if (requireNamespace("hvtiRutilities", quietly = TRUE)) {
library("hvtiRutilities")
} else {
pkgload::load_all(export_all = FALSE, helpers = FALSE, quiet = TRUE)
}
#>
#> hvtiRutilities 1.5.11
#>
#> Type hvtiRutilities.news() to see new features, changes, and bug fixes.
#> Simulating Cardiac Surgery Survival Data
Overview
The generate_survival_data() function creates a realistic synthetic cardiac surgery cohort suitable for testing and demonstrating survival analysis workflows. Survival times are drawn from a Weibull distribution with a linear predictor built from clinical variables (LVEF, age, hemoglobin, NYHA class, eGFR), and administrative censoring is applied at up to 15 years.
Generating the Dataset
set.seed(42)
dta <- generate_survival_data(n = 500, seed = 1024)
dim(dta)
#> [1] 500 24
names(dta)
#> [1] "ccfid" "origin_year" "iv_opyrs" "iv_dead" "dead"
#> [6] "reop" "iv_reop" "age" "sex" "bmi"
#> [11] "hgb_bs" "wbc_bs" "plate_bs" "gfr_bs" "lvefvs_b"
#> [16] "lvmass_b" "lvmsi_b" "stvoli_b" "stvold_b" "bypass_time"
#> [21] "xclamp_time" "nyha_class" "diabetes" "hypertension"The dataset contains 24 columns covering patient identifiers, calendar anchors, survival outcomes, reoperation, demographics, pre-operative labs, cardiac function, and surgical variables.
Data Structure
str(dta)
#> 'data.frame': 500 obs. of 24 variables:
#> $ ccfid : chr "PT00001" "PT00002" "PT00003" "PT00004" ...
#> ..- attr(*, "label")= chr "Patient ID"
#> $ origin_year : int 2002 2001 2018 1998 2007 2003 2011 2010 2003 2014 ...
#> ..- attr(*, "label")= chr "Calendar year for iv_opyrs = 0"
#> $ iv_opyrs : num 5.13 4.03 10.51 12.84 3.17 ...
#> ..- attr(*, "label")= chr "Observation interval (years) since origin_year"
#> $ iv_dead : num 5.13 4.03 10.51 12.84 3.17 ...
#> ..- attr(*, "label")= chr "Follow-up time to death (years)"
#> $ dead : int 0 0 0 0 0 0 1 0 1 0 ...
#> ..- attr(*, "label")= chr "Death indicator (1=dead, 0=censored)"
#> $ reop : int 0 0 1 0 1 0 0 0 0 0 ...
#> ..- attr(*, "label")= chr "Reoperation (1=yes, 0=no)"
#> $ iv_reop : num NA NA 4.37 NA 1.39 NA NA NA NA NA ...
#> ..- attr(*, "label")= chr "Follow-up time to reoperation (years)"
#> $ age : num 33.3 39.2 14.5 30.3 48.7 13.4 39.3 76.1 60.4 52.1 ...
#> ..- attr(*, "label")= chr "Age at surgery (years)"
#> $ sex : Factor w/ 2 levels "Female","Male": 1 1 1 1 1 2 2 1 1 1 ...
#> ..- attr(*, "label")= chr "Sex"
#> $ bmi : num 33.3 25.9 28.5 29.2 20.8 25.3 34.3 26 19.2 27.2 ...
#> ..- attr(*, "label")= chr "Body mass index (kg/m2)"
#> $ hgb_bs : num 15.3 10.1 15.4 11.7 9.8 9.8 9.6 15.7 15.7 13.1 ...
#> ..- attr(*, "label")= chr "Baseline hemoglobin (g/dL)"
#> $ wbc_bs : num 1.5 10.37 3.29 6.37 10.99 ...
#> ..- attr(*, "label")= chr "Baseline WBC count (K/uL)"
#> $ plate_bs : num 102 245 279 294 190 193 120 107 299 219 ...
#> ..- attr(*, "label")= chr "Baseline platelet count (K/uL)"
#> $ gfr_bs : num 79.6 82.6 89 87 95.4 40.3 31.9 80.2 72.1 87.7 ...
#> ..- attr(*, "label")= chr "Baseline eGFR (mL/min/1.73m2)"
#> $ lvefvs_b : num 56.8 58.3 67.9 55.1 62.6 59.3 55.8 58 75 41.8 ...
#> ..- attr(*, "label")= chr "Baseline LV ejection fraction (%)"
#> $ lvmass_b : num 164 140 148 119 224 ...
#> ..- 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 74.1 30.9 35.9 62.6 59.2 55.7 62.9 64.1 76.3 66.2 ...
#> ..- attr(*, "label")= chr "Baseline SV index - systolic (mL/m2)"
#> $ stvold_b : num 109.8 89.2 73.4 77.6 94.9 ...
#> ..- attr(*, "label")= chr "Baseline SV index - diastolic (mL/m2)"
#> $ bypass_time : num 102 92 47 79 75 34 51 44 110 72 ...
