| Title: | Computational tools for meta-analysis of diagnostic accuracy test |
|---|---|
| Description: | Computational tools for meta-analysis of diagnostic accuracy test. This package enables computations of confidence interval for the AUC of summary ROC curve and some related AUC-based inference methods. |
| Authors: | Hisashi Noma |
| Maintainer: | Hisashi Noma <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.2 |
| Built: | 2025-01-22 05:57:26 UTC |
| Source: | https://github.com/nomahi/dmetatools |
Computational tools for meta-analysis of diagnostic accuracy test. This package enables computations of confidence interval for the AUC of summary ROC curve and some related AUC-based inference methods.
Hisashi Noma <[email protected]>
Noma, H. and Matsushima, Y. (2020). Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. arXiv:2004.04339. https://arxiv.org/abs/2004.04339
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)
data(asthma)data(asthma)
A data frame with 12 rows and 4 variables
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. https://doi.org/10.1016/S2213-2600(15)00050-8
Noma, H. and Matsushima, Y. (2020). Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. arXiv:2004.04339. https://arxiv.org/abs/2004.04339
Calculating the confidence interval for AUC of summary ROC curve by parametric bootstrap.
AUC_boot(TP, FP, FN, TN, B=2000, alpha=0.95)AUC_boot(TP, FP, FN, TN, B=2000, alpha=0.95)
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 (defalt: 2000) |
alpha |
The confidence level (default: 0.95) |
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).
Hisashi Noma <[email protected]>
Noma, H. and Matsushima, Y. (2020). Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. arXiv:2004.04339. https://arxiv.org/abs/2004.04339
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$TN) AUC_boot(LAG$TP,LAG$FP,LAG$FN,LAG$TN) AUC_boot(MRI$TP,MRI$FP,MRI$FN,MRI$TN)data(cervical) CT <- cervical[cervical$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) AUC_boot(LAG$TP,LAG$FP,LAG$FN,LAG$TN) AUC_boot(MRI$TP,MRI$FP,MRI$FN,MRI$TN)
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.
AUC_comparison(TP1, FP1, FN1, TN1, TP2, FP2, FN2, TN2, B=2000, alpha=0.05)AUC_comparison(TP1, FP1, FN1, TN1, TP2, FP2, FN2, TN2, B=2000, alpha=0.05)
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 (defalt: 2000) |
alpha |
The significance level (default: 0.05) |
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.
Hisashi Noma <[email protected]>
Noma, H. and Matsushima, Y. (2020). Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. arXiv:2004.04339. https://arxiv.org/abs/2004.04339
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$TN) # Comparison of the AUCs AUC_comparison(MRI$TP,MRI$FP,MRI$FN,MRI$TN,LAG$TP,LAG$FP,LAG$FN,LAG$TN) AUC_comparison(MRI$TP,MRI$FP,MRI$FN,MRI$TN,CT$TP,CT$FP,CT$FN,CT$TN)data(cervical) CT <- cervical[cervical$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) # Comparison of the AUCs AUC_comparison(MRI$TP,MRI$FP,MRI$FN,MRI$TN,LAG$TP,LAG$FP,LAG$FN,LAG$TN) AUC_comparison(MRI$TP,MRI$FP,MRI$FN,MRI$TN,CT$TP,CT$FP,CT$FN,CT$TN)
Influence diagnostics based on AUC of the summary ROC curve by leave-one-out analysis. The threthold to determine influential outlying study is computed by parametric bootstrap.
AUC_IF(TP, FP, FN, TN, B=2000, alpha=0.95, Cores=detectCores())AUC_IF(TP, FP, FN, TN, B=2000, alpha=0.95, Cores=detectCores())
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 (defalt: 2000) |
alpha |
The error level to be calculated for the bootstrap interval of |
Cores |
The number of cores to be used for parallel computations (if the user don't want to implement parallel computations, please set to 1). |
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).
Hisashi Noma <[email protected]>
Noma, H. and Matsushima, Y. (2020). Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. arXiv:2004.04339. https://arxiv.org/abs/2004.04339
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) # Influential analysis based on the AUC detach(asthma)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) # Influential analysis based on the AUC detach(asthma)
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)
data(cervical)data(cervical)
A data frame with 44 rows and 8 variables
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. https://jamanetwork.com/journals/jama/fullarticle/vol/278/pg/1096
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. https://doi.org/10.1016/j.jclinepi.2005.02.022
Noma, H. and Matsushima, Y. (2020). Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. arXiv:2004.04339. https://arxiv.org/abs/2004.04339