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In bamdit: Bayesian Meta-Analysis of Diagnostic Test Data
#' Bayesian Meta-Analysis of Diagnostic Test Data
#'
#' Bayesian meta-analysis of diagnostic test data based on a scale mixtures
#' bivariate random-effects model.
#' This package was developed with the aim of simplifying the use of meta-analysis
#' models that up to now have demanded great statistical expertise in Bayesian meta-analysis.
#' The package implements a series of innovative statistical techniques including:
#' the BSROC (Bayesian Summary ROC) curve, the BAUC (Bayesian AUC), predictive surfaces,
#' the use of prior distributions that avoid boundary estimation problems of component
#' of variance and correlation parameters, analysis of conflict of evidence and robust
#' estimation of model parameters. In addition, the package comes with several published
#' examples of meta-analysis that can be used for illustration or further research in
#' this area.
#'
#' \tabular{ll}{
#' Package: \tab bamdit \cr
#' Type: \tab Package \cr
#' Version: \tab 3.4.2 \cr
#' Date: \tab 2024-09-20 \cr
#' License: \tab GPL (>= 2)\cr
#' LazyLoad: \tab yes\cr }
#'
#'
#' @aliases bamdit-package bamdit
"_PACKAGE"
#'
#' @author PD Dr. Pablo Emilio Verde \email{pabloemilio.verde@@hhu.de}
#'
#' @references Verde P. E. (2010). Meta-analysis of diagnostic test data: A
#' bivariate Bayesian modeling approach. Statistics in Medicine. 29(30):3088-102.
#' doi: 10.1002/sim.4055.
#'
#' @references Verde P. E. (2018). bamdit: An R Package for Bayesian Meta-Analysis
#' of Diagnostic Test Data. Journal of Statistical Software. Volume 86, issue 10, pages 1--32.
#'
#' @keywords Meta-Analysis Outliers ROC Sensitivity Specificity MCMC JAGS AUC
#'
NULL
#' Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis.
#'
#' @name skin
#'
#' @docType data
#'
#' @description This data frame contains results 70 studies investigated computer-aided diagnosis of melanoma
#'
#' @format A matrix with 70 rows and 15 columns. Each row represents a study's results, the columns are:
#' \describe{
#' \item{"TP"}{number of true positives.}
#' \item{"TN"}{number of ture negatives.}
#' \item{"FP"}{number of false positives.}
#' \item{"FN"}{number of false negative.}
#' \item{"study_ID"}{Study identification}
#' \item{"test_set_source"}{Public or propietary.}
#' \item{"method"}{Diagnostic technique used in the study: computer vision; deep learning or hardware-based.}
#' \item{"test_set_independent"}{yes or no.}
#' \item{"SAMPLE_SELECTION_BR"}{QUADAS-2, Patient selection bias.}
#' \item{"INDEX_TEST_BR"}{QUADAS-2, Index test description/application bias.}
#' \item{"REFERENCE_STANDARD_BR"}{QUADAS-2, Reference standard bias.}
#' \item{"FLOW_AND_TIMING_BR"}{QUADAS-2, Patient flow and timing bias.}
#' \item{"SAMPLE_SELECTION_AP}{QUADAS-2, Patient selection bias.}
#' \item{"INDEX_TEST_AP"}{QUADAS-2, Index test description/application bias.}
#' \item{"REFERENCE_STANDARD_AP"}{QUADAS-2, Reference standard bias.}
#' }
#'
#' @source The data were obtained from
#'
#' Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. Accuracy of Computer-Aided Diagnosis of Melanoma:
#' A Meta-analysis. JAMA Dermatol. 2019 Nov 1;155 11:1291-1299.
#' doi: 10.1001/jamadermatol.2019.1375. PMID: 31215969; PMCID: PMC6584889.
#'
#' @keywords datasets
NULL
#' Accuracy of Computer-Aided Diagnosis of Melanoma: A Comparative Meta-analysis.
