Package {metainsight}

metainsight: A flexible platform for meta-analysis

Description

Run the application via the function run_metainsight

asyncLog

Description

For internal use. Similar to writeLog but for use inside async functions

Usage

asyncLog(async, ..., type = "default")

Arguments

Value

No return value, called for side effects

Compare treatment pairs

Description

Produce a table of comparisons of all treatment pairs for baseline risk models using bnma::relative.effects.table()

Usage

baseline_compare(model, logger = NULL)

Arguments

Value

Relative effects table

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_iter, max_iter and check_iter are set low to run quickly, but should
# be left as the default values in real use

fitted_baseline_model <- baseline_model(configured_data = configured_data,
                                        regressor_type = "shared",
                                        n_iter = 120,
                                        max_iter = 120,
                                        check_iter = 10)

baseline_compare(model = fitted_baseline_model)

Produce deviance plots for baseline risk models

Description

Produce deviance plotly plots for baseline risk models. Unlike for bayes_model output, only stem and leverage plots are produced.

Usage

baseline_deviance(model, async = FALSE)

Arguments

Value

list containing:

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_iter, max_iter and check_iter are set low to run quickly, but should
# be left as the default values in real use

fitted_baseline_model <- baseline_model(configured_data = configured_data,
                                        regressor_type = "shared",
                                        n_iter = 120,
                                        max_iter = 120,
                                        check_iter = 10)

baseline_deviance(model = fitted_baseline_model)

Produce a forest plot for baseline risk models

Description

Produce a forest plot for a baseline risk model using gemtc::blobbogram()

Usage

baseline_forest(
  model,
  xmin = NULL,
  xmax = NULL,
  title = "Baseline risk regression analysis",
  ranking = FALSE,
  logger = NULL
)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_iter, max_iter and check_iter are set low to run quickly, but should
# be left as the default values in real use

fitted_baseline_model <- baseline_model(configured_data = configured_data,
                                        regressor_type = "shared",
                                        n_iter = 120,
                                        max_iter = 120,
                                        check_iter = 10)

baseline_forest(model = fitted_baseline_model)

Convert output of bnma::relative.effects.table() into a suitable format to pass to gemtc::blobbogram()

Description

Convert output of bnma::relative.effects.table() into a suitable format to pass to gemtc::blobbogram()

Usage

baseline_forest_format(median_ci_table, reference_treatment)

Arguments

Extract the default x-axis limits using the same method internal to gemtc::forest()

Description

Extract the default x-axis limits using the same method internal to gemtc::forest()

Usage

baseline_forest_limits(forest_data)

Arguments

Fit a baseline risk regression model

Description

Fit a baseline risk regression model using bnma::network.run(). The output is consistent with outputs produced by gemtc.

Usage

baseline_model(
  configured_data,
  regressor_type,
  n_iter = 20000,
  max_iter = 60000,
  check_iter = 10000,
  async = FALSE
)

Arguments

Value

List of bnma related output:

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_iter, max_iter and check_iter are set low to run quickly, but should
# be left as the default values in real use

fitted_baseline_model <- baseline_model(configured_data = configured_data,
                                        regressor_type = "shared",
                                        n_iter = 120,
                                        max_iter = 120,
                                        check_iter = 10)

Baseline regression data

Description

Generate data required to produce a metaregression plot for a baseline risk model.

Usage

baseline_regression(model, configured_data, async = FALSE)

Arguments

Value

List containing:

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_iter, max_iter and check_iter are set low to run quickly, but should
# be left as the default values in real use

fitted_baseline_model <- baseline_model(configured_data = configured_data,
                                        regressor_type = "shared",
                                        n_iter = 120,
                                        max_iter = 120,
                                        check_iter = 10)

regression_data <- baseline_regression(model = fitted_baseline_model,
                                       configured_data = configured_data)

Summarise baseline risk

Description

Produce a plot summarising baselink risk for each study arm

Usage

baseline_summary(configured_data, logger = NULL)

Arguments

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

baseline_summary(configured_data)

Compare treatment pairs

Description

Produce a table of comparisons of all treatment pairs for Bayesian models using gemtc::relative.effect.table()

Usage

bayes_compare(model, logger = NULL)

covariate_compare(...)

Arguments

Value

Relative effects table

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

bayes_compare(model = fitted_bayes_model)

Summarise a Bayesian model

Description

Produce a summary of a Bayesian model

Usage

bayes_details(model, logger = NULL)

covariate_details(...)

baseline_details(...)

