Data Generation: Mechanisms and Examples • RMSTpowerBoost

What this covers

We show how to construct a recipe (covariates (X), treatment (A), event time (Y), and censoring (C)), and then simulate datasets with simulate_from_recipe(). We also show batch generation via generate_recipe_sets() and how to read compact metadata back with load_recipe_sets().

The simulator takes a plain list as the recipe. No YAML is required.

Recipe skeleton

list(
  n = 300,
  covariates = list(defs = list(/* see Covariates */)),
  treatment  = list(/* see Treatment */),
  event_time = list(/* see Event-time engines */),
  censoring  = list(/* see Censoring */),
  seed = 42
)

Covariates

Each covariate has a name, type ("continuous" or "categorical"), a dist with params, and optional transform steps applied after generation (e.g., "center(a)", "scale(b)").

Available distributions

Type	`dist` (string)	Parameters
continuous	`normal`	`mean`, `sd`
continuous	`lognormal`	`meanlog`, `sdlog`
continuous	`gamma`	`shape`, `scale`
continuous	`weibull`	`shape`, `scale`
continuous	`uniform`	`min`, `max`
continuous	`beta`	`shape1`, `shape2`
continuous	`t`	`df`
categorical	`bernoulli`	`p` (probability of 1)
categorical	`categorical`	`prob = c(...)`, `labels = c(...)` (opt.)
categorical	`ordinal`	`prob = c(...)`, `labels = c(...)` (opt.)

Example: define covariates

covs <- list(
  list(name="age",   type="continuous",  dist="normal",     params=list(mean=62, sd=10),
       transform=c("center(60)","scale(10)")),
  list(name="sex",   type="categorical", dist="bernoulli",  params=list(p=0.45)),
  list(name="stage", type="categorical", dist="ordinal",
       params=list(prob=c(0.3,0.5,0.2), labels=c("I","II","III"))),
  list(name="x",     type="continuous",  dist="lognormal",  params=list(meanlog=0, sdlog=0.6))
)

Treatment

Choose one assignment:

Assignment	Key fields	Meaning
`"randomization"`	`allocation = "a:b"`	Bernoulli with probability (p_1 = a/(a+b)).
`"stratified"`	`allocation`, `stratify_by = c(...)`	Same allocation within each stratum defined by listed categorical covariates.
`"logistic_ps"`	`ps_model = list(formula, beta)`	Treatment probability is (^{-1}()) from user model.

Examples

Randomization:

tr_rand <- list(assignment="randomization", allocation="1:1")

Stratified by "stage":

tr_strat <- list(assignment="stratified", allocation="2:1", stratify_by=c("stage"))

Logistic propensity:

tr_ps <- list(
  assignment = "logistic_ps",
  ps_model  = list(
    formula = "~ 1 + x + sex",
    beta    = c(-0.3, 1.2, -0.6)  # (Intercept), x, sex
  )
)

Event-time engines

Let (Z) be treatment (0/1), (X) be covariates, and () be the linear predictor (defined in effects, below). Supported engines and baseline parameterizations:

Model (user-facing)	`model` value	Baseline parameters	Notes
AFT Lognormal	`"aft_lognormal"`	`mu`, `sigma`	(T = + + ), ((0,1)).
AFT Weibull	`"aft_weibull"`	`shape`, `scale`	(S_0(t) = (-(t/)^k)); AFT shift via ().
AFT Log-Logistic	`"aft_loglogistic"`	`shape`, `scale`	(T = (),(U/(1-U))^{1/k}).
PH Exponential	`"ph_pwexp"`	`rates = c(\lambda)`, `cuts = numeric(0)`	Piecewise-Exp with a single segment is exponential.
PH Weibull	`"ph_weibull"`	`shape`, `scale`	Proportional hazards with Weibull baseline.
PH Gompertz	`"ph_gompertz"`	`rate`, `gamma`	Hazard (h(t) = a, (bt)).
PH Piecewise Exp	`"ph_pwexp"`	`rates = c(r1,r2,...)`, `cuts = c(...)`	Rate in segment (s) is (r_s ()).

Effects and linear predictor

Specify effects on the appropriate scale (AFT: log-time; PH: log-hazard):

effects = list(
  intercept  = 0,                      # default is 0
  treatment  = -0.25,
  covariates = list(age = 0.01, sex = -0.2)
  # or: formula="~ age + sex", beta=c(0.01, -0.2)
)

effects$covariates must be a named list (e.g., list(age=0.01)), not a named vector from c().

