MetaDose provides a suite of functions to perform linear and nonlinear dose-response meta-regression on study-level data. It supports both continuous (mdcont()) and binary (mdbin()) outcomes, with visualization and S3 methods for easy inspection of results.
The workflow is:
- Model: Use
mdcont()for continuous outcomes ormdbin()for binary outcomes to estimate linear or nonlinear dose-response relationships, including restricted cubic spline modeling. - Visualize: Use the returned
doseobject’splot()method to generate publication-ready dose-response plots, andprint()to inspect the model summaries. - Interact: For users who prefer a graphical interface,
MetaDoseprovides an interactive Shiny application. The app allows uploading data, performing linear or nonlinear dose-response meta-regression, and visualizing results without writing R code.
The Shiny app is hosted online and can be accessed here: MetaDose Shiny App
This approach helps researchers understand the relationship between dose and outcome in a meta-analytic context, providing both numerical and graphical summaries.
Install the development version of MetaDose from GitHub with:
# install.packages("remotes")
remotes::install_github("asmpro7/MetaDose")Continuous Outcome Example
# Perform linear and nonlinear dose-response meta-regression
cont_results <- mdcont(
data = study_data,
mean.e = mean_e,
sd.e = sd_e,
n.e = n_e,
mean.c = mean_c,
sd.c = sd_c,
n.c = n_c,
dose = dose,
measure = "MD"
)
# Print both linear and nonlinear model summaries
print(cont_results, model = "both")
# Plot the dose-response curves
plot(cont_results, model = "both")Binary Outcome Example
# Perform linear and nonlinear dose-response meta-regression
bin_results <- mdbin(
data = study_data,
event.e = event_e,
n.e = n_e,
event.c = event_c,
n.c = n_c,
dose = dose,
measure = "RR"
)
# Print model summaries
print(bin_results, model = "both")
# Plot the dose-response curves
plot(bin_results, model = "both")