#'
#' @name skin2
#'
#' @docType data
#'
#' @description This data frame contains results 14 comparative diagnostic studies: CAD vs Dermatologists
#'
#' @format A matrix with 14 rows and 12 columns. Each row represents a study's results, the columns are:
#' \describe{
#' \item{"study"}{Study name and year}
#' \item{"TP_CAD"}{number of true positives CAD.}
#' \item{"TN_CAD"}{number of ture negatives CAD.}
#' \item{"FP_CAD"}{number of false positives CAD.}
#' \item{"FN_CAD"}{number of false negative CAD.}
#' \item{"TP_Dermatologists"}{number of true positives Dermatologists.}
#' \item{"TN_Dermatologists"}{number of ture negatives Dermatologists.}
#' \item{"FP_Dermatologists"}{number of false positives Dermatologists.}
#' \item{"FN_Dermatologists"}{number of false negative Dermatologists.}
#' \item{"test_set_source"}{The database was public domain or propietary.}
#' \item{"cad_method"}{Computer-Aided Diagnostic Technique: computer vision; deep learning or hardware-based.}
#' \item{"test_set_independent"}{yes or no.}
#' }
#'
#' @source The data were obtained from
#'
#' Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. Accuracy of Computer-Aided Diagnosis of Melanoma:
#' A Meta-analysis. JAMA Dermatol. 2019 Nov 1;155 11:1291-1299.
#' doi: 10.1001/jamadermatol.2019.1375. PMID: 31215969; PMCID: PMC6584889.
#'
#' @keywords datasets
NULL
#' Systematic review which compares the accuracy of HbA1c vs
#' FPG in diabetes
#'
#' This data frame contains results of diagnostic accuraccy of 38 studies
#' which reported comparison of sensitivity and specificity between
#' HbA1c vs FPG in a population-based screening for type 2 diabetes.
#'
#' @name diabetes
#'
#' @docType data
#'
#' @description This data frame contains results of diagnostic accuracy of 38 studies which reported comparison of sensitivity and specificity between HbA1c vs FPG in a population based screening for type 2 diabetes.
#'
#' @format A data frame with 38 rows and 9 columns. Each row represents study results, the columns are:
#'
#' \describe{
#'\item{Study}{Name of the first author.}
#'\item{TP_HbA1c}{Number of true positive cases for HbA1c.}
#'\item{FP_HbA1c}{Number of false positive cases for HbA1c.}
#'\item{FN_HbA1c}{Number of false negative cases for HbA1c.}
#'\item{TN_HbA1c}{Number of true negative cases for HbA1c.}
#'\item{TP_FPG}{Number of true positive cases for FPG.}
#'\item{FP_FPG}{Number of false positive cases for FPG.}
#'\item{FN_FPG}{Number of false negative cases for FPG.}
#'\item{TN_FPG}{Number of true negative cases for FPG.}
#' }
#'
#'
#'
#' @source
#' Hoyer, A., Kuss, O. Meta-analysis for the comparison of two
#' disgnostic test: a new approach based on copulas. Stat. Med. 2018; 37:739-748
#'
#'
#'
#' @keywords datasets
#'
#'
NULL
#' Systematic reviews of clinical decision tools for acute abdominal pain
#'
#' This data frame contains results of diagnostic accuraccy of 13 studies
#' which reported comparison of sensitivity and specificity between
#' doctors using diagnostic tools vs doctors without decision tools.
#'
#'
#' @name rapt
#' @docType data
#'
#' @description This data frame corresponds to 13 clinical studies reporting the accuracy of doctors added with decision tools.
#'
#' @format A data frame with 13 rows and 13 columns. Each row represents study results, the columns are:
#' \describe{
#'\item{Author}{Name of the first author and year of publication}
#'\item{tp.dr}{Number of true positive cases for unadded doctors.}
#'\item{fp.dr}{Number of false positive cases for unadded doctors.}
#'\item{fn.dr}{Number of false negative cases for unadded doctors.}
#'\item{tn.dr}{Number of true negative cases for unadded doctors.}
#'\item{tp.tools}{Number of true positive cases for doctors with decision tools.}
#'\item{fp.tools}{Number of false positive cases for doctors with decision tools.}
#'\item{fn.tools}{Number of false negative cases for doctors with decision tools.}
#'\item{tn.tools}{Number of true negative cases for doctors with decision tools.}
#'\item{tool}{Diagnostic tool.}
#'\item{n.dr}{Total number of cases for unadded doctors.}
#'\item{n.tools}{Total number of cases for doctors with decision tools.}
#'\item{design}{Study design.}
#' }
#'
#' @references
#'
#' Health Technol Assess. 2006 Nov;10(47):1-167, iii-iv.
