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CD4 Count and AIDS Progression Data

Source: R/joint_modeling_data.R
cd4_joint_data.Rd

A longitudinal dataset containing CD4 count measurements and AIDS progression outcomes for 180 HIV patients.

Usage

cd4_joint_data

Format

A data frame with 1341 observations and 8 variables:

patient_id

Character. Unique patient identifier (HIV_001 to HIV_180)

age

Numeric. Patient age at baseline (years)

baseline_viral_load

Numeric. Baseline viral load (copies/mL)

art_adherence

Factor. Antiretroviral therapy adherence (Poor, Good)

visit_time

Numeric. Time of CD4 measurement (months from baseline)

cd4_count

Numeric. CD4+ T cell count (cells/μL)

survival_time

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

aids_death_status

Numeric. Event indicator (0 = censored, 1 = AIDS/death)

Source

Simulated data based on HIV cohort studies

Details

The dataset simulates HIV disease progression where:

  • CD4 counts generally increase with good ART adherence

  • Poor adherence leads to CD4 decline

  • Lower CD4 counts increase hazard of AIDS/death

  • Regular monitoring every ~6 months

  • Low event rate (1.1%) reflecting modern HIV care

Examples

data(cd4_joint_data)

# Compare CD4 trajectories by adherence
library(ggplot2)
ggplot(cd4_joint_data, aes(x = visit_time, y = cd4_count, color = art_adherence)) +
  geom_smooth(method = "loess") +
  labs(title = "CD4 Trajectories by ART Adherence", 
       x = "Time (months)", y = "CD4 Count (cells/μL)")
#> `geom_smooth()` using formula = 'y ~ x'