Rosenbaum bounds sensitivity analysis (data-frame interface)

Native base R rank-matched-pair signed-rank bounds across a Gamma grid, via the normal approximation to the Wilcoxon signed-rank statistic.

Usage

sensitivity_rosenbaum(
  data,
  treatment,
  outcome,
  covariates,
  gamma_range = c(1, 3),
  n_gamma = 20L
)

Arguments

data: Data frame with treatment + outcome columns.
treatment: Binary treatment column (0/1).
outcome: Outcome column.
covariates: Covariates (used only for matching approximation, here a simple rank-match).
gamma_range: c(min, max) of Gamma. Default c(1, 3).
n_gamma: Number of Gamma values. Default 20.

Value

Data frame with Gamma, p_lower, p_upper.

Examples

if (requireNamespace("EValue", quietly = TRUE) && requireNamespace("rbounds", quietly = TRUE)) {
  set.seed(1)
  df <- data.frame(d = rbinom(60, 1, 0.5), x1 = rnorm(60))
  df$y <- df$d * 0.5 + df$x1 + rnorm(60)
  res <- try(sensitivity_rosenbaum(df, "d", "y", "x1"))
  if (!inherits(res, "try-error")) str(res, max.level = 1)
}
#> 'data.frame':	20 obs. of  3 variables:
#>  $ Gamma  : num  1 1.11 1.21 1.32 1.42 ...
#>  $ p_lower: num  9.60e-07 2.77e-07 7.99e-08 2.32e-08 6.74e-09 ...
#>  $ p_upper: num  9.60e-07 2.98e-06 7.60e-06 1.68e-05 3.29e-05 ...