#> ..- attr(*, "label")= chr "Cardiopulmonary bypass time (min)"
#> $ xclamp_time : num 63 63 28 55 46 20 28 26 81 54 ...
#> ..- attr(*, "label")= chr "Aortic cross-clamp time (min)"
#> $ nyha_class : Ord.factor w/ 4 levels "I"<"II"<"III"<..: 1 2 2 2 2 1 3 1 4 2 ...
#> ..- attr(*, "label")= chr "NYHA functional class"
#> $ diabetes : Factor w/ 2 levels "No","Yes": 1 2 1 2 2 2 1 1 1 1 ...
#> ..- attr(*, "label")= chr "Diabetes mellitus"
#> $ hypertension: Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 1 2 1 2 ...
#> ..- attr(*, "label")= chr "Hypertension"Key columns:
| Column | Description |
|---|---|
ccfid |
Patient identifier |
origin_year |
Calendar year corresponding to iv_opyrs = 0 |
iv_opyrs |
Observation interval length (years) anchored at origin_year |
iv_dead |
Observed follow-up time (years) |
dead |
Event indicator (1 = death, 0 = censored) |
reop |
Reoperation indicator |
iv_reop |
Time to reoperation (years; NA if no reoperation) |
Outcome Summary
# Event rates
cat("Death rate: ", round(mean(dta$dead), 3), "\n")
#> Death rate: 0.54
cat("Reoperation rate: ", round(mean(dta$reop), 3), "\n")
#> Reoperation rate: 0.202
# Follow-up distribution
summary(dta$iv_dead)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.060 2.783 4.385 5.149 6.915 14.900hist(
dta$iv_dead,
breaks = 30,
main = "Distribution of Follow-up Time",
xlab = "Follow-up (years)",
col = "steelblue",
border = "white"
)
Integration with r_data_types() and label_map()
The dataset arrives with variable labels attached and several columns that benefit from type conversion. The r_data_types() function handles these automatically.
# Convert types: keep IDs and continuous outcomes as-is
model_data <- r_data_types(
dta,
factor_size = 5,
skip_vars = c("ccfid", "iv_dead", "iv_reop", "iv_opyrs")
)
#> Warning: r_data_types(): 'use_value_labels' defaults to FALSE, so a column
#> carrying SAS value labels is converted from its numeric codes and the level
#> text -- Home, Rehab, SNF -- is discarded. Pass use_value_labels = TRUE to
#> convert through the labels instead, or FALSE to silence this warning. The
#> default will become TRUE in a later release.
str(model_data[, c("dead", "reop", "sex", "nyha_class", "diabetes", "hypertension")])
#> 'data.frame': 500 obs. of 6 variables:
#> $ dead : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
#> ..- attr(*, "label")= chr "Death indicator (1=dead, 0=censored)"
#> $ reop : logi FALSE FALSE TRUE FALSE TRUE FALSE ...
#> ..- attr(*, "label")= chr "Reoperation (1=yes, 0=no)"
#> $ sex : Factor w/ 2 levels "Female","Male": 1 1 1 1 1 2 2 1 1 1 ...
#> ..- attr(*, "label")= chr "Sex"
#> $ nyha_class : Ord.factor w/ 4 levels "I"<"II"<"III"<..: 1 2 2 2 2 1 3 1 4 2 ...
#> ..- attr(*, "label")= chr "NYHA functional class"
#> $ diabetes : Factor w/ 2 levels "No","Yes": 1 2 1 2 2 2 1 1 1 1 ...
#> ..- attr(*, "label")= chr "Diabetes mellitus"
#> $ hypertension: Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 1 2 1 2 ...
#> ..- attr(*, "label")= chr "Hypertension"After conversion:
deadandreop(2 unique values) →logicalsex,diabetes,hypertension(character) →factornyha_classwas already an orderedfactor
Extracting Variable Labels
lmap <- label_map(model_data)
print(lmap)
#> key label
#> 1 ccfid Patient ID
#> 2 origin_year Calendar year for iv_opyrs = 0
#> 3 iv_opyrs Observation interval (years) since...
