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Multiple-outcome CMR assignment

Source: R/cmr_multiple_outcomes.R
cmr_multiple_outcomes.Rd

Estimate an effective two-arm variance rectangle for multiple outcomes and return the CMR treatment share.

Usage

cmr_multiple_outcomes(
  y,
  d,
  weights = NULL,
  estimand = c("coprimary", "index"),
  alpha = 0.05,
  method = c("auto", "bounded", "bernoulli", "maurer_pontil", "mp", "bernoulli_exact",
    "martinez_taboada_ramdas", "mtr"),
  beta = NULL,
  na.rm = TRUE,
  tol = 1e-11
)

Arguments

y

Pilot outcomes as a numeric matrix, data frame, or vector. Rows are units and columns are outcomes.

d

Pilot treatment indicator; treatment is 1 and control is 0.

weights

Optional nonnegative outcome weights. If NULL, equal weights are used.

estimand

Either "coprimary" or "index".

alpha

Target joint error level.

method

Confidence-set method. "auto" chooses exact Bernoulli bounds for 0/1 outcomes and bounded Maurer–Pontil bounds otherwise.

beta

Optional scalar endpoint error allocation.

na.rm

If TRUE, drop rows with missing y or d.

tol

Numerical tolerance for exact Bernoulli bound inversion.

Value

A list of class cmr_multiple_outcomes and cmr_two_arm with treatment share pi, CMR certificate U_CMR, effective confidence rectangle, pilot summaries, outcome weights, estimand metadata, endpoint error allocation, and diagnostics.

See also

Other CMR rules: binary_rectangle_corners(), cmr_multiarm(), cmr_multiarm_from_rectangle(), cmr_proxy(), cmr_stratified(), cmr_stratified_from_rectangle(), cmr_two_arm(), cmr_unbounded(), cmr_unbounded_from_rectangle(), print.cmr_two_arm()

Examples

set.seed(10)
d <- rep(c(1, 0), each = 20)
y <- cbind(
  y1 = c(rbeta(20, 2, 6), rbeta(20, 4, 4)),
  y2 = c(rbeta(20, 5, 3), rbeta(20, 3, 5))
)
cmr_multiple_outcomes(y, d, weights = c(0.6, 0.4))
#> <cmr_multiple_outcomes>
#>   pi: 0.5
#>   U_CMR: 0.25
#>   method: bounded
#>   n: 40