Arguments

Value

HTML summary of the model

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

bayes_details(model = fitted_bayes_model)

Produce deviance plots

Description

Produce deviance plots using the output of gemtc::mtc.deviance() for Bayesian and covariate models. Because these plots are interactive, it is not currently possible to download them, although they can be included in html reports.

Usage

bayes_deviance(model, n_adapt = 5000, n_iter = 20000, async = FALSE)

covariate_deviance(...)

Arguments

Value

A list containing different elements depending on the input model:

When model was created by bayes_model() containing:

When model was created by covariate_model() containing:

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

bayes_deviance(model = fitted_bayes_model,
               n_adapt = 100,
               n_iter = 100)

Bayesian forest plot

Description

Produce a Bayesian forest plot with gemtc::forest()

Usage

bayes_forest(
  model,
  xmin = NULL,
  xmax = NULL,
  title = "",
  ranking = FALSE,
  logger = NULL
)

covariate_forest(...)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

bayes_forest(model = fitted_bayes_model)

Extract the default x-axis limits using the same method internal to gemtc::forest()

Description

Extract the default x-axis limits using the same method internal to gemtc::forest()

Usage

bayes_forest_limits(model)

Arguments

Markov chain Monte Carlo plots

Description

Produce Markov chain Monte Carlo plots for Bayesian models

Usage

bayes_mcmc(model, async = FALSE)

covariate_mcmc(...)

baseline_mcmc(...)

Arguments

Value

list containing:

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

bayes_mcmc(model = fitted_bayes_model)

Fit a Bayesian model

Description

Fit a Bayesian model using gemtc

Usage

bayes_model(configured_data, n_adapt = 5000, n_iter = 20000, async = FALSE)

Arguments

Value

List containing:

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

Fit a Bayesian nodesplitting model

Description

Fit a Bayesian nodesplitting model with gemtc::mtc.nodesplit(). This is not possible for all networks and the function will return an error if the nodes cannot be split.

Usage

bayes_nodesplit(configured_data, n_adapt = 5000, n_iter = 20000, async = FALSE)

Arguments

Value

mtc.nodesplit object containing an mtc.result object for each node

Examples

nodesplit_path <- system.file("extdata", "continuous_nodesplit.csv", package = "metainsight")
loaded_data <- setup_load(data_path = nodesplit_path,
                          outcome = "continuous")

configured_data <- setup_configure(loaded_data = loaded_data,
                                   reference_treatment = "Placebo",
                                   effects = "random",
                                   outcome_measure = "MD",
                                   ranking_option = "good",
                                   seed = 123)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

nodesplit_model <- bayes_nodesplit(configured_data,
                                   n_adapt = 100,
                                   n_iter = 100)

Bayesian nodesplitting forest plot

Description

Produce a forest plot from nodesplitting results

Usage

bayes_nodesplit_plot(nodesplit, main_analysis = TRUE, logger = NULL)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Examples

nodesplit_path <- system.file("extdata", "continuous_nodesplit.csv", package = "metainsight")
loaded_data <- setup_load(data_path = nodesplit_path,
                          outcome = "continuous")

configured_data <- setup_configure(loaded_data = loaded_data,
                                   reference_treatment = "Placebo",
                                   effects = "random",
                                   outcome_measure = "MD",
                                   ranking_option = "good",
                                   seed = 123)

nodesplit_model <- bayes_nodesplit(configured_data,
                                   n_adapt = 100,
                                   n_iter = 100)

bayes_nodesplit_plot(nodesplit_model)

Treatment rankings

Description

Generate treatment ranking data required to produce SUCRA plots from Bayesian models

Usage

bayes_ranking(model, configured_data, logger = NULL)

baseline_ranking(...)

covariate_ranking(...)

Arguments

Value

List of output created by rankdata()

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

ranking_data <- bayes_ranking(fitted_bayes_model, configured_data)

Summarise a Bayesian model

Description

Produce a table summarising Bayesian models

Usage

bayes_results(model, logger = NULL)

covariate_results(...)

baseline_results(...)

Arguments

Value

HTML summary of the model

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

bayes_results(fitted_bayes_model)

Common parameters

Description

Common parameters

Arguments

Fit a covariate regression model

Description

Fit a covariate regression model using gemtc.

