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rocbc

LE Bantis, B Brewer, CT Nakas, B Reiser

About the rocbc Package

(Authors: LE Bantis, B Brewer, CT Nakas, B Reiser)

This package is focused on Box-Cox based ROC curves and provides point estimates, confidence intervals (CIs), and hypothesis tests. It can be used both for inferences for a single biomarker and when comparisons of two correlated biomarkers are of interest. It provides inferences and comparisons around the area under the ROC curve (AUC), the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff. This documentation consists of two parts, the one marker and the two marker case. All approaches presented herein have been recently published (see references for each function).

The functions of each part:


checkboxcox


#DATA GENERATION
set.seed(123)
x=rgamma(100, shape=2, rate = 8) # Generates biomarker data from a gamma distribution 
                                 # for the healthy group.
y=rgamma(100, shape=2, rate = 4) # Generates biomarker data from a gamma distribution 
                                 # for the diseased group.
scores=c(x,y)
D=c(zeros(1,100), ones(1,100))

out=checkboxcox(marker=scores, D, plots="on", printShapiro = TRUE)
## 
## 	Shapiro-Wilk normality test
## 
## data:  x
## W = 0.92354, p-value = 2.181e-05
## 
## 
## 	Shapiro-Wilk normality test
## 
## data:  y
## W = 0.90169, p-value = 1.7e-06
## 
## 
## 	Shapiro-Wilk normality test
## 
## data:  transx
## W = 0.98765, p-value = 0.4826
## 
## 
## 	Shapiro-Wilk normality test
## 
## data:  transy
## W = 0.98936, p-value = 0.6128

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summary(out)
##                          Length Class  Mode    
## transformation.parameter   1    -none- numeric 
## transx                   100    -none- numeric 
## transy                   100    -none- numeric 
## pval_x                     1    -none- numeric 
## pval_y                     1    -none- numeric 
## pval_transx                1    -none- numeric 
## pval_transy                1    -none- numeric 
## rocfun                     1    -none- function

threerocs


#DATA GENERATION
set.seed(123)
x <- rgamma(100, shape=2, rate = 8) # generates biomarker data from a gamma
                                 # distribution for the healthy group.
y <- rgamma(100, shape=2, rate = 4) # generates biomarker data from a gamma
                                 # distribution for the diseased group.
scores <- c(x,y)
D=c(pracma::zeros(1,100), pracma::ones(1,100))
out=threerocs(marker=scores, D, plots="on")

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summary(out)
##               Length Class  Mode   
## AUC_Empirical 1      -none- numeric
## AUC_Metz      1      -none- numeric
## AUC_BoxCox    1      -none- numeric

rocboxcox


set.seed(123)
x=rgamma(100, shape=2, rate = 8)
y=rgamma(100, shape=2, rate = 4)
scores=c(x,y)
D=c(zeros(1,100), ones(1,100))
out=rocboxcox(marker=scores,D, 0.05, plots="on", printProgress=FALSE)

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summary(out)
##                          Length Class       Mode    
## transx                   100    -none-      numeric 
## transy                   100    -none-      numeric 
## transformation.parameter   1    -none-      numeric 
## AUC                        1    -none-      numeric 
## AUCCI                      2    -none-      numeric 
## pvalueAUC                  1    -none-      numeric 
## J                          1    -none-      numeric 
## JCI                        2    -none-      numeric 
## pvalueJ                    1    -none-      numeric 
## Sens                       1    -none-      numeric 
## CImarginalSens             2    -none-      numeric 
## Spec                       1    -none-      numeric 
## CImarginalSpec             2    -none-      numeric 
## cutoff                     1    -none-      numeric 
## CIcutoff                   2    -none-      numeric 
## areaegg                    1    -none-      numeric 
## arearect                   1    -none-      numeric 
## mxlam                      1    -none-      numeric 
## sxlam                      1    -none-      numeric 
## mylam                      1    -none-      numeric 
## sylam                      1    -none-      numeric 
## results                   18    formattable numeric 
## rocfun                     1    -none-      function

rocboxcoxCI


out=rocboxcoxCI(marker, D, givenSP=NA, givenSE=givenSE, alpha, plots)

givenSP=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9)
givenSE=NA
out=rocboxcoxCI(marker=scores ,D, givenSP=givenSP, givenSE=NA, alpha=0.05, plots="on")

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summary(out)
##          Length Class       Mode   
## SPandCIs  4     formattable numeric
## SEandCIs 36     formattable numeric
## Sevalues  1     -none-      logical
## Sphat     1     -none-      numeric
## CIsp      2     formattable numeric
## Spvalues  9     -none-      numeric
## Sehat     9     -none-      numeric
## CIse     18     formattable numeric

checkboxcox2


#DATA GENERATION
set.seed(123)

nx <- 100
Sx <- matrix(c(1, 0.5, 0.5, 1), nrow = 2, ncol = 2)

mux <- c(X = 10, Y = 12)
X = mvtnorm::rmvnorm(nx, mean = mux, sigma = Sx)

ny <- 100
Sy <- matrix(c(1.1, 0.6, 0.6, 1.1), nrow = 2, ncol = 2)

muy <- c(X = 11, Y = 13.7)
Y = mvtnorm::rmvnorm(ny, mean = muy, sigma = Sy)

dx = pracma::zeros(nx,1)
dy = pracma::ones(ny,1)

markers = rbind(X,Y);
marker1 = markers[,1]
marker2 = markers[,2]
D = c(rbind(dx,dy))

out=checkboxcox2(marker1, marker2, D, plots = "on")

