MetaDose (version 1.0.1)

mdcont: 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)
)

Value

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

linear_model

The fitted linear dose-response meta-regression model.

linear_plot

A plot of the linear dose-response relationship.

nonlinear_model

The fitted nonlinear dose-response meta-regression model.

nonlinear_plot

A plot of the nonlinear dose-response relationship.

Arguments

measure

Character string specifying the effect size measure. Options are "MD" for mean difference or "SMD" for standardized mean difference.

mean.e

Numeric vector of means in the experimental group.

sd.e

Numeric vector of standard deviations in the experimental group.

n.e

Numeric vector of sample sizes in the experimental group.

mean.c

Numeric vector of means in the control group.

sd.c

Numeric vector of standard deviations in the control group.

n.c

Numeric vector of sample sizes in the control group.

dose

Numeric vector of dose levels corresponding to each study.

data

A data frame containing the meta-analysis data.

linear

Logical; if TRUE, a linear dose-response model is fitted.

nonlinear

Logical; if TRUE, a nonlinear dose-response model using restricted cubic splines is fitted.

x_axis

Character string specifying the x-axis label for plots.

y_axis

Character string specifying the y-axis label for plots.

knots

Numeric vector of quantiles used to place knots for the nonlinear restricted cubic spline model.

Author

Ahmed Abdelmageed

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.

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