Demonstrate Sensitivity, Specificity, PPV, and NPV

This function demonstrates how to get PPV and NPV from Sensitivity, Specificity, and Prevalence by using a virtual population rather than a direct application of Bayes Rule. This approach is more intuitive to mathphobes.

SensSpec.demo(sens, spec, prev, n = 100000, step = 11)

Arguments

sens

Sensitivity (between 0 and 1)

spec

Specificity (between 0 and 1)

prev

Prevalence (between 0 and 1)

n

Size of the virtual population (large round number)

step

which step of the process to display

Details

The common way to compute Positive Predictive Value (probability of disease given a positive test (PPV)) and Negative Predictive Value (probability of no disease given negative test (NPV)) is to use Bayes' rule with the Sensitivity, Specificity, and Prevalence.

This approach can be overwhelming to non-math types, so this demonstration goes through the steps of assuming a virtual population, then filling in a 2x2 table based on the population and given values of Sensitivity, Specificity, and Prevalence. PPV and NPV are then computed from this table. This approach is more intuitive to many people.

The function can be run multiple times with different values of step to show the steps in building the table, then rerun with different values to show how changes in the inputs affect the results.

Value

An invisible matrix with the 2x2 table

Author

Greg Snow, 538280@gmail.com

See also

roc.demo, fagan.plot, the various Epi packages, tkexamp

Examples

for(i in seq(1,11,2)) {
  SensSpec.demo(sens=0.95, spec=0.99, prev=0.01, step=i)
  if( interactive() ) {
    readline("Press Enter to continue")
  }
}
#>           Disease
#> Test          Yes     No      Total
#>   Positive                         
#>   Negative                         
#>                                    
#>   Total                       1e+05
#>  
#> PPV =
#> NPV = 
#> 
#>           Disease
#> Test          Yes     No      Total
#>   Positive                         
#>   Negative                         
#>                                    
#>   Total      1000  99000      1e+05
#>  
#> PPV =
#> NPV = 
#> 
#>           Disease
#> Test          Yes     No      Total
#>   Positive    950                  
#>   Negative     50                  
#>                                    
#>   Total      1000  99000      1e+05
#>  
#> PPV =
#> NPV = 
#> 
#>           Disease
#> Test          Yes     No      Total
#>   Positive    950    990           
#>   Negative     50  98010           
#>                                    
#>   Total      1000  99000      1e+05
#>  
#> PPV =
#> NPV = 
#> 
#>           Disease
#> Test          Yes     No      Total
#>   Positive    950    990       1940
#>   Negative     50  98010      98060
#>                                    
#>   Total      1000  99000     100000
#>  
#> PPV =
#> NPV = 
#> 
#>           Disease
#> Test          Yes     No      Total
#>   Positive    950    990       1940
#>   Negative     50  98010      98060
#>                                    
#>   Total      1000  99000     100000
#>  
#> PPV = 950/1940 = 0.4897
#> NPV = 98010/98060 = 0.9995 
#>