Nothing
#' bnma: A package for network meta analysis using Bayesian methods
#'
#' A package for running Bayesian network meta analysis
#'
#' Network meta-analysis or mixed treatment comparison (MTC) is a method that allows simultaneous comparison of more than two treatments.
#' We use a Bayesian approach to combine both direct and indirect evidence as in Dias et al. 2013a.
#' This package is a user friendly application that can run network meta analysis models without having to code a JAGS model.
#' The program takes the input data and transforms it to a suitable format of analysis, generates a JAGS model and reasonable
#' initial values and runs the model through the rjags package.
#' The focus of this package was inclusion of multinomial response and various options for adding covariates and/or baseline risks effects.
#' Also, while sampling, the package uses Gelman-Rubin convergence criteria to decide whether to continue sampling or not.
#' Furthermore, package includes different models such as contrast based models and unrelated mean effects (UME) model and nodesplitting model to test for inconsistency.
#'
#' @docType package
#' @name bnma-package
#' @references A.J. Franchini, S. Dias, A.E. Ades, J.P. Jansen, N.J. Welton (2012), \emph{Accounting for correlation in network meta-analysis with multi-arm trials}, Research Synthesis Methods 3(2):142-160. \doi{10.1002/jrsm.1049}
#' @references S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013a), \emph{A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials}, Medical Decision Making 33(5):607-617. \doi{10.1177/0272989X12458724}
#' @references S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013b), \emph{Heterogeneity-Subgroups, Meta-Regression, Bias, and Bias-Adjustment}, Medical Decision Making 33(5):618-640. \doi{10.1177/0272989X13485157}
#' @references S. Dias, N.J. Welton, D.M. Caldwellb, A.E. Ades (2010), \emph{Checking consistency in mixed treatment}, Statistics in Medicine 29(7-8, Sp. Iss. SI): 932-944. \doi{10.1002/sim.3767}
#' @references S. Dias, N.J. Welton, A.J. Sutton, D.M. Caldwell, G. Lu, and A.E. Ades (2013), \emph{Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials}, Medical Decision Making 33(5):641-656. \doi{10.1177/0272989X12455847}
#' @references C.H. Schmid, T.A. Trikalinos, I. Olkin (2014), \emph{Bayesian network meta-analysis for unordered categorical outcomes with incomplete data}, Research Synthesis Methods 5(2):162-185. \doi{10.1002/jrsm.1103}
#' @references A. Gelman, D.B. Rubin (1992), \emph{Inference from iterative simulation using multiple sequences}, Statistical Science 7(4):457-472. \doi{10.1214/ss/1177011136}
#' @references D.J. Spiegelhalter, N.G. Best, and B.P. Carlin (1998), \emph{Bayesian deviance, the effective nunmber of parameters, and the comparison of arbitrarily complex models}, Technical report, MRC Biostatistics Unit, Cambridge, UK.
#' @references F.A. Achana, N.J. Cooper, S. Dias, G. Lu, S.J.C. Rice, D. Kendrick, A.J. Sutton (2012), \emph{Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis}, Statistics in Medicine 32(5):752-771. \doi{10.1002/sim.5539}
#' @references F.A. Achana, N.J. Cooper, S. Bujkiewicz, S.J. Hubbard, D. Kendrick, D.R. Jones, A.J. Sutton (2014), \emph{Network meta-analysis of multiple outcomes measures accounting for borrowing of information across outcomes}, BMC Medical Research Methodology 14:92. \doi{10.1186/1471-2288-14-92}
#' @references G. Salanti, A.E. Ades, J.P.A. Ioannidisa (2011), \emph{Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial}, Journal of Clinical Epidemiology 64(2):163-171. \doi{10.1016/j.jclinepi.2010.03.016}
#' @references G. van Valkenhoef, G. Lu, B. de Brock, H. Hillege, A.E. Ades, and N.J. Welton (2012), \emph{Automating network meta-analysis}, Research Synthesis Methods 3(4):285-299. \doi{10.1002/jrsm.1054}
#' @references N.J. Cooper, A.J. Sutton, D. Morris, A.E. Ades, N.J. Welton (2009), \emph{Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: Application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation}, Statistics in Medicine 28:1861-1881. \doi{10.1002/sim.3594}
#' @references W. Viechtbauer (2010), \emph{Conducting meta-analyses in R with the metafor package}, Journal of Statistical Software, 36(3):1-48. \doi{10.18637/jss.v036.i03}
#' @seealso \code{\link{network.data}}, \code{\link{network.run}}
NULL
#' Beta blockers to prevent mortality after myocardial infarction
#'
#' A dataset of 22 trials investigating beta blockers versus control to prevent mortality after myocardial
#' infarction. Control is coded as 1 and beta blocker treatment is coded as 2.
#' @references S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013a), \emph{A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials}, Medical Decision Making 33(5):607-617. \doi{10.1177/0272989X12458724}
#' @format A list of Outcomes, Treat, Study, and N.
"blocker"
#' Trials of certolizumab pegol (CZP) for the treatment of rheumatoid arthritis in patients
#'
#' A dataset of 12 trials for investigating CZP for the treatment for those who had failed on disease-modifying antirheumatic drugs, including methotrexate (MTX).