Censoring

We assume a single censoring mechanism that may depend on observed covariates (but not on the unobserved event time after conditioning on those covariates). Two user-facing modes are supported:

Mode	Fields	Semantics
`"target_overall"`	`target`, `admin_time`	Finds an exponential random-censoring rate so overall censoring fraction ≈ `target`, with an administrative floor at `admin_time` (if provided).
`"explicit"`	Any of: `administrative = list(time=...)`; `random = list(dist="exponential", params=list(rate=...))`; `dependent = list(formula="~ ...", base=..., beta=c(...))`	Compose administrative, random, and covariate-dependent censoring directly. The `dependent` block defines a covariate-dependent log-rate model; it does not denote competing risks.

Note. No cause-specific or competing-risk censoring is generated here; the simulator’s “dependent” option is strictly covariate-dependent censoring under a single mechanism.

Examples

Target overall censoring

cz_target <- list(
  mode        = "target_overall",
  target      = 0.25,   # desired overall censoring fraction
  admin_time  = 36      # optional administrative cutoff (same units as event time)
)

Explicit mix (administrative + random)

cz_explicit <- list(
  mode = "explicit",
  administrative = list(time = 36),                                      # admin cutoff
  random         = list(dist = "exponential", params = list(rate = 0.02))# random censoring
)

Explicit covariate-dependent censoring

The dependent block uses a log-rate model: [ _{c}(X) = (b) + X^,] where (b>0) is the baseline rate. Implementation uses base as a rate, and beta on the log-rate scale. When base is supplied, the formula must not include an intercept.

cz_dep <- list(
  mode = "explicit",
  dependent = list(
    # sex is coded numeric 0/1 in these recipes; if yours is a factor, use I(as.numeric(sex)-1)
    formula = "~ 0 + age + sex",   # NO intercept because we use 'base' as the rate
    base    = 0.03,                # positive baseline rate
    beta    = c(0.012, 0.35)       # (age, sex) on log-rate scale
  ),
  administrative = list(time = 48) # optional admin cutoff
)

If you prefer a log-intercept style, omit base and include an intercept in the formula and in beta, e.g. formula="~ 1 + age + sex"; beta=c(-3.5, 0.012, 0.35).

Using a censoring recipe inside a full simulation

# Covariates (sex will be numeric 0/1 here)
covs <- list(
  list(name="age", type="continuous",  dist="normal",    params=list(mean=62, sd=10)),
  list(name="sex", type="categorical", dist="bernoulli", params=list(p=0.45))
)

# Censoring: single covariate-dependent mechanism using a positive base rate
cz_dep <- list(
  mode = "explicit",
  dependent = list(
    formula = "~ 0 + age + sex",  # NO intercept; sex is numeric 0/1
    base    = 0.03,               # baseline censoring rate (>0)
    beta    = c(0.012, 0.35)      # (age, sex) on log-rate scale
  ),
  administrative = list(time = 48)
)

# Quick sanity check (optional)
mm   <- model.matrix(~ 0 + age + sex, data.frame(age=c(50,70), sex=c(0,1)))
rate <- cz_dep$dependent$base * exp(mm %*% matrix(cz_dep$dependent$beta, ncol=1))
stopifnot(all(rate > 0 & is.finite(rate)))

# Recipe
rec <- list(
  n = 300,
  covariates = list(defs = covs),
  treatment  = list(assignment="randomization", allocation="1:1"),
  event_time = list(
    model    = "ph_pwexp",
    baseline = list(rates = c(0.05), cuts = numeric(0)), # exponential baseline
    effects  = list(intercept=0, treatment=-0.3, covariates=list(age=0.01, sex=-0.2))
  ),
  censoring  = cz_dep,
  seed = 2025
)

# Simulate
dat <- simulate_from_recipe(validate_recipe(rec))

# Output
knitr::kable(
  data.frame(achieved_censoring = attr(dat, "achieved_censoring")),
  caption = "Achieved censoring rate"
)

Achieved censoring rate
achieved_censoring
0.51

knitr::kable(utils::head(dat, 8), caption = "First 8 rows of simulated data")

First 8 rows of simulated data
time	status	arm	age	sex
0.0749714	0	0	68.20757	0
1.1019457	0	0	62.35641	1
14.3347821	0	1	69.73154	0
1.0472994	1	1	74.72489	0
1.7066127	1	0	65.70975	0
0.8646883	1	0	60.37146	0
0.1501047	1	1	65.97112	0
5.0730373	1	1	61.20011	0

Worked examples

We now build full recipes and call simulate_from_recipe(). We report realized censoring via attr(dat, "achieved_censoring").