Usage

covariate_model(
  configured_data,
  covariate_value,
  regressor_type,
  covariate_model_output = NULL,
  n_adapt = 5000,
  n_iter = 20000,
  async = FALSE
)

Arguments

Value

List of gemtc related output:

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

# initial model
fitted_covariate_model <- covariate_model(configured_data = configured_data,
                                          covariate_value = 98,
                                          regressor_type = "shared",
                                          n_adapt = 100,
                                          n_iter = 100)

# updated for new covariate value
updated_covariate_model <- covariate_model(configured_data = configured_data,
                                          covariate_value = 97,
                                          regressor_type = "shared",
                                          covariate_model_output = fitted_covariate_model,
                                          n_adapt = 100,
                                          n_iter = 100)

Covariate regression data

Description

Generate data from a covariate model required to produce a metaregression plot

Usage

covariate_regression(model, configured_data, async = FALSE)

Arguments

Value

List containing:

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_covariate_model <- covariate_model(configured_data = configured_data,
                                          covariate_value = 98,
                                          regressor_type = "shared",
                                          n_adapt = 100,
                                          n_iter = 100)

regression_data <- covariate_regression(model = fitted_covariate_model,
                                        configured_data = configured_data)

Summarise covariate data

Description

Produce a plot summarising the covariate value for each study arm

Usage

covariate_summary(configured_data, logger = NULL)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

covariate_summary(configured_data)

Creates posterior density plots of MCMC samples.

Description

Creates posterior density plots of MCMC samples.

Usage

density_plots(model, parameters)

Arguments

Value

List of ggplot density plots.

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

mcmc <- bayes_mcmc(model = fitted_bayes_model)

density_plots(fitted_bayes_model$mtcResults, mcmc$parameters)

Create a summary table of deviance information criterion stats for Bayesian models

Description

Create a summary table of deviance information criterion stats for Bayesian models

Usage

dic_table(dic, analysis = "all")

Arguments

export_cinema

Description

Prepare project into a JSON format that CINeMA can read.

Usage

export_cinema(configured_data, gemtc_results = NULL, logger = NULL)

Arguments

Value

JSON string with the following structure: A named list of lists. The top level list contains items:

• "project" Information for CINeMA project

  • "CM" Contribution matrices

    • "contributionMatrices" output from .PrepareAnalysisForCinema()

  • "format" Data format. Always "long"

  • "type" Outcome type. Either "binary" or "continuous"

  • "Studies" Study data

    • "long" Output from .PrepareDataForCinema()

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

cinema_project <- export_cinema(configured_data = configured_data)

writeLines(cinema_project, tempfile(fileext = ".json"))

Calculate a sensible step value to use in numericInput

Description

Calculate a sensible step value to use in numericInput

Usage

format_step(x)

Arguments

Format xlimits to pretty values. Adapted from gemtc::blobbogram()

Description

Format xlimits to pretty values. Adapted from gemtc::blobbogram()

Usage

format_xlim(x, limit, log.scale)

Arguments

Compare treatments for frequentist models

Description

Produce a comparison table of treatments using netmeta::netleague().

Usage

freq_compare(configured_data, logger = NULL)

Arguments

Value

Dataframe of comparisons with one row and one column per treatment

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

freq_compare(configured_data = configured_data)

Produce a frequentist forest plot

Description

Produce an annotated frequentist forest plot using meta::forest()

Usage

freq_forest(
  configured_data,
  xmin = NULL,
  xmax = NULL,
  title = "",
  logger = NULL
)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

freq_forest(configured_data = configured_data)

Extract the minimum and maximum confidence intervals from the summary produced by netmeta

Description

Extract the minimum and maximum confidence intervals from the summary produced by netmeta

Usage

freq_forest_limits(freq, outcome)

Arguments

Value

List containing:

Inconsistency tables for frequentist models

Description

Produce inconsistency tables using netmeta::netsplit()

Usage

freq_inconsistency(configured_data, logger = NULL)

Arguments

Value

Dataframe of inconsistency data:

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

freq_inconsistency(configured_data = configured_data)

Summary forest plot matrix

Description

Produce a summary forest plot matrix with treatments ranked by SUCRA score, determined by netmeta::rankogram(). This function can only be used when configured_data contains between 3 and 10 treatments.

Usage

freq_summary(configured_data, plot_title = "", logger = NULL)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

freq_summary(configured_data = configured_data)

Fit a frequentist model

Description

Fits a frequentist model with netmeta::netmeta()

Usage

frequentist(
  non_covariate_data,
  outcome,
  treatments,
  outcome_measure,
  effects,
  reference_treatment
)

Arguments

Value

List containing:

Creates Gelman plots for a gemtc or bnma model.

Description

Creates Gelman plots for a gemtc or bnma model.

