multiArmAnalysis {epts} | R Documentation |
Bayesian or Frequentist Analysis with Forest Plot Comparison for Multi-Arm Trial Designs
Description
This function fits Bayesian or frequentist and producing a forest plot across multiple intervention groups for cluster randomized trials (CRT), multisite trials (MST) or simple randomized trials (SRT).
Usage
multiArmAnalysis(
method = "crtBayes",
data,
outcome = "posttest",
interventions = "interventions",
Random = "schools",
Nsim = 10000,
Threshold = 0.05,
FREQoption = "Default",
nPerm = NULL,
nBoot = NULL,
bootType = NULL,
continuous_covariates = NULL,
categorical_covariates = NULL,
maintitle = NULL,
xlabel = NULL,
ylabel = NULL,
vlinecolor = "black",
intlabels = NULL,
intcolors = NULL
)
Arguments
method |
The model fitting method. Should be specified as a character string. Choices are:
|
data |
A data frame containing the variables including outcome, predictors, the clustering variable, and the intervention. |
outcome |
The name of the outcome (post-test) variable. |
interventions |
A string specifying the intervention variable. |
Random |
The name of the clustering variable (e.g., schools or sites) for CRT and MST designs. |
Nsim |
Number of MCMC iterations to be performed for Bayesian analysis. A minimum of 10,000 is recommended to ensure convergence. |
Threshold |
The effect size threshold for posterior computation for Bayesian analysis (default = 0.05). |
FREQoption |
The option for frequentist methods. Choices are "Default", "Permutation", or "Bootstrap". |
nPerm |
The number of permutations required to generate a permutated p-value. |
nBoot |
The number of bootstraps required to generate bootstrap confidence intervals. |
bootType |
method of bootstrapping including case re-sampling at student level "case(1)",case re-sampling at school level "case(2)", case re-sampling at both levels "case(1,2)" and residual bootstrapping using "residual". If not provided, default will be case re-sampling at student level. |
continuous_covariates |
A character vector specifying the names of continuous covariates. |
categorical_covariates |
A character vector specifying the names of categorical covariates (converted to factors). |
maintitle |
main title for the plot. |
xlabel |
Label for the x-axis. |
ylabel |
Label for the y-axis. |
vlinecolor |
Color of the vertical reference line (default = "black"). |
intlabels |
Optional custom intervention labels for the plot. |
intcolors |
Optional intervention colors for the plot. |
Details
This function loops through each intervention, fits the requested statistical model, stores the results, and forest plot visualization for easy comparison. It allows flexible customization for plotting aesthetics.
Value
A ggplot
object showing intervention effect sizes and their confidence intervals.
See Also
Functions from the eefAnalytics package:
crtBayes
, crtFREQ
, mstBayes
, mstFREQ
, srtBayes
, srtFREQ
Examples
### Bayesian analysis of cluster randomised trials ###
data(crt4armSimData)
multiArmAnalysis(method = "crtBayes", data = crt4armSimData, outcome = "posttest",
interventions = "interventions", Random = "schools", Nsim = 10000, Threshold = 0.05,
continuous_covariates = c("pretest"), categorical_covariates = c("gender", "ethnicity"),
intlabels = c("Intervention A", "Intervention B", "Intervention C"),
maintitle = "Forest plot of comparison of effect sizes", xlabel = "Hedges'g",
ylabel = "Interventions", vlinecolor = "black")
###MLM analysis of multisite trials with residual bootstrap confidence intervals ###
data(mst4armSimData)
multiArmAnalysis(method = "mstFREQ", data = mst4armSimData, outcome = "posttest",
interventions = "interventions", Random = "schools", nBoot = 1000, bootType="residual",
continuous_covariates = c("pretest"), categorical_covariates = c("gender", "ethnicity"),
intlabels = c("Intervention A", "Intervention B", "Intervention C"),
intcolors = c("Intervention A" = "blue", "Intervention B" = "green", "Intervention C" = "red"),
maintitle = "Forest plot of comparison of effect sizes ", xlabel = "Hedges'g",
ylabel = "Interventions", vlinecolor = "black")
###MLM analysis of multisite trials with permutation p-value###
data(mst4armSimData)
multiArmAnalysis(method = "mstFREQ", data = mst4armSimData, outcome = "posttest",
interventions = "interventions", Random = "schools", nPerm = 1000,
continuous_covariates = c("pretest"), categorical_covariates = c("gender", "ethnicity"),
intlabels = c("Intervention A", "Intervention B", "Intervention C"),
intcolors = c("Intervention A" = "blue", "Intervention B" = "green", "Intervention C" = "red"),
maintitle = "Forest plot of comparison of effect sizes ",
xlabel = "Hedges'g", ylabel = "Interventions", vlinecolor = "black")
###Bayesian analysis of simple randomised trials###
data(srt4armSimData)
multiArmAnalysis(method = "srtBayes", data = srt4armSimData, outcome = "posttest",
interventions = "interventions", Random = "schools", Nsim = 10000, Threshold = 0.05,
continuous_covariates = c("pretest"), categorical_covariates = c("gender", "ethnicity"),
intlabels = c("Int A", "Int B", "Int C"),
intcolors = c("Int A" = "#1F77B4", "Int B" = "#2CA02C", "Int C" = "#D62728"),
maintitle = "Forest plot of comparison of effect sizes ", xlabel = "Hedges'g",
ylabel = "Interventions", vlinecolor = "black")