Help for package dmetatools

Type:

Package

Title:

Computational Tools for Meta-Analysis of Diagnostic Accuracy Test

Version:

1.1.1

Date:

2023-05-27

Maintainer:

Hisashi Noma <noma@ism.ac.jp>

Description:

Computational tools for meta-analysis of diagnostic accuracy test. Bootstrap-based computational methods of the confidence interval for AUC of summary ROC curve and some related AUC-based inference methods are available (Noma et al. (2021) <doi:10.1080/23737484.2021.1894408>).

Imports:

MASS, mada

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

NeedsCompilation:

Packaged:

2023-05-30 05:07:12 UTC; Hisashi

Author:

Hisashi Noma

[aut, cre]

Repository:

CRAN

Date/Publication:

2023-05-30 10:50:10 UTC

The 'dmetatools' package.

Description

Author(s)

Hisashi Noma <noma@ism.ac.jp>

References

Noma, H., Matsushima, Y., and Ishii, R. (2021). Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. Communications in Statistics: Case Studies and Data Analysis 7: 344-358. doi:10.1080/23737484.2021.1894408

Influence diagnostics based on the AUC of summary ROC curve

Description

Influence diagnostics based on AUC of the summary ROC curve by leave-one-out analysis. The threshold to determine influential outlying study is computed by parametric bootstrap.

Usage

AUC_IF(TP, FP, FN, TN, B=2000, alpha=0.95)

Arguments

TP

A vector of the number of true positives (TP)

FP

A vector of the number of false positives (FP)

FN

A vector of the number of false negatives (FN)

TN

A vector of the number of true negatives (TN)

B

The number of bootstrap resampling (default: 2000)

alpha

The error level to be calculated for the bootstrap interval of deltaAUC (default: 0.95)

Value

Influence diagnostic statistics based on the AUC of the summary ROC curve. The output is sorted by the absolute size of deltaAUC.

AUC: The AUC of the summary ROC curve.
id: identification number.
AUC(-i): The AUC estimate if ith study is excluded.
deltaAUC: The difference of AUC estimates for all study and for the subpopulation that ith study is excluded.
Q1: Default 2.5th percentile of the bootstrap distribution of deltaAUC (can be changed by alpha).
Q2: Default 97.5th percentile of the bootstrap distribution of deltaAUC (can be changed by alpha).

Author(s)

Hisashi Noma <noma@ism.ac.jp>

References

Examples

require(mada)

data(asthma)

fit1 <- reitsma(asthma)    # DTA analysis using the Reitsma model
summary(fit1)

plot(fit1)		# Plot the SROC curves
points(fpr(asthma), sens(asthma), cex = .5)

attach(asthma)
AUC_IF(TP, FP, FN, TN, B=2)    # Influential analysis based on the AUC
detach(asthma)
# This is an example command for illustration. B should be >= 1000.

Confidence interval for AUC of summary ROC curve

Description

Calculating the confidence interval for AUC of summary ROC curve by parametric bootstrap.

Usage

AUC_boot(TP, FP, FN, TN, B=2000, alpha=0.95)

Arguments

TP

A vector of the number of true positives (TP)

FP

A vector of the number of false positives (FP)

FN

A vector of the number of false negatives (FN)

TN

A vector of the number of true negatives (TN)

B

The number of bootstrap resampling (default: 2000)

alpha

The confidence level (default: 0.95)

Value

The confidence interval for AUC of summary ROC curve is calculated.

AUC: The AUC of the summary ROC curve.
AUC_CI: The 95% confidence interval for the AUC of the summary ROC curve (the confidence level can be changed by alpha).

Author(s)

Hisashi Noma <noma@ism.ac.jp>

References

Examples

require(mada)

data(cervical)

CT <- cervical[cervical$method==1,]
LAG <- cervical[cervical$method==2,]
MRI <- cervical[cervical$method==3,]

fit1 <- reitsma(CT)    # DTA meta-analysis using the Reitsma model
summary(fit1)
fit2 <- reitsma(LAG)
summary(fit2)
fit3 <- reitsma(MRI)
summary(fit3)

plot(fit1)    # Plot the SROC curves
lines(sroc(fit2), lty=2, col="blue")
ROCellipse(fit2, lty=2, pch=2, add=TRUE, col="blue")
lines(sroc(fit3), lty=3, col="red")
ROCellipse(fit3, lty=3, pch=3, add=TRUE, col="red")
points(fpr(CT), sens(CT), cex = .5)
points(fpr(LAG), sens(LAG), pch = 2, cex = 0.5, col="blue")
points(fpr(MRI), sens(MRI), pch = 3, cex = 0.5, col="red")
legend("bottomright", c("CT", "LAG", "MRI"), pch = 1:3, lty = 1:3, col=c("black","blue","red"))

AUC_boot(CT$TP,CT$FP,CT$FN,CT$TN,B=5)
AUC_boot(LAG$TP,LAG$FP,LAG$FN,LAG$TN,B=5)
AUC_boot(MRI$TP,MRI$FP,MRI$FN,MRI$TN,B=5)
# These are example commands for illustration. B should be >= 1000.