Usage

gelman_plots(gelman_data, parameters)

Arguments

Value

List of ggplot Gelman plots

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

mcmc <- bayes_mcmc(model = fitted_bayes_model)

gelman_plots(mcmc$gelman_data, mcmc$parameters)

Creates network connectivity info displayed under network plots

Description

Creates network connectivity info displayed under network plots

Usage

make_netconnect(freq)

Arguments

Value

Vector summarising network connectivity created by netmeta::netconnection().

Produce a meta-regression plot

Description

Produce a composite meta-regression plot which comprises plots showing direct and indirect evidence from baseline or covariate models. The design was adapted from Donegan et al. (2018) https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1292

Usage

metaregression_plot(
  model,
  configured_data,
  regression_data,
  comparators,
  include_covariate = FALSE,
  include_ghosts = FALSE,
  include_extrapolation = FALSE,
  include_credible = FALSE,
  credible_opacity = 0.2,
  covariate_symbol = "circle open",
  covariate_symbol_size = 10,
  legend_position = "BR",
  logger = NULL
)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_iter, max_iter and check_iter are set low to run quickly, but should
# be left as the default values in real use

fitted_baseline_model <- baseline_model(configured_data = configured_data,
                                        regressor_type = "shared",
                                        n_iter = 120,
                                        max_iter = 120,
                                        check_iter = 10)

regression_data <- baseline_regression(model = fitted_baseline_model,
                                       configured_data = configured_data)

metaregression_plot(model = fitted_baseline_model,
                    configured_data = configured_data,
                    regression_data = regression_data,
                    comparators = c("the_Younger", "the_Little"))

Calculate edge.weights for network

Description

Calculate edge.weights for network

Usage

network_structure(freq, order = NA)

Arguments

Value

data.frame containing the number of studies that compare each treatment against the reference treatment.

printVecAsis

Description

For internal use. Print objects as character string

Usage

printVecAsis(x)

Arguments

Value

A character string to reproduce the object

Treatment ranking plots

Description

Produce either a rankogram or radial SUCRA plot ranking the treatments

Usage

ranking_plot(
  ranking_data,
  style,
  colourblind = FALSE,
  simple = FALSE,
  regression_text = "",
  logger = NULL
)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Ranking probability table

Description

Ranking probability table

Usage

ranking_table(ranking_data)

Arguments

Value

dataframe

Return svg

Description

Return svg

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Run "metainsight" Application

Description

This function runs the "metainsight" application in the user's default web browser.

Usage

run_metainsight(
  launch.browser = TRUE,
  port = getOption("shiny.port"),
  load_file = NULL
)

Arguments

Author(s)

Jamie Kass jkass@gradcenter.cuny.edu

Gonzalo E. Pinilla-Buitrago gpinillabuitrago@gradcenter.cuny.edu

Simon E. H. Smart simon.smart@cantab.net

Examples

if(interactive()) {
run_metainsight()
}

Configure the analysis

Description

Checks the connectivity of the loaded data and converts it into formats for later analyses. Conducts a frequentist analysis using netmeta::netmeta(). The output can be passed to many other functions - all summary_ and freq_ functions and bayes_model(), baseline_model() and covariate_model().

Usage

setup_configure(
  loaded_data,
  reference_treatment,
  effects,
  outcome_measure,
  ranking_option,
  seed,
  logger = NULL
)

Arguments

Value

configured_data containing:

Examples

minimal_data_path <- system.file("extdata", "continuous_minimal.csv", package = "metainsight")
loaded_data <- setup_load(data_path = minimal_data_path,
                          outcome = "continuous")

configured_data <- setup_configure(loaded_data = loaded_data,
                                   reference_treatment = "the Great",
                                   effects = "random",
                                   outcome_measure = "MD",
                                   ranking_option = "good",
                                   seed = 123)

Summarise the analysis configuration

Description

Create a table summarising how the analysis has been configured

Usage

setup_configure_table(configured_data)

Arguments

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

setup_configure_table(configured_data)

Takes the configured data, removes any excluded studies and returns subsets of the data to be passed to other functions.

Description

Takes the configured data, removes any excluded studies and returns subsets of the data to be passed to other functions.

Usage

setup_exclude(configured_data, exclusions, async = FALSE)

Arguments

Value

configured_data containing:

• cross: Crosses

• circle_open: Open circles

• none: No symbols in which case only the plot of direct evidence is

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

subsetted_data <- setup_exclude(configured_data = configured_data,
                                exclusions = c("Leo", "Minerva"))

Produce an version of the summary_study() plot for use in the interface for excluding studies. Inside the app this is interactive, but it can also be rendered for non-interactive use.

Description

Produce an version of the summary_study() plot for use in the interface for excluding studies. Inside the app this is interactive, but it can also be rendered for non-interactive use.

