maicplus (version 0.1.2)

estimate_weights: Derive individual weights in the matching step of MAIC

Description

Assuming data is properly processed, this function takes individual patient data (IPD) with centered covariates (effect modifiers and/or prognostic variables) as input, and generates weights for each individual in IPD trial to match the covariates in aggregate data.

The plot function displays individuals weights with key summary in top right legend that includes median weight, effective sample size (ESS), and reduction percentage (what percent ESS is reduced from the original sample size). There are two options of plotting: base R plot and ggplot. The default for base R plot is to plot unscaled and scaled separately. The default for ggplot is to plot unscaled and scaled weights on a same plot.

Usage

estimate_weights(
  data,
  centered_colnames = NULL,
  start_val = 0,
  method = "BFGS",
  n_boot_iteration = NULL,
  set_seed_boot = 1234,
  boot_strata = "ARM",
  ...
)
# S3 method for maicplus_estimate_weights
plot(
  x,
  ggplot = FALSE,
  bin_col = "#6ECEB2",
  vline_col = "#688CE8",
  main_title = NULL,
  scaled_weights = TRUE,
  bins = 50,
  ...
)

Value

a list with the following 4 elements,

data

a data.frame, includes the input data with appended column 'weights' and 'scaled_weights'. Scaled weights has a summation to be the number of rows in data that has no missing value in any of the effect modifiers

centered_colnames

column names of centered effect modifiers in data

nr_missing

number of rows in data that has at least 1 missing value in specified centered effect modifiers

ess

effective sample size, square of sum divided by sum of squares

opt

R object returned by base::optim(), for assess convergence and other details

boot_strata

'strata' from a boot::boot object

boot_seed

column names in data of the stratification factors

boot

a n by 2 by k array or NA, where n equals to number of rows in data, and k equals n_boot_iteration. The 2 columns in the second dimension include a column of numeric indexes of the rows in data that are selected at a bootstrapping iteration and a column of weights. boot is NA when argument n_boot_iteration is set as NULL

Arguments

data

a numeric matrix, centered covariates of IPD, no missing value in any cell is allowed

centered_colnames

a character or numeric vector (column indicators) of centered covariates

start_val

a scalar, the starting value for all coefficients of the propensity score regression

method

a string, name of the optimization algorithm (see 'method' argument of base::optim()) The default is "BFGS", other options are "Nelder-Mead", "CG", "L-BFGS-B", "SANN", and "Brent"

n_boot_iteration

an integer, number of bootstrap iterations. By default is NULL which means bootstrapping procedure will not be triggered, and hence the element "boot" of output list object will be NULL.

set_seed_boot

a scalar, the random seed for conducting the bootstrapping, only relevant if n_boot_iteration is not NULL. By default, use seed 1234

boot_strata

a character vector of column names in data that defines the strata for bootstrapping. This ensures that samples are drawn proportionally from each defined stratum. If NULL, no stratification during bootstrapping process. By default, it is "ARM"

...

Additional control parameters passed to stats::optim.

x

object from estimate_weights

ggplot

indicator to print base weights plot or ggplot weights plot

bin_col

a string, color for the bins of histogram

vline_col

a string, color for the vertical line in the histogram

main_title

title of the plot. For ggplot, name of scaled weights plot and unscaled weights plot, respectively.

scaled_weights

(base plot only) an indicator for using scaled weights instead of regular weights

bins

(ggplot only) number of bin parameter to use

Methods (by generic)

• plot(maicplus_estimate_weights): Plot method for estimate_weights objects

Examples

data(centered_ipd_sat)
centered_colnames <- grep("_CENTERED", colnames(centered_ipd_sat), value = TRUE)
weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)
# \donttest{
# To later estimate bootstrap confidence intervals, we calculate the weights
# for the bootstrap samples:
weighted_data_boot <- estimate_weights(
  data = centered_ipd_sat, centered_colnames = centered_colnames, n_boot_iteration = 100
)
# }
plot(weighted_sat)

if (requireNamespace("ggplot2")) {
  plot(weighted_sat, ggplot = TRUE)
}