Generate Synthetic Cardiac Surgery Survival Data

Creates a simulated dataset that mimics a cardiac surgery cohort, suitable for testing survival analysis workflows in rf.survival.study.qmd. Variable labels are attached via labelled::var_label() so they are compatible with haven and hvtiRutilities::label_map().

Usage

generate_survival_data(n = 500, seed = 1024)

Arguments

n

Integer. Number of patients to simulate. Default 500.

seed

Integer. Random seed passed to set.seed for reproducibility. Default 1024.

Value

A data.frame with n rows and 24 columns:

ccfid

Character. Patient identifier ("PT00001", …).

origin_year

Integer. Calendar year corresponding to iv_opyrs = 0 (randomly sampled between 1998 and 2018).

iv_opyrs

Numeric. Observation interval length in years measured from origin_year; always greater than or equal to iv_dead.

iv_dead

Numeric. Observed follow-up time in years (min of true survival time and administrative censoring time).

dead

Integer. Event indicator: 1 = death, 0 = censored.

reop

Integer. Reoperation indicator: 1 = yes, 0 = no.

iv_reop

Numeric. Time to reoperation (years); NA if no reoperation.

age

Numeric. Age at surgery in years (range 1–85).

sex

Factor. "Male" / "Female" (55 / 45 %).

bmi

Numeric. Body mass index in kg/m² (range 15–50).

hgb_bs

Numeric. Baseline hemoglobin in g/dL (range 6–18).

wbc_bs

Numeric. Baseline WBC count in K/µL (range 1.5–20).

plate_bs

Numeric. Baseline platelet count in K/µL (range 50–500).

gfr_bs

Numeric. Baseline eGFR in mL/min/1.73 m² (range 10–120).

lvefvs_b

Numeric. Baseline LV ejection fraction in % (range 15–75).

lvmass_b

Numeric. Baseline LV mass in g (range 60–400).

lvmsi_b

Numeric. Baseline LV mass index in g/m² (range 40–220); derived as lvmass_b / (bmi * 0.5).

stvoli_b

Numeric. Baseline systolic stroke-volume index in mL/m² (range 20–110).

stvold_b

Numeric. Baseline diastolic stroke-volume index in mL/m² (range 40–180).

bypass_time

Numeric. Cardiopulmonary bypass time in minutes (range 20–240).

xclamp_time

Numeric. Aortic cross-clamp time in minutes; sampled as 50–75 % of bypass_time.

nyha_class

Ordered factor. NYHA functional class I–IV.

diabetes

Factor. Diabetes mellitus: "No" / "Yes" (75 / 25 %).

hypertension

Factor. Hypertension: "No" / "Yes" (55 / 45 %).

Details

Survival times are drawn from a Weibull distribution (shape = 1.5, ~8-year median follow-up) with a linear predictor built from LVEF, age, baseline hemoglobin, NYHA class, and eGFR. Administrative censoring is applied uniformly between 1 and 15 years. Reoperation probability is modelled via a logistic function of bypass time and NYHA class.

The returned data frame includes the five columns excluded from model_data in the template (ccfid, reop, iv_reop, origin_year, iv_opyrs), plus the survival outcome (iv_dead, dead) and 17 clinical predictors spanning demographics, pre-operative labs, cardiac function, and surgical variables.

The global RNG state is saved and restored on exit, so calling this function does not affect subsequent random number generation in the session.