#> 4 iv_dead Follow-up time to death (years)
#> 5 dead Death indicator (1=dead, 0=censored)
#> 6 reop Reoperation (1=yes, 0=no)
#> 7 iv_reop Follow-up time to reoperation (years)
#> 8 age Age at surgery (years)
#> 9 sex Sex
#> 10 bmi Body mass index (kg/m2)
#> 11 hgb_bs Baseline hemoglobin (g/dL)
#> 12 wbc_bs Baseline WBC count (K/uL)
#> 13 plate_bs Baseline platelet count (K/uL)
#> 14 gfr_bs Baseline eGFR (mL/min/1.73m2)
#> 15 lvefvs_b Baseline LV ejection fraction (%)
#> 16 lvmass_b Baseline LV mass (g)
#> 17 lvmsi_b Baseline LV mass index (g/m2)
#> 18 stvoli_b Baseline SV index - systolic (mL/m2)
#> 19 stvold_b Baseline SV index - diastolic (mL/m2)
#> 20 bypass_time Cardiopulmonary bypass time (min)
#> 21 xclamp_time Aortic cross-clamp time (min)
#> 22 nyha_class NYHA functional class
#> 23 diabetes Diabetes mellitus
#> 24 hypertension Hypertension
#> label_full truncated over_cap
#> 1 Patient ID FALSE FALSE
#> 2 Calendar year for iv_opyrs = 0 FALSE FALSE
#> 3 Observation interval (years) since origin_year TRUE FALSE
#> 4 Follow-up time to death (years) FALSE FALSE
#> 5 Death indicator (1=dead, 0=censored) FALSE FALSE
#> 6 Reoperation (1=yes, 0=no) FALSE FALSE
#> 7 Follow-up time to reoperation (years) FALSE FALSE
#> 8 Age at surgery (years) FALSE FALSE
#> 9 Sex FALSE FALSE
#> 10 Body mass index (kg/m2) FALSE FALSE
#> 11 Baseline hemoglobin (g/dL) FALSE FALSE
#> 12 Baseline WBC count (K/uL) FALSE FALSE
#> 13 Baseline platelet count (K/uL) FALSE FALSE
#> 14 Baseline eGFR (mL/min/1.73m2) FALSE FALSE
#> 15 Baseline LV ejection fraction (%) FALSE FALSE
#> 16 Baseline LV mass (g) FALSE FALSE
#> 17 Baseline LV mass index (g/m2) FALSE FALSE
#> 18 Baseline SV index - systolic (mL/m2) FALSE FALSE
#> 19 Baseline SV index - diastolic (mL/m2) FALSE FALSE
#> 20 Cardiopulmonary bypass time (min) FALSE FALSE
#> 21 Aortic cross-clamp time (min) FALSE FALSE
#> 22 NYHA functional class FALSE FALSE
#> 23 Diabetes mellitus FALSE FALSE
#> 24 Hypertension FALSE FALSEThe label map is useful for annotating tables and plots with descriptive names.
Preparing Data for Survival Analysis
# Drop admin columns to match a typical model_data setup
model_data <- model_data[, !names(model_data) %in% c("ccfid", "reop", "iv_reop",
"origin_year", "iv_opyrs")]
# Outcome variable breakdown
table(model_data$dead)
#>
#> FALSE TRUE
#> 230 270# Kaplan-Meier style summary by NYHA class (base R)
nyha_levels <- levels(model_data$nyha_class)
median_fu <- sapply(nyha_levels, function(lvl) {
sub <- model_data[model_data$nyha_class == lvl, ]
median(sub$iv_dead)
})
event_rate <- sapply(nyha_levels, function(lvl) {
sub <- model_data[model_data$nyha_class == lvl, ]
mean(sub$dead)
})
nyha_summary <- data.frame(
nyha_class = nyha_levels,
n = table(model_data$nyha_class),
median_fu_yr = round(median_fu, 2),
death_rate = round(event_rate, 3)
)
print(nyha_summary)
#> nyha_class n.Var1 n.Freq median_fu_yr death_rate
#> I I I 124 4.67 0.444
#> II II II 178 4.81 0.522
#> III III III 151 3.95 0.596
#> IV IV IV 47 3.45 0.681Reproducibility
The seed argument ensures reproducible datasets for testing:
dta_a <- generate_survival_data(n = 100, seed = 99)
dta_b <- generate_survival_data(n = 100, seed = 99)
identical(dta_a, dta_b) # TRUE
#> [1] TRUEDifferent seeds produce different datasets:
dta_c <- generate_survival_data(n = 100, seed = 7)
identical(dta_a, dta_c) # FALSE
#> [1] FALSESession Information
sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] hvtiRutilities_1.5.11
#>
#> loaded via a namespace (and not attached):
#> [1] vctrs_0.7.3 cli_3.6.6 knitr_1.52 rlang_1.3.0
#> [5] xfun_0.62 forcats_1.0.1 otel_0.2.0 haven_2.5.5
#> [9] generics_0.1.4 jsonlite_2.0.0 glue_1.8.1 buildtools_1.0.0
#> [13] htmltools_0.5.9 maketools_1.3.2 sys_3.4.3 hms_1.1.4
#> [17] rmarkdown_2.32 evaluate_1.0.5 tibble_3.3.1 fastmap_1.2.0
#> [21] yaml_2.3.12 lifecycle_1.0.5 compiler_4.6.1 dplyr_1.2.1
#> [25] pkgconfig_2.0.3 labelled_2.16.1 digest_0.6.39 R6_2.6.1
#> [29] tidyselect_1.2.1 pillar_1.11.1 magrittr_2.0.5 withr_3.0.3
#> [33] tools_4.6.1