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-any)mada_0.5.12.zip(r-4.6-any)mada_0.5.12.zip(r-4.5-any)
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✨
DESCRIPTION
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:

6.41 score 3 packages 98 scripts 2.9k downloads 78 mentions 31 exports 13 dependencies

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

Target	Result	Time
linux-devel	OK	157
source / vignettes	OK	170
linux-release	OK	141
macos-release	OK	114
macos-oldrel	OK	109
windows-devel	OK	91
windows-release	OK	67
windows-oldrel	OK	77
wasm-release	OK	100

Exports:AUC CIrho cochran.Q crosshair forest forestmada fpr madad madauni mcsroc mslSROC phm predv_d predv_r print.madad print.madauni print.predv_d print.predv_r print.summary.madauni reitsma ROCellipse rsSROC sens spec sroc summary.madauni summary.predv_d summary.predv_r SummaryPts talpha vcov.madauni

Dependencies:digest ellipse lattice mathjaxr Matrix metadat metafor mixmeta mvmeta mvtnorm nlme numDeriv pbapply

Meta-Analysis of Diagnostic Accuracy with mada

Rendered frommada.Rnwusingutils::Sweave

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Meta-Analysis of diagnostic accuracy studies mada	mada-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 statistic	cochran.Q
Crosshair plot	crosshair crosshair.default
Forest plot for univariate measures	forest forest.madad forest.madauni forestmada
Diagnostic accuracy data	AuditC Dementia IAQ mada-data SAQ skin_tests smoking
Descriptive statistics for meta-analysis of diagnostic accuracy	madad madad-class print.madad
Meta-Analyisis of univariate measures of diagnostic accuracy	madauni
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 curve	mslSROC
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 Specificities	predv_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 Specificities	predv_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 space	ROCellipse ROCellipse.default
Plot the Ruecker-Schumacher (2010) SROC curve	rsSROC
Sensitivity, Specificity and False Positive Rate	fpr 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 model	print.SummaryPts summary.SummaryPts SummaryPts SummaryPts.default SummaryPts.reitsma
The t_alpha transformation as a link function for binary GLMs.	talpha