#' Systematic reviews of clinical decision tools for acute abdominal pain.
#' Liu JL1, Wyatt JC, Deeks JJ, Clamp S, Keen J, Verde P, Ohmann C,
#' Wellwood J, Dawes M, Altman DG.
#'
#' @source
#'
#' Health Technol Assess. 2006 Nov;10(47):1-167, iii-iv.
#' Systematic reviews of clinical decision tools for acute abdominal pain.
#' Liu JL1, Wyatt JC, Deeks JJ, Clamp S, Keen J, Verde P, Ohmann C,
#' Wellwood J, Dawes M, Altman DG.
#'
#' @keywords datasets
NULL
#' Diagnosis of appendicities with computer tomography scans
#'
#' This data frame corresponds to 51 clinical studies reporting
#' the accuracy of
#' computer tomography (CT) scans for the diagnosis of appendicities.
#'
#'
#' @name ct
#' @docType data
#'
#'
#' @description This data frame corresponds to 51 clinical studies reporting the accuracy of computer tomography (CT) scans for the diagnosis of appendicities.
#'
#'
#' @format A matrix with 51 rows and 17 columns. Each row represents study results, the columns are:
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{Author}{First author and year.}
#' \item{country}{Country: EU = 1, others/USA = 2.}
#' \item{hosp}{Type of hospital: 1 = university, 2 = others.}
#' \item{inclus}{Inclusion criteria: 1 = Suspected, 2 = appendectomy.}
#' \item{indfind}{Other CT findings included: 1 = no, 2 = yes.}
#' \item{design}{Study design: 1 = prospective, 2 = retrospective.}
#' \item{contr}{Contrast medium: 1 = no, 2 = yes.}
#' \item{localis}{Localisation: 1 = one area, 2 = more than one area.}
#' \item{child}{Children included: 1 = no, 2 = yes.}
#' \item{fup.na}{Followup: 0 = no, 1 = yes.}
#' \item{refer.na}{Valid reference: 0 = no, 1 = yes.}
#' \item{sample.na}{Sample: 0 = selected, 1= consecutive/random.}
#' \item{gender.na}{Gender, female: 0 = less than 50\%; 1 = more than 50\%.}
#' }
#'
#' @references Verde P. E. (2010). Meta-analysis of diagnostic test data: A
#' bivariate Bayesian modeling approach. \emph{Statistics in Medicine}.
#' \bold{29}, 3088-3102.
#'
#' @source The data were obtainded from
#'
#' Ohmann C, Verde PE, Gilbers T, Franke C, Fuerst G, Sauerland S,
#' Boehner H. (2006) Systematic review of CT investigation in suspected acute
#' appendicitis. \emph{Final Report; Coordination Centre for Clinical Trials,
#' Heinrich-Heine University}. Moorenstr. 5, D-40225 Duesseldorf Germany.
#'
#' @keywords datasets
NULL
#' Ectopic pregnancy vs. all other pregnancies data
#'
#' Ectopic pregnancy vs. all other pregnancies data
#' Table III Mol et al. 1998
#'
#' @name ep
#' @docType data
#'
#' @format A matrix with 21 rows and 8 columns. Each row represents study
#' results, the columns are:
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{d1}{Prospective vs. retrospective.}
#' \item{d2}{Cohort vs. case-control}
#' \item{d3}{Consecutive sampling patients series vs. non-consecutive.}
#' }
#'
#'
#' @source Table III Mol et al. 1998
#' @keywords datasets
NULL
#' Radiological evaluation of lymph node metastases in patients with cervical cancer: a
#' meta-analysis.
#'
#' This data frame summarizes the tables 1-3 of Scheidler et al. 1997.
#'
#'
#' @name scheidler
#' @docType data
#' @format A matrix with 46 rows and 7 columns. Each row represents study
#' results, the columns are:
#' \describe{
#' \item{tp}{true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{author}{first author of the study.}
#' \item{year}{publication date.}
#' \item{test}{test method used in the study.}
#' }
#'
#'
#' @references Verde P. E. (2010). Meta-analysis of diagnostic test data: A
#' bivariate Bayesian modeling approach. \emph{Statistics in Medicine}.
