Pre-specify a stress test

A plan names the families to run and their settings before unblinding, so the stress test can be written into a statistical analysis plan and run with one call (run_plan()). Each family argument is NULL to skip the family, an empty list() to run it with its defaults, or a named list of arguments for the family function. Printing the plan gives the text a statistical analysis plan can carry.

Usage

stress_plan(
  spec,
  covariance = list(),
  inference = list(),
  covariates = list(),
  weights = TRUE,
  population = NULL,
  data = NULL,
  loo = list(),
  grid = NULL,
  permute = list(n = 2000),
  tipping = NULL,
  reference = list(),
  estimator = list(),
  fragility = list(),
  curve = NULL,
  estimand = NULL,
  visits = NULL,
  title = "Pre-specified stress test",
  rule = c("interval", "interval_or_direction")
)

Arguments

spec

A stress_spec(): the pre-specified analysis.

covariance

Arguments for stress_covariance().

inference

Arguments for stress_inference().

covariates

Arguments for stress_covariates().

weights

TRUE to run stress_weights().

population

Arguments for stress_population() (needs rules).

data

Arguments for stress_data() (needs alternatives).

loo

Arguments for stress_loo(); a list of lists runs it several times (subjects and a site variable, say).

grid

Arguments for stress_grid().

permute

Arguments for stress_permute().

tipping

Arguments for stress_tipping() (needs deltas).

reference

Arguments for stress_reference().

estimator

Arguments for stress_estimator().

fragility

Arguments for stress_fragility().

curve

Arguments for stress_curve() (needs values and modify).

estimand

Arguments for stress_estimand() (needs ice).

visits

Arguments for stress_visits().

title

A title for the plan.

rule

Classification rule passed to stress_verdict(): "interval" (default) or "interval_or_direction".

Value

An object of class stress_plan.

Examples

spec <- stress_spec(mmrm::fev_data, "FEV1", "USUBJID", "AVISIT", "ARMCD", "PBO",
                    covariates = ~ RACE + SEX + FEV1_BL)
plan <- stress_plan(spec, covariance = list(structures = c("us", "cs", "ar1h")),
                    inference = list(), covariates = list(), loo = list(by = "RACE"),
                    permute = NULL, reference = NULL, estimator = list(estimators = "ancova"),
                    fragility = list(max_k = 2))
plan
#> Pre-specified stress test
#> 
#> Pre-specified analysis: FEV1 ~ RACE + SEX + FEV1_BL + ARMCD + AVISIT + ARMCD:AVISIT + us(AVISIT | USUBJID); contrast TRT minus PBO at VIS4; Kenward-Roger df, Kenward-Roger-Linear variance; alpha 0.05; equal LS-mean weights.
#> 
#> Perturbation families, run after unblinding exactly as listed; every refit is judged against the pre-specified estimate,
#> its 95% confidence interval and alpha.
#> Classification: fragile if any counted refit (an alternative analysis or a deletion) lands outside the pre-specified 95% confidence interval; otherwise sensitive if any crosses alpha; otherwise robust.
#> Specifications reached by two families are counted once; tipping-point grids are searches and are not counted; deletions are
#> counted separately.
#> 
#> 1. Within-subject covariance structure: structures = us, cs, ar1h
#> 2. Degrees-of-freedom method and variance estimator: defaults
#> 3. Covariate set: defaults
#> 4. Least-squares-mean weighting (equal versus observed margins): defaults
#> 5. Leave-one-out deletions: by = RACE
#> 6. Estimator class: estimators = ancova
#> 7. Smallest set of subjects that changes the conclusion: max_k = 2