Internal constructor for sample_size_result objects

Usage

new_sample_size_result(
  design,
  task,
  outcome,
  design_type,
  parameters = list(),
  legacy = list(),
  formula_ascii = NULL,
  formula_latex = NULL,
  calculation = NULL,
  reference = NULL,
  roles = character(),
  adjustments = list(),
  hints = character(),
  calculation_steps = NULL
)

Arguments

design: Design keyword (e.g. "rct", "cohort", "diagAccuracy")
task: Statistical task: "estimate", "test", "noninferiority", "superiority", "equivalence", or "plan"
outcome: Outcome type: "mean", "proportion", "rate", "survival", "auc", "lr", "correlation", "ordinal", or "events"
design_type: Human-readable design description
parameters: Named list of input parameters. Each entry must be finite numeric scalar (or vector for prop_bar); e.g. mean1 = 10, sd = 3.
legacy: Legacy result fields (n_per_group, total_n, pairs_count, target_events_needed, target_state_cases_needed, cases_needed, ...)
formula_ascii: Plain-text formula (single string, e.g. "n = 2*((Za/2+Zb)*sd/(mean1-mean2))^2")
formula_latex: LaTeX formula wrapped in $$...$$
calculation: Step-by-step calculation explanation (character vector)
reference: Literature reference (section/page precision)
roles: Character vector documenting parameter roles for the design
adjustments: Named list of applied adjustments (dropout, nonresponse)
hints: Character vector of methodological warnings/tips
calculation_steps: Alias for calculation

Value

A sample_size_result object

Examples

r <- sampleSizeCalc:::new_sample_size_result(
  design = "rct", task = "test", outcome = "mean",
  design_type = "Two independent samples, mean comparison",
  parameters = list(mean1 = 10, mean2 = 12, sd = 3, alpha = 0.05, power = 0.8),
  legacy = list(n_per_group = 37L, total_n = 74L),
  formula_ascii = "n = 2*((Za/2+Zb)*sd/(mean1-mean2))^2",
  reference = "Chow et al. (2017)"
)
stopifnot(inherits(r, "sample_size_result"))