MAC2: Internal function for the Meta-Analytical Calibration Curve...

In CalibrationCurves: Calibration Performance

Internal function for the Meta-Analytical Calibration Curve (MAC2)

Description

Computes meta-analytical calibration curves using multiple methods (logistic regression, loess or splines) and performs meta-analysis across clusters to generate aggregated calibration curves with confidence and prediction intervals.

Usage

MAC2(
  data = NULL,
  p,
  y,
  cluster,
  grid,
  cl.level = 0.95,
  alpha.lr = 0.05/3,
  plot = TRUE,
  cluster_curves = FALSE,
  knots = 3,
  transf = "logit",
  method_choice = c("splines", "log", "loess"),
  method.tau = "REML",
  prediction = TRUE,
  random = TRUE,
  sm = "PLOGIT",
  hakn = FALSE,
  linewidth = 1,
  method.predict = "HTS",
  verbose = FALSE
)

Arguments

data	optional data frame containing the variables p, y, and cluster. If supplied, variable names should be given without quotation marks.
p	predicted probabilities (numeric vector) or name of the column in data.
y	binary outcome variable or the name of the column in data.
cluster	Cluster identifier (factor, character, or integer) or name of the column in data.
grid	the grid for the calibration curve evaluation
cl.level	the confidence level for the calculation of the confidence interval. Default is 0.95.
alpha.lr	the alpha-level used for the likelihood ratio test, selecting the number of knots for the restricted cubic splines
plot	logical, indicates whether to plot the calibration curves. Default is TRUE.
cluster_curves	logical, whether to include cluster-specific curves in the plot. Default is FALSE.
knots	integer, number of knots for splines. Default is 3.
transf	character, transformation for predictions: "logit" or "identity". Default is "logit".
method_choice	character, which method to use for meta-analysis. Options are: "log", "loess" or "splines". Default is "splines".
method.tau	character, method for between-study heterogeneity estimation. Default is "REML". This argument is passed to the metagen function.
prediction	logical, whether to compute prediction intervals. Default is TRUE. This argument is passed to the prediction argument of the metagen function.
random	logical, whether to use random-effects model. Default is TRUE. This argument is passed to the random argument of the metagen function.
sm	character, summary measure for meta-analysis. Default is "PLOGIT". This argument is passed to the sm argument of the metagen function.
hakn	logical, whether to use Hartung-Knapp adjustment. Default is FALSE. This argument is passed to the method.random.ci argument of the metagen function.
linewidth	numeric, line width for the meta-curve. Default is 1.
method.predict	character, method for prediction intervals. Default is "HTS". This argument is passed to the method.predict argument of the metagen function.
verbose	logical, indicates whether progress has to be printed in the console.

Details

This function estimates the center-specific calibration curves using logistic regression, loess or splines. Hereafter, it aggregates the calibration curves using meta-analytical techniques. The meta-analysis is performed using the function metagen from the meta package. The method_choice argument determines which method is for the meta-analytical aggregation.

Value

A list containing:

cluster_data

Data frame with linear predictors and standard errors for each method per cluster

plot_data

Data frame with meta-analysis results including predictions and intervals

plot

A ggplot2 object if plot = TRUE, otherwise NULL

CalibrationCurves documentation built on Dec. 9, 2025, 5:08 p.m.