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Kidney Function Decline and ESRD/Death Data

Source: R/joint_modeling_data.R
kidney_joint_data.Rd

A longitudinal dataset containing eGFR measurements and end-stage renal disease (ESRD) or death outcomes for 150 chronic kidney disease patients.

Usage

kidney_joint_data

Format

A data frame with 1573 observations and 9 variables:

patient_id

Character. Unique patient identifier (CKD_001 to CKD_150)

age

Numeric. Patient age at baseline (years)

diabetes

Factor. Diabetes status (No, Yes)

hypertension

Factor. Hypertension status (No, Yes)

baseline_proteinuria

Numeric. Baseline proteinuria (g/day)

visit_time

Numeric. Time of eGFR measurement (months from baseline)

egfr

Numeric. Estimated glomerular filtration rate (mL/min/1.73m²)

survival_time

Numeric. Time to ESRD/death or last follow-up (months)

esrd_death_status

Numeric. Event indicator (0 = censored, 1 = ESRD, 2 = death)

Source

Simulated data based on chronic kidney disease cohort studies

Details

The dataset simulates chronic kidney disease progression where:

  • eGFR generally declines over time

  • Diabetes and hypertension accelerate decline

  • Lower eGFR increases hazard of ESRD or death

  • Competing risks: ESRD vs death

  • 14.7% event rate over 72 months follow-up

Examples

data(kidney_joint_data)

# eGFR trajectories by diabetes status
library(ggplot2)
ggplot(kidney_joint_data, aes(x = visit_time, y = egfr, color = diabetes)) +
  geom_smooth(method = "loess") +
  labs(title = "eGFR Decline by Diabetes Status", 
       x = "Time (months)", y = "eGFR (mL/min/1.73m²)")
#> `geom_smooth()` using formula = 'y ~ x'