Example 1 — AFT Lognormal

# Covariates
covs1 <- list(
  list(name="age",   type="continuous",  dist="normal",     params=list(mean=62, sd=10),
       transform=c("center(60)","scale(10)")),
  list(name="sex",   type="categorical", dist="bernoulli",  params=list(p=0.45)),
  list(name="stage", type="categorical", dist="ordinal",
       params=list(prob=c(0.3,0.5,0.2), labels=c("I","II","III"))),
  list(name="x",     type="continuous",  dist="lognormal",  params=list(meanlog=0, sdlog=0.6))
)

# Recipe
rec1 <- list(
  n = 300,
  covariates = list(defs = covs1),
  treatment  = list(assignment="randomization", allocation="1:1"),
  event_time = list(model="aft_lognormal",
                    baseline=list(mu=3.0, sigma=0.6),
                    effects=list(intercept=0, treatment=-0.25,
                                 covariates=list(age=0.01, sex=-0.2, x=0.05))),
  censoring  = list(mode="target_overall", target=0.25, admin_time=36),
  seed = 11
)

# Simulate
dat1 <- simulate_from_recipe(validate_recipe(rec1))

# Display: data (first rows)
knitr::kable(utils::head(dat1, 8), caption = "Example 1 — AFT Lognormal: first 8 rows")

Example 1 — AFT Lognormal: first 8 rows
time	status	arm	age	sex	stage	x
17.256804	1	0	-0.3910311	0	II	1.2192376
19.531620	1	1	0.2265944	1	II	0.6122083
11.902048	1	0	-1.3165531	0	III	1.2334828
18.770511	1	0	-1.1626533	0	II	0.5784534
16.584294	1	1	1.3784892	0	II	0.2619596
9.377759	1	1	-0.7341513	0	II	1.1455235
12.079110	1	0	1.5236056	1	III	1.7865600
17.979116	1	1	0.8249178	1	II	1.4493842


# Display: key attributes (compact)
attr_tbl1 <- data.frame(
  attribute = c("achieved_censoring", "n_rows", "n_cols", "class"),
  value = c(
    as.character(attr(dat1, "achieved_censoring")),
    nrow(dat1),
    ncol(dat1),
    paste(class(dat1), collapse = ", ")
  )
)
knitr::kable(attr_tbl1, caption = "Example 1 — Attributes")

Example 1 — Attributes
attribute	value
achieved_censoring	0.23
n_rows	300
n_cols	7
class	data.frame

Example 2 — AFT Weibull

# Start from Example 1 and tweak the event-time engine
rec2 <- rec1
rec2$event_time <- list(
  model    = "aft_weibull",
  baseline = list(shape=1.3, scale=12),
  effects  = list(intercept=0, treatment=-0.20, covariates=list(age=0.008, x=0.04))
)

dat2 <- simulate_from_recipe(validate_recipe(rec2), seed=12)

# Display: data (first rows)
knitr::kable(utils::head(dat2, 8), caption = "Example 2 — AFT Weibull: first 8 rows")

Example 2 — AFT Weibull: first 8 rows
time	status	arm	age	sex	stage	x
8.0059365	1	0	-1.2805676	0	I	0.4280147
22.1334913	1	0	1.7771695	0	II	2.1502917
3.1817150	1	0	-0.7567445	0	II	0.9171049
0.8247902	1	1	-0.7200052	1	II	1.5528655
3.9411737	1	1	-1.7976421	0	II	0.6726879
12.4980918	1	1	-0.0722960	0	II	2.2757744
9.3739465	1	1	-0.1153487	0	I	0.3663151
1.0425923	1	1	-0.4282552	0	III	0.9247253


# Display: key attributes (compact)
attr_tbl2 <- data.frame(
  attribute = c("achieved_censoring", "n_rows", "n_cols", "class"),
  value = c(
    as.character(attr(dat2, "achieved_censoring")),
    nrow(dat2),
    ncol(dat2),
    paste(class(dat2), collapse = ", ")
  )
)
knitr::kable(attr_tbl2, caption = "Example 2 — Attributes")

Example 2 — Attributes
attribute	value
achieved_censoring	0.246666666666667
n_rows	300
n_cols	7
class	data.frame

