Package: mada 0.5.12

Philipp Doebler

mada: Meta-Analysis of Diagnostic Accuracy

Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter & Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.

Authors:Philipp Doebler [aut, cre], Bernado Sousa-Pinto [ctb]

mada_0.5.12.tar.gz
mada_0.5.12.zip(r-4.7)mada_0.5.12.zip(r-4.6)mada_0.5.12.zip(r-4.5)
mada_0.5.12.tgz(r-4.6-any)mada_0.5.12.tgz(r-4.5-any)
mada_0.5.12.tar.gz(r-4.7-any)mada_0.5.12.tar.gz(r-4.6-any)
mada_0.5.12.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mada/json (API)

# Install 'mada' in R:
install.packages('mada', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://r-forge.r-project.org/projects/mada

Datasets:
  • AuditC - Diagnostic accuracy data
  • Dementia - Diagnostic accuracy data
  • IAQ - Diagnostic accuracy data
  • SAQ - Diagnostic accuracy data
  • skin_tests - Diagnostic accuracy data
  • smoking - Diagnostic accuracy data

On CRAN:

Conda:

6.63 score 3 packages 93 scripts 5.2k downloads 78 mentions 31 exports 13 dependencies

Last updated from:5c98495b8c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK129
source / vignettesOK163
linux-release-x86_64OK134
macos-release-arm64OK107
macos-oldrel-arm64OK102
windows-develOK97
windows-releaseOK108
windows-oldrelOK96
wasm-releaseOK103

Exports:AUCCIrhocochran.QcrosshairforestforestmadafprmadadmadaunimcsrocmslSROCphmpredv_dpredv_rprint.madadprint.madauniprint.predv_dprint.predv_rprint.summary.madaunireitsmaROCellipsersSROCsensspecsrocsummary.madaunisummary.predv_dsummary.predv_rSummaryPtstalphavcov.madauni

Dependencies:digestellipselatticemathjaxrMatrixmetadatmetaformixmetamvmetamvtnormnlmenumDerivpbapply

Meta-Analysis of Diagnostic Accuracy with mada

Rendered frommada.Rnwusingutils::Sweaveon May 21 2026.

Last update: 2022-07-11
Started: 2013-12-11

Readme and manuals

Help Manual

Help pageTopics
Meta-Analysis of diagnostic accuracy studies madamada-package mada
Area under the curve (AUC)AUC auc AUC.default AUC.phm AUC.reitsma
Confidence intervals for Spearman's rho.CIrho
Cochran's Q statisticcochran.Q
Crosshair plotcrosshair crosshair.default
Forest plot for univariate measuresforest forest.madad forest.madauni forestmada
Diagnostic accuracy dataAuditC Dementia IAQ mada-data SAQ skin_tests smoking
Descriptive statistics for meta-analysis of diagnostic accuracymadad madad-class print.madad
Meta-Analyisis of univariate measures of diagnostic accuracymadauni
Methods for the class 'madauni'.madauni-class print.madauni print.summary.madauni summary.madauni summary.madauni-class vcov.madauni
Plot the Moses-Shapiro-Littenberg SROC curvemslSROC
Diagnostic Meta-Analysis with the proportional hazards model approach of Holling et.al (2012)phm phm.default
Methods for 'phm' objects.phm-class plot.phm print.phm sroc.phm summary.phm
Estimation of Distributions of Predictive Values Based on Prevalence Probability Distributions and Pooled Sensitivities and Specificitiespredv_d
Methods for the class 'predv_d'.predv_d-class print.predv_d summary.predv_d summary.predv_d-class
Estimation of Distributions of Predictive Values Based on Prevalence Ranges and Pooled Sensitivities and Specificitiespredv_r
Methods for the class 'predv_r'.predv_r-class print.predv_r summary.predv_r summary.predv_r-class
Fit the bivariate model of Reitsma et al. (2005) and extensions.reitsma reitsma.default
Methods for 'reitsma' objects.anova.reitsma crosshair.reitsma mcsroc mcsroc.reitsma plot.reitsma print.anova.reitsma print.reitsma reitsma-class ROCellipse.reitsma sroc sroc.reitsma summary.reitsma
Confidence Regions on ROC spaceROCellipse ROCellipse.default
Plot the Ruecker-Schumacher (2010) SROC curversSROC
Sensitivity, Specificity and False Positive Ratefpr sens spec
Use the Zwindermann & Bossuyt (2008) MCMC procedure to generate summary points (positive and negative likelihood ratio, diagnostic odds ratio) for the Reitsma et al. (2005) bivariate modelprint.SummaryPts summary.SummaryPts SummaryPts SummaryPts.default SummaryPts.reitsma
The t_alpha transformation as a link function for binary GLMs.talpha