Bootstrap test for the difference of AUCs of summary ROC curves for multiple diagnostic tests

Description

Calculating the difference of AUCs of summary ROC curves (dAUC) and its confidence interval, and the p-value for the test of "dAUC=0" by parametric bootstrap.

Usage

AUC_comparison(TP1, FP1, FN1, TN1, TP2, FP2, FN2, TN2, B=2000, alpha=0.05)

Arguments

TP1

A vector of the number of true positives (TP) of test 1

FP1

A vector of the number of false positives (FP) of test 1

FN1

A vector of the number of false negatives (FN) of test 1

TN1

A vector of the number of true negatives (TN) of test 1

TP2

A vector of the number of true positives (TP) of test 2

FP2

A vector of the number of false positives (FP) of test 2

FN2

A vector of the number of false negatives (FN) of test 2

TN2

A vector of the number of true negatives (TN) of test 2

B

The number of bootstrap resampling (default: 2000)

alpha

The significance level (default: 0.05)

Value

The AUCs of the summary ROC curves and their confidence intervals are calculated. Also, the difference of the AUCs (dAUC) and its confidence interval, and the p-value for the test of "dAUC=0" are provided.

AUC1: The AUC of the summary ROC curve for test 1.
AUC1_CI: The 95% confidence interval for the AUC of the summary ROC curve for test 1 (the confidence level can be changed by alpha).
AUC2: The AUC of the summary ROC curve for test 2.
AUC2_CI: The 95% confidence interval for the AUC of the summary ROC curve for test 2 (the confidence level can be changed by alpha).
dAUC: The difference of the AUC1 and AUC2.
dAUC_CI: The 95% confidence interval for dAUC (the confidence level can be changed by alpha).
pvalue: The p-value of the test of dAUC=0.

Author(s)

Hisashi Noma <noma@ism.ac.jp>

References

Examples

require(mada)

data(cervical)

CT <- cervical[cervical$method==1,]
LAG <- cervical[cervical$method==2,]
MRI <- cervical[cervical$method==3,]

fit1 <- reitsma(CT)    # DTA meta-analysis using the Reitsma model
summary(fit1)
fit2 <- reitsma(LAG)
summary(fit2)
fit3 <- reitsma(MRI)
summary(fit3)

plot(fit1)    # Plot the SROC curves
lines(sroc(fit2), lty=2, col="blue")
ROCellipse(fit2, lty=2, pch=2, add=TRUE, col="blue")
lines(sroc(fit3), lty=3, col="red")
ROCellipse(fit3, lty=3, pch=3, add=TRUE, col="red")
points(fpr(CT), sens(CT), cex = .5)
points(fpr(LAG), sens(LAG), pch = 2, cex = 0.5, col="blue")
points(fpr(MRI), sens(MRI), pch = 3, cex = 0.5, col="red")
legend("bottomright", c("CT", "LAG", "MRI"), pch = 1:3, lty = 1:3, col=c("black","blue","red"))

AUC_comparison(CT$TP,CT$FP,CT$FN,CT$TN,LAG$TP,LAG$FP,LAG$FN,LAG$TN,B=5)
AUC_comparison(MRI$TP,MRI$FP,MRI$FN,MRI$TN,LAG$TP,LAG$FP,LAG$FN,LAG$TN,B=5)
AUC_comparison(MRI$TP,MRI$FP,MRI$FN,MRI$TN,CT$TP,CT$FP,CT$FN,CT$TN,B=5)
# These are example commands for illustration. B should be >= 1000.

Korevaar et al. (2015)'s data of minimally invasive markers for detection of airway eosinophilia in asthma

Description

TP: A vector of the number of true positives (TP)
FP: A vector of the number of false positives (FP)
FN: A vector of the number of false negatives (FN)
TN: A vector of the number of true negatives (TN)

Usage

data(asthma)

Format

A data frame with 12 rows and 4 variables

References

Korevaar, D. A., Westerhof, G. A., Wang, J., et al. (2015). Diagnostic accuracy of minimally invasive markers for detection of airway eosinophilia in asthma: a systematic review and meta-analysis. Lancet Respiratory Medicine. 3: 290-300. doi:10.1016/S2213-2600(15)00050-8

Scheidler et al. (1997)'s cervical cancer data

Description

id: identification number
author: The first author name of the corresponding study
year: The published year of the corresponding study
method: The diagnostic method; 1=CT (computed tomography), 2=LAG (lymphangiography), 3=MRI (magnetic resonance imaging)
TP: A vector of the number of true positives (TP)
FP: A vector of the number of false positives (FP)
FN: A vector of the number of false negatives (FN)
TN: A vector of the number of true negatives (TN)

Usage

data(cervical)

Format

A data frame with 44 rows and 8 variables

References

Scheidler, J., Hricak, H., Yu, K. K., Subak, L., and Segal, M. R. (1997). Radiological evaluation of lymph node metastases in patients with cervical cancer. A meta-analysis. JAMA 278: 1096-1101.

Reitsma, J. B., Glas, A. S., Rutjes, A. W., Scholten, R. J., Bossuyt, P. M., and Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology 58: 982-990. doi:10.1016/j.jclinepi.2005.02.022