Process hypotheses with boundary specifications — process

Transforms hypotheses data frame by appending computed columns: - index, dist_type: Basic indexing and distribution type lookup - treatments: Combined control/test treatment vectors - sfpar, nominal: Spending function parameters (if missing) - description_sf, description_max_info: Human-readable descriptions - enroll_rate, distribution: Nested data filtered to hypothesis-specific combinations - maturity_time: Binary endpoint timing - times_analysed: Analysis timing from linked analyses - information_factor, max_information_factor, information_fractions: GSD information calculations - weights: Weight scenarios (unnested, creating multiple rows per hypothesis) - control_pooled_rate, test_pooled_rate: Pooled rates for binary endpoints - specs, power, hurdles, nominal_p: Boundary specifications from GSD calculations - description_effect_size: Effect size descriptions

Usage

process_hypotheses(
  hypotheses,
  analyses,
  enroll_rate,
  distribution,
  weights,
  alpha = 0.025
)

Arguments

hypotheses: Hypothesis specifications
analyses: Processed analyses
enroll_rate: Enrollment rate data
distribution: Distribution data
weights: Weight scenarios
alpha: Type I error rate

Value

Processed hypotheses with 18+ additional computed columns, expanded by weight scenarios