Example 3 — PH Exponential (single segment)

rec3 <- list(
  n = 400,
  covariates = list(defs = covs1),
  treatment  = list(assignment="randomization", allocation="1:1"),
  event_time = list(
    model    = "ph_pwexp",
    baseline = list(rates = c(0.05), cuts = numeric(0)),
    effects  = list(intercept=0, treatment=-0.3, covariates=list(age=0.01, x=0.03))
  ),
  censoring  = list(mode="target_overall", target=0.20, admin_time=30),
  seed = 13
)

dat3 <- simulate_from_recipe(validate_recipe(rec3))

# Display: data (first rows)
knitr::kable(utils::head(dat3, 8), caption = "Example 3 — PH Exponential: first 8 rows")

Example 3 — PH Exponential: first 8 rows
time	status	arm	age	sex	stage	x
30.00000	0	0	0.7543269	1	III	2.4109639
12.97451	1	1	-0.0802719	1	II	1.0548136
30.00000	0	0	1.9751634	1	I	0.3014883
10.72582	1	1	0.3873201	0	I	2.3418948
16.35756	1	1	1.3425261	1	I	1.8945147
26.61325	1	0	0.6155261	1	III	1.0956426
30.00000	0	1	1.4295066	0	II	1.0172397
30.00000	0	1	0.4366797	0	II	1.9923261


# Display: a small summary + attributes
summ3 <- data.frame(
  Metric = c("n", "Events", "Censoring rate", "Mean time", "Median time"),
  Value  = c(nrow(dat3),
             sum(dat3$status == 1, na.rm = TRUE),
             round(mean(dat3$status == 0, na.rm = TRUE), 3),
             round(mean(dat3$time,   na.rm = TRUE), 2),
             round(stats::median(dat3$time, na.rm = TRUE), 2))
)
knitr::kable(summ3, caption = "Example 3 — Summary")

Example 3 — Summary
Metric	Value
n	400.000
Events	297.000
Censoring rate	0.258
Mean time	16.440
Median time	15.130


attr_tbl3 <- data.frame(
  attribute = c("achieved_censoring", "n_rows", "n_cols", "class"),
  value = c(
    as.character(attr(dat3, "achieved_censoring")),
    nrow(dat3),
    ncol(dat3),
    paste(class(dat3), collapse = ", ")
  )
)
knitr::kable(attr_tbl3, caption = "Example 3 — Attributes")

Example 3 — Attributes
attribute	value
achieved_censoring	0.2575
n_rows	400
n_cols	7
class	data.frame

Example 4 — PH Piecewise Exponential (multi-segment)

rec4 <- list(
  n = 500,
  covariates = list(defs = list(
    list(name="age", type="continuous",  dist="normal",    params=list(mean=60, sd=8)),
    list(name="sex", type="categorical", dist="bernoulli", params=list(p=0.5)),
    list(name="x",   type="continuous",  dist="lognormal", params=list(meanlog=0, sdlog=0.5))
  )),
  treatment  = list(assignment="randomization", allocation="1:1"),
  event_time = list(
    model    = "ph_pwexp",
    baseline = list(rates=c(0.10, 0.06, 0.03), cuts=c(6, 18)),
    effects  = list(intercept=0, treatment=-0.4, covariates=list(age=0.01, x=0.03))
  ),
  censoring  = list(mode="target_overall", target=0.25, admin_time=30),
  seed = 123
)

dat4 <- simulate_from_recipe(validate_recipe(rec4))

# Display: data (first rows)
knitr::kable(utils::head(dat4, 8), caption = "Example 4 — PH Piecewise Exponential (multi-segment): first 8 rows")

Example 4 — PH Piecewise Exponential (multi-segment): first 8 rows
time	status	arm	age	sex	x
11.9350637	0	1	55.51619	0	2.1580717
0.4796944	1	1	58.15858	1	0.9466222
7.2330213	1	1	72.46967	0	1.2914109
2.4702411	1	0	60.56407	1	1.1129109
15.1776274	0	1	61.03430	1	0.9111385
1.9382470	1	0	73.72052	0	0.9415791
2.3146500	1	1	63.68733	1	1.6593354
6.3131555	1	1	49.87951	0	0.9041780


# Display: summary + attributes
summ4 <- data.frame(
  Metric = c("n", "Events", "Censoring rate", "Mean time", "Median time"),
  Value  = c(nrow(dat4),
             sum(dat4$status == 1, na.rm = TRUE),
             round(mean(dat4$status == 0, na.rm = TRUE), 3),
             round(mean(dat4$time,   na.rm = TRUE), 2),
             round(stats::median(dat4$time, na.rm = TRUE), 2))
)
knitr::kable(summ4, caption = "Example 4 — Summary")

