Comparison of Predictive Values using Relative Predictive Values

Performs a test for differences in (positive and negative) predictive values of two binary diagnostic tests in a paired study design using relative predictive values, as proposed by Moskowitz and Pepe (2006).

Usage

pv.rpv(tab, alpha)

Arguments

tab: An object of class tab.paired.
alpha: Significance level alpha used to compute 100(1-alpha)%-confidence intervals, the default is 0.05.

Value

A list containing:

ppv: named vector containing test1 (the positive predictive value of test 1), test2 (the positive predictive value of test 2), rppv (the relative positive predictive value, computed as test2 / test1), se.log.rppv (the standard error of the logarithm of rppv), lcl.rppv (lower confidence limit of rppv), ucl.rppv (the upper confidence limit of rppv), the test.statistic and the corresponding p.value.
npv: named vector containing test1 (the negative predictive value of test 1), test2 (the negative predictive value of test 2), rnpv (the relative negative predictive value, computed as test2 / test1), se.log.rnpv (the standard error of the logarithm of rnpv), lcl.rnpv (the lower confidence limit of rnpv), ucl.rnpv (the upper confidence limit of rnpv), the test.statistic and the corresponding p.value.
Sigma: Estimated variance-covariance matrix for {log(relative positive predictive value), log(relative negative predictive value)}.
method: Name of the method used to compare predictive values, here “relative predictive values (rpv)”.
alpha: Significance level alpha used to compute 100(1-alpha)%-confidence intervals for rppv and rnpv, the default is 0.05.

References

Moskowitz, C.S., and Pepe, M.S. (2006). Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs. Clin Trials, 3(3):272-9.

Examples

data(Paired1) # Hypothetical study data
ftable(Paired1)
#>      y2   0   1
#> d y1           
#> 0 0     155  22
#>   1      53  31
#> 1 0      32  22
#>   1      78 319
paired.layout <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
paired.layout 
#> Two binary diagnostic tests (paired design)
#> 
#> Test1: 'y1'
#> Test2: 'y2'
#> 
#> Diseased:
#>            Test1 pos. Test1 neg. Total
#> Test2 pos.        319         22   341
#> Test2 neg.         78         32   110
#> Total             397         54   451
#> 
#> Non-diseased:
#>            Test1 pos. Test1 neg. Total
#> Test2 pos.         31         22    53
#> Test2 neg.         53        155   208
#> Total              84        177   261
#> 
rpv.results <- pv.rpv(paired.layout)
str(rpv.results)
#> List of 5
#>  $ ppv   : Named num [1:8] 0.8254 0.8655 1.0486 0.0199 1.0085 ...
#>   ..- attr(*, "names")= chr [1:8] "test1" "test2" "rppv" "se.log.rppv" ...
#>  $ npv   : Named num [1:8] 0.7662 0.6541 0.8536 0.0378 0.7926 ...
#>   ..- attr(*, "names")= chr [1:8] "test1" "test2" "rnpv" "se.log.rnpv" ...
#>  $ Sigma : num [1:2, 1:2] 0.000397 0.000402 0.000402 0.001432
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:2] "log.rppv" "log.rnpv"
#>   .. ..$ : chr [1:2] "log.rppv" "log.rnpv"
#>  $ method: chr "relative predictive values (rpv)"
#>  $ alpha : num 0.05
rpv.results
#> $ppv
#>          test1          test2           rppv    se.log.rppv       lcl.rppv 
#>     0.82536383     0.86548223     1.04860694     0.01991247     1.00847050 
#>       ucl.rppv test.statistic        p.value 
#>     1.09034078     2.38355929     0.01714612 
#> 
#> $npv
#>          test1          test2           rnpv    se.log.rnpv       lcl.rnpv 
#>   7.662338e-01   6.540881e-01   8.536403e-01   3.783679e-02   7.926258e-01 
#>       ucl.rnpv test.statistic        p.value 
#>   9.193516e-01  -4.182314e+00   2.885568e-05 
#> 
#> $Sigma
#>              log.rppv     log.rnpv
#> log.rppv 0.0003965065 0.0004024578
#> log.rnpv 0.0004024578 0.0014316223
#> 
#> $method
#> [1] "relative predictive values (rpv)"
#> 
#> $alpha
#> [1] 0.05
#> 
rpv.results$ppv["p.value"]
#>    p.value 
#> 0.01714612