Package index • rnmamod

All functions

balloon_plot(): Enhanced balloon plot

baseline_model(): The baseline model for binary outcome

bland_altman_plot(): The Bland-Altman plot

comp_clustering(): End-user-ready results for comparison dissimilarity and hierarchical clustering (Comparisons' comparability for transitivity evaluation)

data_preparation(): Prepare the dataset in the proper format for R2jags

dendro_heatmap(): Dendrogram with amalgamated heatmap (Comparisons' comparability for transitivity evaluation)

describe_network(): A function to describe the evidence base

distr_characteristics(): Visualising the distribution of characteristics (Comparisons' comparability for transitivity evaluation)

forestplot(): Comparator-specific forest plot for network meta-analysis

forestplot_juxtapose(): Forest plot of juxtaposing several network meta-analysis models

forestplot_metareg(): Comparator-specific forest plot for network meta-regression

gower_distance(): Gower's dissimilarity measure (Trials' comparability for transitivity evaluation)

heatmap_missing_dataset(): Heatmap of proportion of missing participants in the dataset

heatmap_missing_network(): Heatmap of proportion of missing participants in the network

heatmap_robustness(): Heatmap of robustness

heter_density_plot(): Visualising the density of two prior distributions for the heterogeneity parameter

heterogeneity_param_prior(): Determine the prior distribution for the heterogeneity parameter

improved_ume(): Detect the frail comparisons in multi-arm trials

inconsistency_variance_prior(): Function for the hyper-parameters of the prior distribution of the inconsistency variance (network meta-analysis with random inconsistency effects)

internal_measures_plot(): Internal measures for cluster validation (Comparisons' comparability for transitivity evaluation)

intervalplot_panel_ume(): A panel of interval plots for the unrelated mean effects model

kld_barplot(): Barplot for the Kullback-Leibler divergence measure (missingness scenarios)

kld_inconsistency(): Density plots of local inconsistency results and Kullback-Leibler divergence when 'rnmamod', 'netmeta' or 'gemtc' R packages are used

kld_inconsistency_user(): Density plots of local inconsistency results and Kullback-Leibler divergence (When dataset is created by the user)

kld_measure(): Function for the Kullback-Leibler Divergence of two normally distributed treatment effects for the same pairwise comparison

league_heatmap(): League heatmap for estimation

league_heatmap_pred(): League heatmap for prediction

league_table_absolute(): League table for relative and absolute effects

league_table_absolute_user(): League table for relative and absolute effects (user defined)

leverage_plot(): Leverage plot

mcmc_diagnostics(): Markov Chain Monte Carlo diagnostics

metareg_plot(): End-user-ready results for network meta-regression

miss_characteristics(): Visualising missing data in characteristics (Comparisons' comparability for transitivity evaluation)

missingness_param_prior(): Define the mean value of the normal distribution of the missingness parameter

netplot(): Network plot

nma.baker2009: Pharmacological interventions for chronic obstructive pulmonary disease

nma.bottomley2011: Pharmacological interventions for moderately severe scalp psoriasis

nma.dogliotti2014: Oral antithrombotics for stroke episode

nma.liu2013: Antidepressants in Parkinson's disease

nma.schwingshackl2014: Training modalities for patients with type 2 diabetes

nma.stowe2011: Antiparkinsonian interventions for later Parkinson's disease

nodesplit_plot(): End-user-ready results for the node-splitting approach

pma.hetrick2012: Paroxetine versus placebo for depressive disorders

pma.taylor2004: Inositol versus glucose for depressive episode

prepare_model(): WinBUGS code for Bayesian pairwise or network meta-analysis and meta-regression

prepare_nodesplit(): WinBUGS code for the node-splitting approach

prepare_ume(): WinBUGS code for the unrelated mean effects model

rainbow_similarities(): Rainbow of Gower's similarity values for each study (Transitivity evaluation)

rankosucra_plot(): Rankograms and SUCRA curves

rnmamod-package rnmamod: rnmamod: Bayesian Network Meta-analysis with Missing Participants

robustness_index(): Robustness index

robustness_index_user(): Robustness index when 'metafor' or 'netmeta' are used

run_metareg(): Perform Bayesian pairwise or network meta-regression

run_model(): Perform Bayesian pairwise or network meta-analysis

run_nodesplit(): Perform the node-splitting approach

run_sensitivity(): Perform sensitivity analysis for missing participant outcome data

run_series_meta(): Perform a series of Bayesian pairwise meta-analyses

run_ume(): Perform the unrelated mean effects model

scatterplot_sucra(): Scatterplot of SUCRA values

scatterplots_dev(): Deviance scatterplots

series_meta_plot(): End-user-ready results for a series of pairwise meta-analyses

table_tau2_prior(): Predictive distributions for the between-study variance in a future meta-analysis on odds ratio or standardised mean difference

taylor_continuous(): Pattern-mixture model with Taylor series for continuous outcome

taylor_imor(): Pattern-mixture model with Taylor series for a binary outcome

ume_plot(): End-user-ready results for the unrelated mean effects model

unrelated_effects_plot(): End-user-ready results for unrelated trial effects model

weight_defined(): Preparing the study weights based on Gower's dissimilarities (Transitivity evaluation)