Usage

setup_exclude_plot(configured_data, exclusions = NULL, hover = FALSE)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Load data

Description

Load data from a spreadsheet or a default dataset and assess the data for validity. This checks the column names for required columns and balanced wide format numbered columns. Data can be in either a long or wide format; long data has one row per study arm whereas wide data has one row per study. For continuous outcomes, long data should contain the columns: Study - an identifier, e.g. author and year, T - treatment, N - number of participants, Mean - mean value of the outcome, SD - standard deviation of the outcome. Wide data for continuous outcomes should contain: Study, N.1, N.2, Mean.1, Mean.2, SD.1, SD.2 where the number refers to the arm of the study and extra columns should be added depending on the number of arms. For binary outcomes, long data should contain: Study, T, N (as for continuous data) and R - the number of participants with the outcome of interest. Wide data for binary outcomes should follow the same convention: Study, T.1, T.2, R.1, R.2, N.1, N.2. Additionally, a ⁠covar.<name>⁠ column can be added to all formats containing covariate data where ⁠<name>⁠ should be replaced with the name of the covariate. For long data, covariate values must be equal for every study arm. Risk of bias data can also be included with all columns containing values ranging from 1 (low risk) to 3 (high risk): rob for the overall risk of bias, indirectness for indirectness and ⁠rob.<name>⁠ for up to ten individual components.

Usage

setup_load(data_path = NULL, outcome, logger = NULL)

Arguments

Value

List containing:

Examples

# load data from a file
minimal_data_path <- system.file("extdata", "continuous_minimal.csv", package = "metainsight")
loaded_data <- setup_load(data_path = minimal_data_path,
                          outcome = "continuous")

# load default data
loaded_data <- setup_load(outcome = "binary")

Upgrade old data formats

Description

Loads a .csv file and converts it into a suitable format for use by setup_load.

Usage

setup_upgrade(data_path, treatments, logger = NULL)

Arguments

Value

Dataframe containing the upgraded data

Examples

old_data_path <- system.file("extdata", "old_data.csv", package = "metainsight")
upgraded_data <- setup_upgrade(data_path = old_data_path,
                               treatments = "A,B,C,D,E")

Characterise the data

Description

Produce summaries of network characteristics, treatments and treatment pairs such as the number of participants and and the mean outcome. Inspired by BUGSnet::net.tab()

Usage

summary_char(configured_data, logger = NULL)

Arguments

Value

List containing:

Produce a plot of the network using netmeta::netgraph()

Description

Produce a plot of the network using netmeta::netgraph()

Usage

summary_network(
  configured_data,
  style,
  label_size = 1,
  title = "",
  logger = NULL
)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

summary_network(configured_data = configured_data,
                style = "netgraph")

Produce a forest plot of pairwise comparisons, grouped by treatment pairs. If risk of bias data was loaded these are also included.

Description

Produce a forest plot of pairwise comparisons, grouped by treatment pairs. If risk of bias data was loaded these are also included.

Usage

summary_study(
  configured_data,
  plot_area_width = 6,
  colourblind = FALSE,
  x_min = NULL,
  x_max = NULL,
  interactive = FALSE,
  logger = NULL
)

Arguments

Value

html. Contains the svg string to generate the plot. This will display the plot when at the end of a quarto or rmarkdown chunk. To view in the viewer panel of Rstudio, use htmltools::browsable(). The output can be saved using write_plot().

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

summary_study(configured_data = configured_data)

Find the default minimum and maximum values for the x-axis and calculate a sensible step to use in the numeric input

Description

Find the default minimum and maximum values for the x-axis and calculate a sensible step to use in the numeric input

Usage

summary_study_limits(pairwise, outcome)

Arguments

Value

Vector of xmin and xmax

Creates trace plots of MCMC samples.

Description

Creates trace plots of MCMC samples.

Usage

trace_plots(model, parameters)

Arguments

Value

List of ggplot trace plots.

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

mcmc <- bayes_mcmc(model = fitted_bayes_model)

trace_plots(fitted_bayes_model$mtcResults, mcmc$parameters)

writeLog

Description

For internal use. Add text to a logger

Usage

writeLog(logger, ..., type = "default", go_to = NULL)

Arguments

Write plots to a file

Description

Write an svg plot to either a png, pdf or svg file.

Usage

write_plot(svg, file)

Arguments

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

tmp <- tempfile(fileext = ".png")
summary_network(configured_data = configured_data,
                style = "netgraph") |>
                write_plot(tmp)
unlink(tmp)