Example 4 — Summary
Metric	Value
n	500.000
Events	367.000
Censoring rate	0.266
Mean time	5.640
Median time	3.560


attr_tbl4 <- data.frame(
  attribute = c("achieved_censoring", "n_rows", "n_cols", "class"),
  value = c(
    as.character(attr(dat4, "achieved_censoring")),
    nrow(dat4),
    ncol(dat4),
    paste(class(dat4), collapse = ", ")
  )
)
knitr::kable(attr_tbl4, caption = "Example 4 — Attributes")

Example 4 — Attributes
attribute	value
achieved_censoring	0.266
n_rows	500
n_cols	6
class	data.frame

Generate data based on formula (event & censoring)

This example uses ~ formulas for both event-time covariate effects and covariate-dependent censoring. We keep the treatment effect as a separate scalar to avoid design ambiguity.

# Covariates (sex numeric 0/1)
covs_f <- list(
  list(name="age", type="continuous",  dist="normal",    params=list(mean=62, sd=10)),
  list(name="sex", type="categorical", dist="bernoulli", params=list(p=0.45))
)

trt_f <- list(assignment="randomization", allocation="1:1")

# Event-time engine: AFT lognormal with formula-based covariate effects
evt_f <- list(
  model    = "aft_lognormal",
  baseline = list(mu = 3.1, sigma = 0.55),
  effects  = list(
    treatment  = -0.25,                 # effect of treatment (log-time scale)
    formula    = "~ 0 + age + sex",     # covariate effects via formula (no intercept)
    beta       = c(0.010, -0.20)        # (age, sex) -- match order in formula
  )
)

# Censoring: covariate-dependent via log-rate model (single mechanism)
cz_f <- list(
  mode = "explicit",
  dependent = list(
    formula = "~ 0 + age + sex",  # NO intercept; sex numeric 0/1
    base    = 0.03,               # positive rate
    beta    = c(0.012, 0.35)      # (age, sex) on log-rate scale
  ),
  administrative = list(time = 48)  # optional admin cutoff
)

# Full recipe
rec_f <- list(
  n = 350,
  covariates = list(defs = covs_f),
  treatment  = trt_f,
  event_time = evt_f,
  censoring  = cz_f,
  seed = 777
)

# Simulate
dat_f <- simulate_from_recipe(validate_recipe(rec_f))

# Display
knitr::kable(
  data.frame(achieved_censoring = attr(dat_f, "achieved_censoring")),
  caption = "Achieved censoring rate (formula-based example)"
)

Achieved censoring rate (formula-based example)
achieved_censoring
0.9028571

knitr::kable(utils::head(dat_f, 8), caption = "First 8 rows — formula-based example")

First 8 rows — formula-based example
time	arm	age	sex
0.5200119	1	66.89786	1
12.4499453	0	58.01459	0
18.2041119	0	67.10836	0
10.3316633	0	58.01188	1
0.7918592	1	78.38686	0
9.0102499	1	68.21274	1
1.0401903	1	64.02704	1
0.7415469	0	73.08938	0

Batch generation with metadata

For simulation studies, write multiple scenarios and formats together. The writer creates a manifest.rds with a list-column meta describing each dataset. The loader reattaches attributes when reading back.

# Build a base recipe here (self-contained)
base <- validate_recipe(list(
  n = 200,
  covariates = list(defs = list(
    list(name="age", type="continuous",  dist="normal", params=list(mean=62, sd=10)),
    list(name="sex", type="categorical", dist="bernoulli", params=list(p=0.45)),
    list(name="x",   type="continuous",  dist="lognormal", params=list(meanlog=0, sdlog=0.6))
  )),
  treatment  = list(assignment="randomization", allocation="1:1"),
  event_time = list(
    model    = "aft_weibull",
    baseline = list(shape=1.3, scale=12),
    effects  = list(intercept=0, treatment=-0.20, covariates=list(age=0.008, x=0.04))
  ),
  censoring  = list(mode="target_overall", target=0.25, admin_time=36),
  seed = 3026
))

out_dir <- file.path(tempdir(), "rmstss-manifest-demo")
unlink(out_dir, recursive = TRUE, force = TRUE)

man <- generate_recipe_sets(
  base_recipe = base,
  vary = list(n = c(200, 400),
              "event_time.effects.treatment" = c(-0.15, -0.25)),
  out_dir  = out_dir,
  formats  = c("rds","csv"),
  n_reps   = 1,
  seed_base = 2025
)