#' \bold{29}, 3088-3102.
#'
#' @source The data were obtained from
#'
#' Scheidler J, Hricak H, Yu KK, Subak L, Segal MR. (1997) Radiological
#' evaluation of lymph node metastases in patients with cervical cancer: a
#' meta-analysis. \emph{The Journal of the American Medical Association};
#' \bold{278}:1096-1101.
#' @keywords datasets
NULL
#' Accuracy of Positron Emission Tomography for Diagnosis of Pulmonary
#' Nodules and Mass Lesions
#'
#' Data from a Meta-Analysis of Studies Quality of FDG-PET for Diagnosis of SPNs and Mass Lesions
#'
#' @name gould
#' @docType data
#' @format A matrix with 31 rows and 6 columns. Each row represents study
#' results, the columns are:
#'
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{author}{first author of the study.}
#' \item{year}{publication date.}
#' }
#'
#'
#' @source The data were obtainded from
#'
#' Gould MK, Maclean CC, Kuschner WG, Rydzak CE, Owens Dk. (2001) Accuracy of
#' positron emission tomography for diagnosis of pulmonary nodules and mass
#' lesions: a meta-analysis. \emph{The Journal of the American Medical
#' Association};\bold{285}:914-24.
#' @keywords datasets
NULL
#' Tumor markers in the diagnosis of primary bladder cancer.
#'
#' Outcome of individual studies evaluating urine markers
#'
#'
#' @name glas
#' @docType data
#'
#' @format A matrix with 46 rows and 7 columns. Each row represents study
#' results, the columns are:
#'
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{author}{first author of the study.}
#' \item{cutoff}{cutoff in U/ml.}
#' \item{marker}{test method used in the study.}
#' }
#'
#' @references Verde P. E. (2010). Meta-analysis of diagnostic test data: A
#' bivariate Bayesian modeling approach. \emph{Statistics in Medicine}.
#' \bold{29}, 3088-3102.
#'
#' @source The data were obtained from
#'
#' Glas AS, Roos D, Deutekom M, Zwindermann AH, Bossuyt PM, Kurth KH. (2003)
#' Tumor markers in the diagnosis of primary bladder cancer. A systematic
#' review. \emph{Journal of Urology}; \bold{169}:1975-82.
#' @keywords datasets
NULL
#' Diagnosis of Intravascular Device-Related Bloodstream Infection
#'
#' Outcome of individual studies evaluating intravascular device-related
#' bloodstream infection
#'
#'
#' @name safdar05
#' @docType data
#' @format A matrix with 78 rows and 8 columns. Each row represents study
#' results, the columns are:
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{author}{first author of the study.}
#' \item{year}{publication date.}
#' \item{technique}{diagnostic technique used in the study.}
#' \item{duration}{duration of catheterization: short term or long term or both.}
#' }
#'
#'
#'
#' @source The data were obtained from
#'
#' Safdar N, Fine JP, Maki DG. (2005) Meta-analysis: methods for diagnosing
#' intravascular device-related bloodstream infection.
#' \emph{Ann Intern Med.}; \bold{142}:451-66.
#'
#' @keywords datasets
NULL
#' Diagnosis of lymph node metastasis with magnetic resonance imaging
#'
#'
#' @docType data
#' @name mri
#'
#'
#' @format A matrix with 10 rows and 4 columns. Each row represents study results, the columns are:
#' \describe{
#' \item{tp}{true positives}
#' \item{n1}{number of patients with disease}
#' \item{fp}{false positives}
#' \item{n2}{number of patients without disease}
#' }
#'
#' @source
#' The data were obtained from
#'
#' Scheidler J, Hricak H, Yu KK, Subak L, Segal MR. (1997)
#' Radiological evaluation of lymph node metastases in
#' patients with cervical cancer: a meta-analysis. \emph{The
#' Journal of the American Medical Association};
#' \bold{278}:1096-1101.
#
#'@references
#' Verde P. E. (2010). Meta-analysis of diagnostic test
#' data: A bivariate Bayesian modeling approach.
#' \emph{Statistics in Medicine}. \bold{29}, 3088-3102.
