estimate_weights: Functions for the Estimation of Propensity Weights

In MAICtools: Performing Matched-Adjusted Indirect Comparisons (MAIC)

Functions for the Estimation of Propensity Weights

Description

Functions for the Estimation of Propensity Weights

Arguments

ipds	A data frame containing individual patient data from the intervention study, with baseline characteristic variables for matching.
agds	A data frame containing aggregate summary data from the comparator study.
matching.list	A character list with two elements giving the names of variables for matching: the first is a vector of binary variables, and the second is a vector of continuous variables. The variable names must match the column names in ipds and agds. Use c() if a type is absent.
intervention.arm	The name of the grouping column in the data frame specified by ipds, e.g., intervention.arm = TRT. The default is TRT.
comparator	The name of the study column in the data frame specified by agds, e.g., comparator = STUDY. The default is STUDY.
comparator.study	A character specifying the comparator study, which must be quoted and cannot be empty (e.g., comparator.study = "Study XX-1"). This is the value of the study column in agds set by the comparator parameter.
comparator.arm	The name of the grouping column in the data frame specified by agds, e.g., comparator.arm = TRT. The default is TRT.
opt.method	The optim method to be used. The default is "BFGS".
seed	The seed for centralized variable missing value imputation (KNN method).
...	Refer to optim for additional parameters.

Value

A data frame containing individual patient data, calculated weights, and rescaled weights.

Examples

cov <- list(
  c("ECOG", "SMK", "METBRAIN"),
  c("BMI", "DIAG")
)

pts <- estimate_weights(
  ipds = IPD,
  agds = AgD_bl,
  matching.list = cov,
  intervention.arm = TRT,
  comparator = STUDY,
  comparator.study = "Study XX-1",
  comparator.arm = TRT
)

MAICtools documentation built on April 4, 2025, 12:17 a.m.