Simulates trials from a planned design and runs a light stress test on
each (covariance, inference, covariate, weighting and leave-one-out
families, plus the greedy deletion search if fragility = TRUE), so that
a sponsor can see, before unblinding, how often a trial of this design
would be classed robust, sensitive or fragile at each power, and how often
the marginal flag would be set.
Usage
stress_calibrate(
n_per_arm = 50,
power = c(0, 0.5, 0.8, 0.95),
n_trials = 100,
n_visits = 4,
sigma = 10,
rho = 0.6,
dropout = 0.08,
baseline_beta = 0.5,
errors = "normal",
t_df = 3,
hetero = 1,
mnar = 0,
interaction = 0,
fragility = FALSE,
cores = 1L,
seed = 1,
rule = c("interval", "interval_or_direction")
)Arguments
- n_per_arm
Subjects per arm.
- power
Target powers for the pre-specified analysis; the effect size giving each is derived from the design (0 gives the null).
- n_trials
Trials per power.
- n_visits
Number of post-baseline visits.
- sigma
Residual standard deviation at each visit.
- rho
AR(1) correlation between adjacent visits.
- dropout
Baseline per-visit dropout probability.
- baseline_beta
Slope of the response on the baseline covariate.
- errors, t_df, hetero, mnar, interaction
Misspecification settings passed to
simulate_trial().- fragility
Run the deletion search (slower).
- cores
Worker processes (the trials are distributed, so each trial's families run on one core).
- seed
Base seed.
- rule
Classification rule for
stress_verdict().
Value
A data frame of class stress_calibration with one row per power:
the effect size, observed power, and the proportion of trials with each
verdict, the marginal flag, and (if run) a one-subject fragility;
attr(, "trials") holds the per-trial rows.
Examples
stress_calibrate(n_per_arm = 20, power = c(0, 0.8), n_trials = 2)
#> Stress-test calibration: 20 per arm, 4 visits, SD 10, AR(1) 0.6, dropout 0.08 per visit
#> power_target delta trials power_observed marginal robust sensitive fragile
#> 0.0 0.00 2 0.0 0 1 0 0
#> 0.8 8.98 2 0.5 0 1 0 0
#> sensitive_given_sig fragile_k1_given_sig
#> NA NA
#> 0 NA