# Inspect the first row's compact metadata (fields only; no file paths)
m <- readRDS(file.path(out_dir, "manifest.rds"))
names(m)
 [1] "scenario_id"                     "rep"                            
 [3] "seed"                            "achieved_censoring"             
 [5] "n"                               "file_txt"                       
 [7] "file_csv"                        "file_rds"                       
 [9] "file_rdata"                      "p__n"                           
[11] "p__event_time.effects.treatment" "meta"                           
if ("meta" %in% names(m) && length(m$meta[[1]]) > 0) {
  list(model = m$meta[[1]]$model,
       baseline = m$meta[[1]]$baseline,
       effects = m$meta[[1]]$effects,
       achieved_censoring = m$meta[[1]]$achieved_censoring,
       n = m$meta[[1]]$n)
} else {
  "Manifest is minimal (older run); use rebuild_manifest() to enrich."
}
$model
[1] "aft_weibull"

$baseline
$baseline$shape
[1] 1.3

$baseline$scale
[1] 12


$effects
$effects$intercept
[1] 0

$effects$treatment
[1] -0.15

$effects$covariates
$effects$covariates$age
[1] 0.008

$effects$covariates$x
[1] 0.04



$achieved_censoring
[1] 0.265

$n
[1] 200

# Load datasets back
sets <- load_recipe_sets(file.path(out_dir, "manifest.rds"))
attr(sets[[1]]$data, "achieved_censoring")
[1] 0.265
str(sets[[1]]$meta)
List of 19
 $ dataset_id        : chr "sc001_r01"
 $ scenario_id       : int 1
 $ rep               : int 1
 $ seed_used         : int 3026
 $ n                 : int 200
 $ n_treat           : int 95
 $ n_control         : int 105
 $ event_rate        : num 0.735
 $ achieved_censoring: num 0.265
 $ model             : chr "aft_weibull"
 $ baseline          :List of 2
  ..$ shape: num 1.3
  ..$ scale: num 12
 $ effects           :List of 3
  ..$ intercept : num 0
  ..$ treatment : num -0.15
  ..$ covariates:List of 2
  .. ..$ age: num 0.008
  .. ..$ x  : num 0.04
 $ treatment         :List of 2
  ..$ assignment: chr "randomization"
  ..$ allocation: chr "1:1"
 $ censoring         :List of 3
  ..$ mode      : chr "target_overall"
  ..$ target    : num 0.25
  ..$ admin_time: num 36
 $ covariates        :List of 3
  ..$ :List of 4
  .. ..$ name  : chr "age"
  .. ..$ type  : chr "continuous"
  .. ..$ dist  : chr "normal"
  .. ..$ params:List of 2
  .. .. ..$ mean: num 62
  .. .. ..$ sd  : num 10
  ..$ :List of 4
  .. ..$ name  : chr "sex"
  .. ..$ type  : chr "categorical"
  .. ..$ dist  : chr "bernoulli"
  .. ..$ params:List of 1
  .. .. ..$ p: num 0.45
  ..$ :List of 4
  .. ..$ name  : chr "x"
  .. ..$ type  : chr "continuous"
  .. ..$ dist  : chr "lognormal"
  .. ..$ params:List of 2
  .. .. ..$ meanlog: num 0
  .. .. ..$ sdlog  : num 0.6
 $ allocation        : chr "1:1"
 $ params            :List of 2
  ..$ n                           : num 200
  ..$ event_time.effects.treatment: num -0.15
 $ files             :List of 4
  ..$ txt  : chr NA
  ..$ csv  : chr "D:\\Rtmp\\Rtmpa4Rm7l/rmstss-manifest-demo/sc1_r1.csv"
  ..$ rds  : chr "D:\\Rtmp\\Rtmpa4Rm7l/rmstss-manifest-demo/sc1_r1.rds"
  ..$ rdata: chr NA
 $ created_at        : chr "2026-01-26 21:45:57.923671"

Reproducibility tips

Set seed in the recipe or pass seed= to simulate_from_recipe().

Assumptions. The censoring hazard depends on observed covariates via a user-specified single mechanism. There are no competing risks. Datasets contain only time, status, treatment, and covariates—no dependent-censoring indicators.