#' Data provides the number of patients who have improved and there are 6 different treatments with placebo. Mean disease duration (years) is provided as a covariate.
#'
#' @references S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013b), \emph{Heterogeneity-Subgroups, Meta-Regression, Bias, and Bias-Adjustment}, Medical Decision Making 33(5):618-640. \doi{10.1177/0272989X13485157}
#' @format A list of Outcomes, Treat, Study, N, covariate, and Treat.order
"certolizumab"
#' Trials of statins for cholesterol lowering vs. placebo or usual care
#'
#' A dataset of 19 trials of statins for cholesterol lowering vs. placebo.
#' Each trial has a subgroup indicator for primary prevention (patients included had no previous heart disease) or
#' secondary prevention (patients had previous heart disease). Dummy variable is coded such that covariate is equal to 1
#' if a study is a secondary prevention study and 0 if a study is a primary prevention study.
#'
#' @references S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013b), \emph{Heterogeneity-Subgroups, Meta-Regression, Bias, and Bias-Adjustment}, Medical Decision Making 33(5):618-640. \doi{10.1177/0272989X13485157}
#' @format A list of Outcomes, Treat, Study, N, covariate, and Treat.order
"statins"
#' Dopamine agonists as adjunct therapy in Parkinson's disease
#'
#' A dataset of 7 studies investigating the mean lost work-time reduction in patients given
#' 4 dopamine agonists and placebo as adjunct therapy for Parkinson's disease.
#' There is placebo and four active drugs coded 2 to 5.
#'
#' @references S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013a), \emph{A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials}, Medical Decision Making 33(5):607-617. \doi{10.1177/0272989X12458724}
#' @format A list of Outcomes, Treat, Study, N, covariate, and Treat.order
"parkinsons"
#' Dopamine agonists as adjunct therapy in Parkinson's disease
#'
#' A contrast level (i.e. treatment difference) dataset of 7 studies investigating the mean lost work-time reduction in patients given
#' 4 dopamine agonists and placebo as adjunct therapy for Parkinson's disease. Placebo is coded as 1, and four active drugs are coded 2 to 5.
#' There is placebo, coded as 1, and four active drugs coded 2 to 5.
#'
#' @references S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013a), \emph{A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials}, Medical Decision Making 33(5):607-617. \doi{10.1177/0272989X12458724}
#' @format A list of Outcomes, Treat, SE, na, and V
"parkinsons_contrast"
#' Trials of low dose and high dose statins for cardiovascular disease vs. placebo
#'
#' A dataset of 17 studies investigating dosage of statin for cardiovascular disease.
#' There are two treatments and a placebo. High dose statin is coded as 3, low dose statin as 2, and placebo is coded as 1 and treated as a baseline treatment.
#' Outcomes are reported as three mutually exclusive unordered outcomes.
#' First column of the outcome is the patients who are still alive (ALIVE). Second column is fatal non-cardiovascular disease (FnCVD).
#' And, the last column is fatal cardiovascular disease (FCVD).
#'
#' @references C.H. Schmid, T.A. Trikalinos, I. Olkin (2014), \emph{Bayesian network meta-analysis for unordered categorical outcomes with incomplete data}, Research Synthesis Methods 5(2):162-185. \doi{10.1002/jrsm.1103}
#' @format A list of Outcomes, Treat, Study, and N
"cardiovascular"
#' Smoking cessation counseling programs
#'
#' Twenty-four studies, including 2 three-arm trials, compared 4 smoking cessation counseling programs and recorded the number of
#' individuals with successful smoking cessation at 6 to 12 month. Counseling programs include 1 = no intervention, 2 = self-help,
#' 3 = individual counseling, and 4 = group counseling.
#'
#' @references S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013a), \emph{A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials}, Medical Decision Making 33(5):607-617. \doi{10.1177/0272989X12458724}
#' @format A list of Outcomes, Treat, Study, and N
"smoking"
#' Thrombolytic drugs and percutaneous transluminal coronary angioplasty
#'
#' A dataset consisting of 50 trials comparing 8 thrombolytic drugs and percutaneous transluminal coronary angioplasty, following
#' acute myocardial infarction. Data consist of the number of deaths in 30 or 35 days and the number of patients in each treatment arm.
#' There are 9 treatments in total: streptokinase (1), alteplase (2), accelerated alteplase (3), streptokinase + alteplase (4),
#' reteplase (5), tenecteplase (6), percutaneous transluminal coronary angioplasty (7), urokinase (8), anistreptilase (9)
#'
#' @references S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013a), \emph{A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials}, Medical Decision Making 33(5):607-617. \doi{10.1177/0272989X12458724}
#' @format A list of Outcomes, Treat, Study, and N
"thrombolytic"
#' @importFrom graphics axis legend lines mtext plot points title abline curve text
NULL
#' @importFrom stats coef end lm quantile rchisq rnorm sd start window aggregate
NULL
#' @importFrom utils combn
NULL
#' @import coda
NULL
#' @import ggplot2
NULL
#' @import grid
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.