#'
#'@keywords datasets
NULL
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#' Bayesian Meta-Analysis of Diagnostic Test Data
#'
#' Bayesian meta-analysis of diagnostic test data based on a scale mixtures
#' bivariate random-effects model.
#' This package was developed with the aim of simplifying the use of meta-analysis
#' models that up to now have demanded great statistical expertise in Bayesian meta-analysis.
#' The package implements a series of innovative statistical techniques including:
#' the BSROC (Bayesian Summary ROC) curve, the BAUC (Bayesian AUC), predictive surfaces,
#' the use of prior distributions that avoid boundary estimation problems of component
#' of variance and correlation parameters, analysis of conflict of evidence and robust
#' estimation of model parameters. In addition, the package comes with several published
#' examples of meta-analysis that can be used for illustration or further research in
#' this area.
#'
#' \tabular{ll}{
#' Package: \tab bamdit \cr
#' Type: \tab Package \cr
#' Version: \tab 3.4.2 \cr
#' Date: \tab 2024-09-20 \cr
#' License: \tab GPL (>= 2)\cr
#' LazyLoad: \tab yes\cr }
#'
#'
#' @aliases bamdit-package bamdit
"_PACKAGE"
#'
#' @author PD Dr. Pablo Emilio Verde \email{pabloemilio.verde@@hhu.de}
#'
#' @references Verde P. E. (2010). Meta-analysis of diagnostic test data: A
#' bivariate Bayesian modeling approach. Statistics in Medicine. 29(30):3088-102.
#' doi: 10.1002/sim.4055.
#'
#' @references Verde P. E. (2018). bamdit: An R Package for Bayesian Meta-Analysis
#' of Diagnostic Test Data. Journal of Statistical Software. Volume 86, issue 10, pages 1--32.
#'
#' @keywords Meta-Analysis Outliers ROC Sensitivity Specificity MCMC JAGS AUC
#'
NULL
#' Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis.
#'
#' @name skin
#'
#' @docType data
#'
#' @description This data frame contains results 70 studies investigated computer-aided diagnosis of melanoma
#'
#' @format A matrix with 70 rows and 15 columns. Each row represents a study's results, the columns are:
#' \describe{
#' \item{"TP"}{number of true positives.}
#' \item{"TN"}{number of ture negatives.}
#' \item{"FP"}{number of false positives.}
#' \item{"FN"}{number of false negative.}
#' \item{"study_ID"}{Study identification}
#' \item{"test_set_source"}{Public or propietary.}
#' \item{"method"}{Diagnostic technique used in the study: computer vision; deep learning or hardware-based.}
#' \item{"test_set_independent"}{yes or no.}
#' \item{"SAMPLE_SELECTION_BR"}{QUADAS-2, Patient selection bias.}
#' \item{"INDEX_TEST_BR"}{QUADAS-2, Index test description/application bias.}
#' \item{"REFERENCE_STANDARD_BR"}{QUADAS-2, Reference standard bias.}
#' \item{"FLOW_AND_TIMING_BR"}{QUADAS-2, Patient flow and timing bias.}
#' \item{"SAMPLE_SELECTION_AP}{QUADAS-2, Patient selection bias.}
#' \item{"INDEX_TEST_AP"}{QUADAS-2, Index test description/application bias.}
#' \item{"REFERENCE_STANDARD_AP"}{QUADAS-2, Reference standard bias.}
#' }
#'
#' @source The data were obtained from
#'
#' Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. Accuracy of Computer-Aided Diagnosis of Melanoma:
#' A Meta-analysis. JAMA Dermatol. 2019 Nov 1;155 11:1291-1299.
#' doi: 10.1001/jamadermatol.2019.1375. PMID: 31215969; PMCID: PMC6584889.
#'
#' @keywords datasets
NULL
#' Accuracy of Computer-Aided Diagnosis of Melanoma: A Comparative Meta-analysis.
