R, Heneghan C. Making sense of diagnostic test likelihood ratios. ACP J Club. 2007 Mar-Apr;146(2):A8-9. PubMed ID: 17335149 PubMed Logo
  2. 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 PubMed Logo

 
Associated Medical Subject Headings [MeSH]
 
         Bayes Theorem,   Diagnostic Techniques and Procedures,   Evidence-Based Medicine,   Humans,   Likelihood Functions,   Odds Ratio

 
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