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summary(out)
##                            Length Class  Mode    
## res_shapiro                  8    -none- list    
## transformation.parameter.1   1    -none- numeric 
## transx1                    100    -none- numeric 
## transy1                    100    -none- numeric 
## transformation.parameter.2   1    -none- numeric 
## transx2                    100    -none- numeric 
## transy2                    100    -none- numeric 
## pval_x1                      1    -none- numeric 
## pval_y1                      1    -none- numeric 
## pval_transx1                 1    -none- numeric 
## pval_transy1                 1    -none- numeric 
## pval_x2                      1    -none- numeric 
## pval_y2                      1    -none- numeric 
## pval_transx2                 1    -none- numeric 
## pval_transy2                 1    -none- numeric 
## roc1                         1    -none- function
## roc2                         1    -none- function

comparebcAUC


#GENERATE SOME BIVARIATE DATA===
set.seed(123)

nx <- 100
 Sx <- matrix(c(1,   0.5,
                0.5,  1), 
            nrow = 2, ncol = 2)

mux <- c(X = 10, Y = 12)
X=rmvnorm(nx, mean = mux, sigma = Sx)

ny <- 100
Sy <- matrix(c(1.1,   0.6,
               0.6,  1.1), 
             nrow = 2, ncol = 2)

muy <- c(X = 11, Y = 13.7)
Y=rmvnorm(ny, mean = muy, sigma = Sy)

dx=zeros(nx,1)
dy=ones(ny,1)

markers=rbind(X,Y);
marker1=markers[,1]
marker2=markers[,2]
D=c(rbind(dx,dy))

#==DATA HAVE BEEN GENERATED====


#===COMPARE THE AUCs of Marker 1 vs Maker 2

out=comparebcAUC(marker1, marker2, D, alpha=0.05,  plots="on")

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summary(out)
##                            Length Class       Mode    
## resultstable                 7    formattable numeric 
## AUCmarker1                   1    -none-      numeric 
## AUCmarker2                   1    -none-      numeric 
## pvalue_probit_difference     1    -none-      numeric 
## pvalue_difference            1    -none-      numeric 
## CI_difference                2    -none-      numeric 
## roc1                         1    -none-      function
## roc2                         1    -none-      function
## transx1                    100    -none-      numeric 
## transy1                    100    -none-      numeric 
## transformation.parameter.1   1    -none-      numeric 
## transx2                    100    -none-      numeric 
## transy2                    100    -none-      numeric 
## transformation.parameter.2   1    -none-      numeric

comparebcJ


#==DATA HAVE BEEN GENERATED====
#===COMPARE THE Js of Marker 1 vs Maker 2

out=comparebcJ(marker1, marker2, D, alpha=0.05,  plots="on")

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summary(out)
##                            Length Class       Mode    
## resultstable                 7    formattable numeric 
## J1                           1    -none-      numeric 
## J2                           1    -none-      numeric 
## pvalue_probit_difference     1    -none-      numeric 
## pvalue_difference            1    -none-      numeric 
## CI_difference                2    -none-      numeric 
## roc1                         1    -none-      function
## roc2                         1    -none-      function
## transx1                    100    -none-      numeric 
## transy1                    100    -none-      numeric 
## transformation.parameter.1   1    -none-      numeric 
## transx2                    100    -none-      numeric 
## transy2                    100    -none-      numeric 
## transformation.parameter.2   1    -none-      numeric

comparebcSens


out=comparebcSens(marker1=marker1, marker2=marker2, D=D, alpha =0.05, atSpec=0.8, plots="on")

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summary(out)
##                            Length Class       Mode    
## resultstable                 7    formattable numeric 
## Sens1                        1    -none-      numeric 
## Sens2                        1    -none-      numeric 
## pvalue_probit_difference     1    -none-      numeric 
## pvalue_difference            1    -none-      numeric 
## CI_difference                2    -none-      numeric 
## roc1                         1    -none-      function
## roc2                         1    -none-      function
## transx1                    100    -none-      numeric 
## transy1                    100    -none-      numeric 
## transformation.parameter.1   1    -none-      numeric 
## transx2                    100    -none-      numeric 
## transy2                    100    -none-      numeric 
## transformation.parameter.2   1    -none-      numeric

comparebcSpec


out=comparebcSpec(marker1=marker1, marker2=marker2, D=D, alpha =0.05, atSens=0.8, plots="on")

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summary(out)
##                            Length Class       Mode    
## resultstable                 7    formattable numeric 
## FPR1                         1    -none-      numeric 
## FPR2                         1    -none-      numeric 
## pvalue_probit_difference     1    -none-      numeric 
## pvalue_difference            1    -none-      numeric 
## CI_difference                2    -none-      numeric 
## roc1                         1    -none-      function
## roc2                         1    -none-      function
## transx1                    100    -none-      numeric 
## transy1                    100    -none-      numeric 
## transformation.parameter.1   1    -none-      numeric 
## transx2                    100    -none-      numeric 
## transy2                    100    -none-      numeric 
## transformation.parameter.2   1    -none-      numeric

threerocs


out=threerocs2(marker1, marker2, D, plots = "on")

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summary(out)
##                Length Class  Mode   
## AUC_BoxCox1    1      -none- numeric
## AUC_BoxCox2    1      -none- numeric
## AUC_Metz1      1      -none- numeric
## AUC_Metz2      1      -none- numeric
## AUC_Empirical1 1      -none- numeric
## AUC_Empirical2 1      -none- numeric

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.