README.md

In chstock/DTComPair: Comparison of Binary Diagnostic Tests in a Paired Study Design

DTComPair

Comparison of the accuracy of two binary diagnostic tests in a “paired” study design, i.e. when each test is applied to each subject in the study.

Installation

CRAN

You can install the current stable version from CRAN with:

install.packages("DTComPair")

GitHub

You can install the current development version from GitHub with:

if (!require("remotes")) {install.packages("remotes")}
remotes::install_github("chstock/DTComPair")

Scope

Diagnostic accuracy measures that can be computed and compared are sensitivity, specificity, positive and negative predictive values, and positive and negative diagnostic likelihood ratios.

Getting Started

Determine the accuracy of one diagnostic test

library(DTComPair)
# Loading required package: PropCIs

data(Paired1) # Hypothetical study data
a1 <- tab.1test(d=d, y=y1, data=Paired1)
print(a1)
# Binary diagnostic test 'y1'
# 
#           Diseased Non-diseased Total
# Test pos.      397           84   481
# Test neg.       54          177   231
# Total          451          261   712

a1 |> acc.1test(method.ci = "waldci") # default Wald intervals
# Diagnostic accuracy of test 'y1'
# 
# (Estimates, standard errors and 95%-confidence intervals)
# 
#                  Est.         SE  Lower CL  Upper CL
# Sensitivity 0.8802661 0.01528718 0.8503038 0.9102284
# Specificity 0.6781609 0.02891782 0.6214830 0.7348388
# PPV         0.8253638 0.01731081 0.7914353 0.8592924
# NPV         0.7662338 0.02784617 0.7116563 0.8208113
# 
#            Est.  SE (log)  Lower CL  Upper CL
# PDLR  2.7351124 0.0915147 2.2860079 3.2724472
# NDLR  0.1765568 0.1346088 0.1356142 0.2298601

a1 |> acc.1test(method.ci = "exactci") # Clopper-Pearson intervals
# Diagnostic accuracy of test 'y1'
# 
# (Estimates, standard errors and 95%-confidence intervals)
# 
#                  Est. SE  Lower CL  Upper CL
# Sensitivity 0.8802661 NA 0.8466745 0.9087592
# Specificity 0.6781609 NA 0.6177792 0.7344406
# PPV         0.8253638 NA 0.7884240 0.8582440
# NPV         0.7662338 NA 0.7062587 0.8192389
# 
#            Est.  SE (log)  Lower CL  Upper CL
# PDLR  2.7351124 0.0915147 2.2860079 3.2724472
# NDLR  0.1765568 0.1346088 0.1356142 0.2298601

Compare the accuracy of two diagnostic tests

Compute accuracy measures

b1 <- tab.paired(d = d, y1 = y1, y2 = y2, data = Paired1)
print(b1)
# 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

b1 |> acc.paired(method.ci = "scoreci") # Wilson intervals
# Diagnostic accuracy of test 'y1'
# 
# (Estimates, standard errors and 95%-confidence intervals)
# 
#                  Est. SE Lower CL Upper CL
# Sensitivity 0.8802661 NA   0.8470   0.9071
# Specificity 0.6781609 NA   0.6193   0.7319
# PPV         0.8253638 NA   0.7889   0.8567
# NPV         0.7662338 NA   0.7076   0.8162
# 
#            Est.  SE (log)  Lower CL  Upper CL
# PDLR  2.7351124 0.0915147 2.2860079 3.2724472
# NDLR  0.1765568 0.1346088 0.1356142 0.2298601
# 
# ----------------------------------------------------------
# Diagnostic accuracy of test 'y2'
# 
# (Estimates, standard errors and 95%-confidence intervals)
# 
#                  Est. SE Lower CL Upper CL
# Sensitivity 0.7560976 NA   0.7144   0.7935
# Specificity 0.7969349 NA   0.7440   0.8413
# PPV         0.8654822 NA   0.8282   0.8957
# NPV         0.6540881 NA   0.6002   0.7042
# 
#            Est.  SE (log)  Lower CL  Upper CL
# PDLR  3.7234238 0.1255060 2.9114648 4.7618247
# NDLR  0.3060507 0.0885996 0.2572629 0.3640906

Compare predictive values

Test based on weighted generalized score statistic:

pv.wgs(b1)  
# $ppv
#          test1          test2           diff test.statistic        p.value 
#     0.82536383     0.86548223     0.04011841     5.46588745     0.01939120 
# 
# $npv
#          test1          test2           diff test.statistic        p.value 
#   7.662338e-01   6.540881e-01  -1.121457e-01   1.653540e+01   4.775012e-05 
# 
# $method
# [1] "weighted generalized score statistic (wgs)"

Estimation and test of relative predictive values:

pv.rpv(b1)
# $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

chstock/DTComPair documentation built on Sept. 27, 2024, 3:35 a.m.