plot.netdose: Dose-response curve plot

Description

Generates a dose-response plot based on the results of a dose-response network meta-analysis (DR-NMA). The plot visualizes predicted dose-response curves alongside observed responses for easy interpretation of model outputs.

Usage

# S3 method for netdose
plot(
  x,
  pooled = if (x$random) "random" else "common",
  only.direct = FALSE,
  col.direct = if (only.direct) "black" else "green",
  col.indirect = if (only.direct) "black" else "red",
  agents = NULL,
  ylim = NULL,
  benchmark.threshold = NULL,
  plateau.threshold = NULL,
  col.line = "blue",
  col.bmdl = "purple",
  col.pd = "gray40",
  legend = !only.direct,
  ...
)

Value

No return value, called for side effects (generates a plot).

Arguments

x: An object of class netdose (mandatory).
pooled: A character string indicating whether results for the common ("common") or random effects model ("random") should be plotted. Abbreviations are allowed. Defaults to "random" if the input object specifies a random effects model; otherwise, defaults to "common".
only.direct: A logical value indicating whether only the study results of direct comparisons with the reference agent for the observed data should be shown in the plot. Defaults to FALSE.
col.direct: The color used for points representing direct comparisons. By default, "green" when only.direct = FALSE; otherwise, "black".
col.indirect: The color used for points representing indirect comparisons. By default, "red" when only.direct = FALSE; otherwise, "black".
agents: Optional character vector specifying which agents to include in the plot. If NULL, all agents will be plotted.
ylim: Optional numeric vector of length 2 specifying the y-axis limits. If NULL, limits are determined automatically.
benchmark.threshold: Numeric; benchmark response level (e.g., 0.1 for 10 percent). Used to compute the Benchmark Dose Lower Confidence Limit (BMDL). By default, no BMDL is computed. To enable BMDL calculation, specify a desired threshold value (e.g., 0.1).
plateau.threshold: Numeric; threshold for identifying the plateau in the dose-response curve. Defines the minimum absolute change in predicted response between adjacent dose levels, below which the response is considered stable (i.e., plateau has been reached). Used to calculate the Plateau Dose (PD). By default, no PD is computed. To enable PD calculation, specify a small value (e.g., 0.0001).
col.line: Colour for the dose-response line.
col.bmdl: Colour for the BMDL line.
col.pd: Colour for the PD line.
legend: A logical value indicating whether to print a legend.
...: Additional arguments. Currently ignored, but included for potential future extensions or compatibility with generic plotting functions.

Author

Maria Petropoulou <m.petropoulou.a@gmail.com>, Guido Schwarzer <guido.schwarzer@uniklinik-freiburg.de>

Details

The function plots the dose-response curve alongside the observed responses:

The horizontal axis represents the dose range, which is defined from 0 to the maximum observed dose, with 100 evenly spaced points generated within this range.
The vertical axis represents the predicted response values, calculated using the predict.netdose function.

The plot includes shaded confidence intervals for the predicted dose-response curve. Observed responses are overlaid for comparison, differentiated into direct and indirect comparisons with customizable colors.

The function also optionally displays the Benchmark Dose (BMD) and the Benchmark Dose Lower Confidence Limit (BMDL), based on a user-defined benchmark response threshold (e.g., 0.1 for 10 percent increase).

If the model indicates that the predicted response stabilizes beyond a certain dose level, the function estimates and plots the Plateau Dose (PD) — the smallest dose beyond which the predicted response increases less than a given threshold (controlled via plateau.threshold). PD is shown only if it occurs after the BMDL, ensuring biological and statistical coherence.

Examples

Run this code

# Use a subset of 3 studies from anesthesia data
anesthesia_subset <-
  subset(anesthesia, study %in% unique(anesthesia$study)[1:3])

# Prepare data for DR-NMA
dat <- pairwise(
  agent = list(agent1, agent2, agent3),
  event = list(event1, event2, event3),
  n = list(n1, n2, n3),
  dose = list(dose1, dose2, dose3),
  data = anesthesia_subset,
  studlab = study,
  append = FALSE
)

# DR-NMA with linear dose-response function
dr1 <- netdose(TE, seTE, agent1, dose1, agent2,
  dose2, studlab,
  data = dat
)

# Dose-response plot
plot(dr1)

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