#'
#' @name skin2
#'
#' @docType data
#'
#' @description This data frame contains results 14 comparative diagnostic studies: CAD vs Dermatologists
#'
#' @format A matrix with 14 rows and 12 columns. Each row represents a study's results, the columns are:
#' \describe{
#' \item{"study"}{Study name and year}
#' \item{"TP_CAD"}{number of true positives CAD.}
#' \item{"TN_CAD"}{number of ture negatives CAD.}
#' \item{"FP_CAD"}{number of false positives CAD.}
#' \item{"FN_CAD"}{number of false negative CAD.}
#' \item{"TP_Dermatologists"}{number of true positives Dermatologists.}
#' \item{"TN_Dermatologists"}{number of ture negatives Dermatologists.}
#' \item{"FP_Dermatologists"}{number of false positives Dermatologists.}
#' \item{"FN_Dermatologists"}{number of false negative Dermatologists.}
#' \item{"test_set_source"}{The database was public domain or propietary.}
#' \item{"cad_method"}{Computer-Aided Diagnostic Technique: computer vision; deep learning or hardware-based.}
#' \item{"test_set_independent"}{yes or no.}
#' }
#'
#' @source The data were obtained from
#'
#' Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. Accuracy of Computer-Aided Diagnosis of Melanoma:
#' A Meta-analysis. JAMA Dermatol. 2019 Nov 1;155 11:1291-1299.
#' doi: 10.1001/jamadermatol.2019.1375. PMID: 31215969; PMCID: PMC6584889.
#'
#' @keywords datasets
NULL
#' Systematic review which compares the accuracy of HbA1c vs
#' FPG in diabetes
#'
#' This data frame contains results of diagnostic accuraccy of 38 studies
#' which reported comparison of sensitivity and specificity between
#' HbA1c vs FPG in a population-based screening for type 2 diabetes.
#'
#' @name diabetes
#'
#' @docType data
#'
#' @description This data frame contains results of diagnostic accuracy of 38 studies which reported comparison of sensitivity and specificity between HbA1c vs FPG in a population based screening for type 2 diabetes.
#'
#' @format A data frame with 38 rows and 9 columns. Each row represents study results, the columns are:
#'
#' \describe{
#'\item{Study}{Name of the first author.}
#'\item{TP_HbA1c}{Number of true positive cases for HbA1c.}
#'\item{FP_HbA1c}{Number of false positive cases for HbA1c.}
#'\item{FN_HbA1c}{Number of false negative cases for HbA1c.}
#'\item{TN_HbA1c}{Number of true negative cases for HbA1c.}
#'\item{TP_FPG}{Number of true positive cases for FPG.}
#'\item{FP_FPG}{Number of false positive cases for FPG.}
#'\item{FN_FPG}{Number of false negative cases for FPG.}
#'\item{TN_FPG}{Number of true negative cases for FPG.}
#' }
#'
#'
#'
#' @source
#' Hoyer, A., Kuss, O. Meta-analysis for the comparison of two
#' disgnostic test: a new approach based on copulas. Stat. Med. 2018; 37:739-748
#'
#'
#'
#' @keywords datasets
#'
#'
NULL
#' Systematic reviews of clinical decision tools for acute abdominal pain
#'
#' This data frame contains results of diagnostic accuraccy of 13 studies
#' which reported comparison of sensitivity and specificity between
#' doctors using diagnostic tools vs doctors without decision tools.
#'
#'
#' @name rapt
#' @docType data
#'
#' @description This data frame corresponds to 13 clinical studies reporting the accuracy of doctors added with decision tools.
#'
#' @format A data frame with 13 rows and 13 columns. Each row represents study results, the columns are:
#' \describe{
#'\item{Author}{Name of the first author and year of publication}
#'\item{tp.dr}{Number of true positive cases for unadded doctors.}
#'\item{fp.dr}{Number of false positive cases for unadded doctors.}
#'\item{fn.dr}{Number of false negative cases for unadded doctors.}
#'\item{tn.dr}{Number of true negative cases for unadded doctors.}
#'\item{tp.tools}{Number of true positive cases for doctors with decision tools.}
#'\item{fp.tools}{Number of false positive cases for doctors with decision tools.}
#'\item{fn.tools}{Number of false negative cases for doctors with decision tools.}
#'\item{tn.tools}{Number of true negative cases for doctors with decision tools.}
#'\item{tool}{Diagnostic tool.}
#'\item{n.dr}{Total number of cases for unadded doctors.}
#'\item{n.tools}{Total number of cases for doctors with decision tools.}
#'\item{design}{Study design.}
#' }
#'
#' @references
#'
#' Health Technol Assess. 2006 Nov;10(47):1-167, iii-iv.
