Design Sensitivity Analysis for Hierarchical Testing — design_sensitivity

Implementation of Rosenbaum's design sensitivity analysis to assess robustness of treatment effect findings to unobserved confounding. This extends the standard single-study approach to hierarchical testing.

Usage

design_sensitivity_analysis(
  idat,
  bdat,
  blockid,
  formula,
  gamma_range = seq(1, 3, by = 0.1),
  test_statistic = "wilcoxon",
  alpha = 0.05
)

Arguments

idat: Individual-level data
bdat: Block-level data
blockid: Block identifier
formula: Test formula
gamma_range: Vector of sensitivity parameters to test
test_statistic: Type of test statistic ("wilcoxon", "sign", "hodges_lehmann")
alpha: Type I error rate

Value

Design sensitivity analysis results

References

Rosenbaum, P. R. (2017). Observation and experiment: an introduction to causal inference. Harvard University Press.