MAIC bootstrap sample

Matching-adjusted indirect comparison bootstrap sampling.

Usage

maic.boot(
  data,
  indices,
  balance_matrix,
  outcome_x_matrix,
  outcome_y,
  ald_targets,
  scaling_factors,
  trt_var,
  family,
  hat_w = NULL,
  ipd = NULL,
  outcome_model = NULL,
  balance_model = NULL,
  ald = NULL,
  moments = 1,
  int = FALSE
)

Arguments

data: Individual-level patient data (data frame).
indices: Vector of indices, same length as original, which define the bootstrap sample.
balance_matrix: Pre-computed balance matrix.
outcome_x_matrix: Pre-computed outcome design matrix.
outcome_y: Pre-computed outcome vector.
ald_targets: Vector of ALD targets.
scaling_factors: Vector of scaling factors.
trt_var: Treatment variable name.
family: A 'family' object specifying the distribution and link function.
hat_w: MAIC weights; default NULL which calls maic_weights().
ipd: Backwards compatibility IPD data (optional).
outcome_model: Backwards compatibility outcome model formula (optional).
balance_model: Backwards compatibility balance model formula (optional).
ald: Backwards compatibility ALD data (optional).
moments: Backwards compatibility moments (default 1).
int: Backwards compatibility interactions flag (default FALSE).

Value

Vector of fitted probabilities for treatments A and C

Usage

Arguments

Value

See also