#' Systematic reviews of clinical decision tools for acute abdominal pain.
#' Liu JL1, Wyatt JC, Deeks JJ, Clamp S, Keen J, Verde P, Ohmann C,
#' Wellwood J, Dawes M, Altman DG.
#'
#' @source
#'
#' Health Technol Assess. 2006 Nov;10(47):1-167, iii-iv.
#' Systematic reviews of clinical decision tools for acute abdominal pain.
#' Liu JL1, Wyatt JC, Deeks JJ, Clamp S, Keen J, Verde P, Ohmann C,
#' Wellwood J, Dawes M, Altman DG.
#'
#' @keywords datasets
NULL
#' Diagnosis of appendicities with computer tomography scans
#'
#' This data frame corresponds to 51 clinical studies reporting
#' the accuracy of
#' computer tomography (CT) scans for the diagnosis of appendicities.
#'
#'
#' @name ct
#' @docType data
#'
#'
#' @description This data frame corresponds to 51 clinical studies reporting the accuracy of computer tomography (CT) scans for the diagnosis of appendicities.
#'
#'
#' @format A matrix with 51 rows and 17 columns. Each row represents study results, the columns are:
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{Author}{First author and year.}
#' \item{country}{Country: EU = 1, others/USA = 2.}
#' \item{hosp}{Type of hospital: 1 = university, 2 = others.}
#' \item{inclus}{Inclusion criteria: 1 = Suspected, 2 = appendectomy.}
#' \item{indfind}{Other CT findings included: 1 = no, 2 = yes.}
#' \item{design}{Study design: 1 = prospective, 2 = retrospective.}
#' \item{contr}{Contrast medium: 1 = no, 2 = yes.}
#' \item{localis}{Localisation: 1 = one area, 2 = more than one area.}
#' \item{child}{Children included: 1 = no, 2 = yes.}
#' \item{fup.na}{Followup: 0 = no, 1 = yes.}
#' \item{refer.na}{Valid reference: 0 = no, 1 = yes.}
#' \item{sample.na}{Sample: 0 = selected, 1= consecutive/random.}
#' \item{gender.na}{Gender, female: 0 = less than 50\%; 1 = more than 50\%.}
#' }
#'
#' @references Verde P. E. (2010). Meta-analysis of diagnostic test data: A
#' bivariate Bayesian modeling approach. \emph{Statistics in Medicine}.
#' \bold{29}, 3088-3102.
#'
#' @source The data were obtainded from
#'
#' Ohmann C, Verde PE, Gilbers T, Franke C, Fuerst G, Sauerland S,
#' Boehner H. (2006) Systematic review of CT investigation in suspected acute
#' appendicitis. \emph{Final Report; Coordination Centre for Clinical Trials,
#' Heinrich-Heine University}. Moorenstr. 5, D-40225 Duesseldorf Germany.
#'
#' @keywords datasets
NULL
#' Ectopic pregnancy vs. all other pregnancies data
#'
#' Ectopic pregnancy vs. all other pregnancies data
#' Table III Mol et al. 1998
#'
#' @name ep
#' @docType data
#'
#' @format A matrix with 21 rows and 8 columns. Each row represents study
#' results, the columns are:
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{d1}{Prospective vs. retrospective.}
#' \item{d2}{Cohort vs. case-control}
#' \item{d3}{Consecutive sampling patients series vs. non-consecutive.}
#' }
#'
#'
#' @source Table III Mol et al. 1998
#' @keywords datasets
NULL
#' Radiological evaluation of lymph node metastases in patients with cervical cancer: a
#' meta-analysis.
#'
#' This data frame summarizes the tables 1-3 of Scheidler et al. 1997.
#'
#'
#' @name scheidler
#' @docType data
#' @format A matrix with 46 rows and 7 columns. Each row represents study
#' results, the columns are:
#' \describe{
#' \item{tp}{true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{author}{first author of the study.}
#' \item{year}{publication date.}
#' \item{test}{test method used in the study.}
#' }
#'
#'
#' @references Verde P. E. (2010). Meta-analysis of diagnostic test data: A
#' bivariate Bayesian modeling approach. \emph{Statistics in Medicine}.
#' \bold{29}, 3088-3102.