See also

rfsrc, Surv, read_sas

Examples

dta <- generate_survival_data(n = 200, seed = 42)
str(dta)
#> 'data.frame':	200 obs. of  24 variables:
#>  $ ccfid       : chr  "PT00001" "PT00002" "PT00003" "PT00004" ...
#>   ..- attr(*, "label")= chr "Patient ID"
#>  $ origin_year : int  2018 2006 1999 2014 2009 2011 1999 2000 2017 2015 ...
#>   ..- attr(*, "label")= chr "Calendar year for iv_opyrs = 0"
#>  $ iv_opyrs    : num  3.77 3.99 8.64 13.54 2.28 ...
#>   ..- attr(*, "label")= chr "Observation interval (years) since origin_year"
#>  $ iv_dead     : num  3.77 2.76 8.64 9.16 2.28 3.72 0.77 5.65 1.09 5.97 ...
#>   ..- attr(*, "label")= chr "Follow-up time to death (years)"
#>  $ dead        : int  0 1 0 1 0 0 1 0 0 1 ...
#>   ..- attr(*, "label")= chr "Death indicator (1=dead, 0=censored)"
#>  $ reop        : int  0 1 0 0 0 1 1 0 0 0 ...
#>   ..- attr(*, "label")= chr "Reoperation (1=yes, 0=no)"
#>  $ iv_reop     : num  NA 1.81 NA NA NA 1.29 0.72 NA NA NA ...
#>   ..- attr(*, "label")= chr "Follow-up time to reoperation (years)"
#>  $ age         : num  65.6 36.5 50.4 54.5 51.1 43.4 67.7 43.6 75.3 44.1 ...
#>   ..- attr(*, "label")= chr "Age at surgery (years)"
#>  $ sex         : Factor w/ 2 levels "Female","Male": 2 2 1 2 1 2 1 2 2 2 ...
#>   ..- attr(*, "label")= chr "Sex"
#>  $ bmi         : num  27 30.8 27.2 30.7 26.3 26.7 29.4 32 20.8 26.8 ...
#>   ..- attr(*, "label")= chr "Body mass index (kg/m2)"
#>  $ hgb_bs      : num  14.9 14.6 13 13.2 11.7 12.6 11.1 11.3 10.8 14.5 ...
#>   ..- attr(*, "label")= chr "Baseline hemoglobin (g/dL)"
#>  $ wbc_bs      : num  8.24 8.48 5 6.69 4.98 5.91 4.48 4.71 9.07 6.82 ...
#>   ..- attr(*, "label")= chr "Baseline WBC count (K/uL)"
#>  $ plate_bs    : num  281 204 226 192 362 50 225 227 234 119 ...
#>   ..- attr(*, "label")= chr "Baseline platelet count (K/uL)"
#>  $ gfr_bs      : num  62 54.9 64.3 72.8 87 83.3 72.9 57.9 97.5 93.3 ...
#>   ..- attr(*, "label")= chr "Baseline eGFR (mL/min/1.73m2)"
#>  $ lvefvs_b    : num  43.1 48.4 65.9 60.1 53.6 53.9 62.5 52.8 55.7 38.5 ...
#>   ..- attr(*, "label")= chr "Baseline LV ejection fraction (%)"
#>  $ lvmass_b    : num  147 172 126 226 136 ...
#>   ..- attr(*, "label")= chr "Baseline LV mass (g)"
#>  $ lvmsi_b     : num  40 40 40 40 40 40 40 40 40 40 ...
#>   ..- attr(*, "label")= chr "Baseline LV mass index (g/m2)"
#>  $ stvoli_b    : num  76.7 53.7 31.4 74.4 56.3 69.5 84.1 71.2 49.7 53 ...
#>   ..- attr(*, "label")= chr "Baseline SV index - systolic (mL/m2)"
#>  $ stvold_b    : num  97.4 131.6 119.8 130.4 61.6 ...
#>   ..- attr(*, "label")= chr "Baseline SV index - diastolic (mL/m2)"
#>  $ bypass_time : num  72 118 88 62 71 73 96 106 77 80 ...
#>   ..- attr(*, "label")= chr "Cardiopulmonary bypass time (min)"
#>  $ xclamp_time : num  50 76 64 34 40 47 62 69 41 56 ...
#>   ..- attr(*, "label")= chr "Aortic cross-clamp time (min)"
#>  $ nyha_class  : Ord.factor w/ 4 levels "I"<"II"<"III"<..: 4 2 2 3 3 3 2 2 3 3 ...
#>   ..- attr(*, "label")= chr "NYHA functional class"
#>  $ diabetes    : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 1 1 2 1 1 ...
#>   ..- attr(*, "label")= chr "Diabetes mellitus"
#>  $ hypertension: Factor w/ 2 levels "No","Yes": 1 1 2 1 2 2 2 1 2 1 ...
#>   ..- attr(*, "label")= chr "Hypertension"
table(dta$dead)          # event rate
#> 
#>   0   1 
#>  92 108 
summary(dta$iv_dead)     # follow-up distribution
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   0.250   2.440   4.100   4.888   6.612  13.980 

# Drop admin columns to match model_data in the Quarto template
model_data <- dta[, !names(dta) %in% c("ccfid", "reop", "iv_reop",
  "origin_year", "iv_opyrs")]