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Simulate survival data for RMST analysis

Source: R/datagen-interface.R
rmst.sim.Rd

A unified, single-call wrapper for generating survival data suitable for use as reference/pilot data in rmst.power and rmst.ss. Supports all seven built-in event-time models (AFT and PH families).

Usage

rmst.sim(
  n,
  model = "aft_lognormal",
  baseline,
  treat_effect = 0,
  covariates = list(),
  target_censoring = 0.25,
  allocation = "1:1",
  L = NULL,
  seed = NULL
)

Arguments

n

Total sample size (split by allocation ratio).

model

Event-time model. One of: "aft_lognormal" (default), "aft_weibull", "aft_loglogistic", "ph_exponential", "ph_weibull", "ph_gompertz", "cox_pwexp".

baseline

Named list of baseline parameters (model-specific; see recipe_quick_aft and recipe_quick_ph).

treat_effect

Numeric treatment coefficient. Log-time scale for AFT models; log-hazard ratio for PH models. Default 0.

covariates

List of covariate definitions. Elements can be created with covar_continuous, covar_binary, or covar_categorical, or supplied as raw named lists in the existing recipe format. Default list() (no covariates).

target_censoring

Target overall censoring fraction (0–1). Default 0.25.

allocation

Treatment allocation ratio string, e.g. "1:1" (default).

L

Optional numeric truncation time. Stored as an attribute on the returned object for downstream use by rmst.power / rmst.ss. Does not affect data generation.

seed

Optional integer seed for reproducibility.

Value

A data.frame of class c("rmst_simdata", "data.frame") with columns time, status, arm (when treatment is present), and one column per covariate. Attributes:

recipe

The validated recipe list used for generation.

L

The truncation time if supplied, else NULL.

achieved_censoring

Actual censoring fraction achieved.

Details

Internally routes to recipe_quick_aft for AFT models and recipe_quick_ph for PH models, then calls simulate_from_recipe.

See also

rmst.power, rmst.ss, recipe_quick_ph, recipe_quick_aft, covar_continuous

Examples

df <- rmst.sim(
  n            = 150,
  model        = "aft_lognormal",
  baseline     = list(mu = 2.2, sigma = 0.5),
  treat_effect = -0.3,
  covariates   = list(covar_continuous("age"), covar_binary("female")),
  L            = 12,
  seed         = 42
)
print(df)
#> ── Simulated RMST Dataset ────────────────────────────────────
#>   Model          : AFT Log-Normal 
#>   N (total)      : 150  |  Arm 0: 81  |  Arm 1: 69
#>   Events         : 110 (73.3%)
#>   Censored       : 40 (26.7%)
#>   Truncation time: 12  (stored as attribute)
#> 
#> First 6 rows:
#>        time status arm        age female
#> 1  9.004186      1   0  1.3709584      0
#> 2 13.198736      1   0 -0.5646982      0
#> 3  6.817518      1   1  0.3631284      0
#> 4  9.655581      1   1  0.6328626      0
#> 5  3.960148      0   0  0.4042683      1
#> 6  4.942770      0   0 -0.1061245      1
s <- summary(df)