Type:	Package
Title:	Dose-Response Meta-Regression for Meta-Analysis
Version:	1.0.1
Date:	2026-01-08
Author:	Ahmed Abdelmageed [cre, aut]
Maintainer:	Ahmed Abdelmageed <ahmedelsaeedmassad@gmail.com>
URL:	https://github.com/asmpro7/MetaDose/
Description:	Conducting linear and nonlinear dose-response meta-regression using study-level summary data. It supports both continuous and binary outcomes and allows modeling of dose-effect relationships using linear trends or nonlinear restricted cubic splines. The package is designed to facilitate transparent, flexible, and reproducible dose-response meta-analyses, with built-in visualization of fitted dose-response curves.
License:	GPL (≥ 3)
Encoding:	UTF-8
Imports:	ggplot2, metafor, rms, rlang
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2026-01-08 22:44:48 UTC; ASM PRO
Repository:	CRAN
Date/Publication:	2026-01-12 20:00:02 UTC

MetaDose: Dose-Response Meta-Regression for Meta-Analysis

Description

The 'MetaDose' package provides tools for conducting linear and nonlinear dose-response meta-regression using study-level summary data. It supports both continuous and binary outcomes and allows modeling of dose-effect relationships using linear trends or nonlinear restricted cubic splines.

The package is designed to facilitate transparent, flexible, and reproducible dose-response meta-analyses, with built-in visualization of fitted dose-response curves.

Author(s)

Ahmed Abdelmageed ahmedelsaeedmassad@gmail.com

See Also

Useful functions:

• mdcont for dose-response meta-regression with continuous outcomes.

• mdbin for dose-response meta-regression with binary outcomes.

Print and Plot Methods for Dose-Response Meta-Regression Objects

Description

S3 methods for objects of class 'dose' returned by 'mdcont()' and 'mdbin()' functions.

• print.dose displays summaries of the fitted linear and/or nonlinear dose-response meta-regression models.

• plot.dose visualizes the fitted dose-response curves produced by the meta-regression models.

Usage

## S3 method for class 'dose'
print(x, model = c("both", "linear", "nonlinear"), ...)

## S3 method for class 'dose'
plot(x, model = c("both", "linear", "nonlinear"), ...)

Arguments

Value

Both methods are called for their side effects.

• print.dose prints model summaries to the console.

• plot.dose draws dose-response plots in the active graphics device.

The original object x is returned invisibly.

Author(s)

Ahmed Abdelmageed ahmedelsaeedmassad@gmail.com

See Also

mdcont, mdbin

Linear and Nonlinear Dose-Response Meta-Regression for Binary Outcomes

Description

Performs linear and/or nonlinear dose-response meta-regression for binary outcomes using study-level event data. The function supports relative risk ('RR') and odds ratio ('OR') effect measures and models the association between dose levels and treatment effects.

Usage

mdbin(
  measure = c("RR", "OR"),
  event.e,
  n.e,
  event.c,
  n.c,
  dose,
  data,
  linear = TRUE,
  nonlinear = TRUE,
  x_axis = "Dose",
  y_axis = "Measured Effect",
  knots = c(0.1, 0.5, 0.9)
)

Arguments

Details

The function computes logarithmic effect sizes and corresponding sampling variances from binary outcome data. Linear and/or nonlinear dose-response meta-regression models are then fitted, with nonlinear relationships modeled using restricted cubic splines. Dose-response plots are generated to visualize the fitted associations.

Value

An S3 object of class 'dose', which is a list containing:

Author(s)

Ahmed Abdelmageed ahmedelsaeedmassad@gmail.com

See Also

mdcont for dose-response meta-regression with continuous outcomes.

Linear and Nonlinear Dose-Response Meta-Regression for Continuous Outcomes

Description

Performs linear and/or nonlinear dose-response meta-regression for continuous outcomes using study-level summary data. The function supports mean difference ('MD') and standardized mean difference ('SMD') effect measures and fits models across different dose levels using meta-regression techniques.

Usage

mdcont(
  measure = c("MD", "SMD"),
  mean.e,
  sd.e,
  n.e,
  mean.c,
  sd.c,
  n.c,
  dose,
  data,
  linear = TRUE,
  nonlinear = TRUE,
  x_axis = "Dose",
  y_axis = "Measured Effect",
  knots = c(0.1, 0.5, 0.9)
)

Arguments

Details

The function first computes effect sizes and their variances from continuous outcome data. It then fits a linear dose-response meta-regression model and/or a nonlinear model using restricted cubic splines, depending on user selection. Corresponding dose-response plots are generated for visualization.

Value

An S3 object of class 'dose', which is a list containing:

Author(s)

Ahmed Abdelmageed ahmedelsaeedmassad@gmail.com

See Also

mdbin for dose-response meta-regression with binary outcomes.

Examples

# Create a small example dataset
study_data <- data.frame(
  mean.e = c(5.2, 6.1, 7.0, 6.5),
  sd.e   = c(1.1, 1.3, 1.2, 1.0),
  n.e    = c(40, 35, 50, 45),
  mean.c = c(4.8, 5.5, 6.2, 5.9),
  sd.c   = c(1.0, 1.2, 1.1, 1.1),
  n.c    = c(38, 33, 48, 42),
  dose   = c(10, 20, 30, 40)
)

# Fit a dose-response meta-regression model
res <- mdcont(
  measure = "MD",
  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,
  data    = study_data,
  linear  = TRUE,
  nonlinear = TRUE
)

# Print results
print(res)

# Access components
# res$linear_model
# res$nonlinear_model