#'
#' @source The data were obtained from
#'
#' Scheidler J, Hricak H, Yu KK, Subak L, Segal MR. (1997) Radiological
#' evaluation of lymph node metastases in patients with cervical cancer: a
#' meta-analysis. \emph{The Journal of the American Medical Association};
#' \bold{278}:1096-1101.
#' @keywords datasets
NULL
#' Accuracy of Positron Emission Tomography for Diagnosis of Pulmonary
#' Nodules and Mass Lesions
#'
#' Data from a Meta-Analysis of Studies Quality of FDG-PET for Diagnosis of SPNs and Mass Lesions
#'
#' @name gould
#' @docType data
#' @format A matrix with 31 rows and 6 columns. Each row represents study
#' results, the columns are:
#'
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{author}{first author of the study.}
#' \item{year}{publication date.}
#' }
#'
#'
#' @source The data were obtainded from
#'
#' Gould MK, Maclean CC, Kuschner WG, Rydzak CE, Owens Dk. (2001) Accuracy of
#' positron emission tomography for diagnosis of pulmonary nodules and mass
#' lesions: a meta-analysis. \emph{The Journal of the American Medical
#' Association};\bold{285}:914-24.
#' @keywords datasets
NULL
#' Tumor markers in the diagnosis of primary bladder cancer.
#'
#' Outcome of individual studies evaluating urine markers
#'
#'
#' @name glas
#' @docType data
#'
#' @format A matrix with 46 rows and 7 columns. Each row represents study
#' results, the columns are:
#'
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{author}{first author of the study.}
#' \item{cutoff}{cutoff in U/ml.}
#' \item{marker}{test method used in the study.}
#' }
#'
#' @references Verde P. E. (2010). Meta-analysis of diagnostic test data: A
#' bivariate Bayesian modeling approach. \emph{Statistics in Medicine}.
#' \bold{29}, 3088-3102.
#'
#' @source The data were obtained from
#'
#' Glas AS, Roos D, Deutekom M, Zwindermann AH, Bossuyt PM, Kurth KH. (2003)
#' Tumor markers in the diagnosis of primary bladder cancer. A systematic
#' review. \emph{Journal of Urology}; \bold{169}:1975-82.
#' @keywords datasets
NULL
#' Diagnosis of Intravascular Device-Related Bloodstream Infection
#'
#' Outcome of individual studies evaluating intravascular device-related
#' bloodstream infection
#'
#'
#' @name safdar05
#' @docType data
#' @format A matrix with 78 rows and 8 columns. Each row represents study
#' results, the columns are:
#' \describe{
#' \item{tp}{number of true positives.}
#' \item{n1}{number of patients with disease.}
#' \item{fp}{number of false positives.}
#' \item{n2}{number of patients without disease.}
#' \item{author}{first author of the study.}
#' \item{year}{publication date.}
#' \item{technique}{diagnostic technique used in the study.}
#' \item{duration}{duration of catheterization: short term or long term or both.}
#' }
#'
#'
#'
#' @source The data were obtained from
#'
#' Safdar N, Fine JP, Maki DG. (2005) Meta-analysis: methods for diagnosing
#' intravascular device-related bloodstream infection.
#' \emph{Ann Intern Med.}; \bold{142}:451-66.
#'
#' @keywords datasets
NULL
#' Diagnosis of lymph node metastasis with magnetic resonance imaging
#'
#'
#' @docType data
#' @name mri
#'
#'
#' @format A matrix with 10 rows and 4 columns. Each row represents study results, the columns are:
#' \describe{
#' \item{tp}{true positives}
#' \item{n1}{number of patients with disease}
#' \item{fp}{false positives}
#' \item{n2}{number of patients without disease}
#' }
#'
#' @source
#' The data were obtained from
#'
#' Scheidler J, Hricak H, Yu KK, Subak L, Segal MR. (1997)
#' Radiological evaluation of lymph node metastases in
#' patients with cervical cancer: a meta-analysis. \emph{The
#' Journal of the American Medical Association};
#' \bold{278}:1096-1101.
#
#'@references
#' Verde P. E. (2010). Meta-analysis of diagnostic test
#' data: A bivariate Bayesian modeling approach.
#' \emph{Statistics in Medicine}. \bold{29}, 3088-3102.
#'
#'@keywords datasets
NULL
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