vl_train: Viral Rates Dataset for Training Viral Load Outcome

Description

The training dataset contains information about patients, specifically their CD4 T cell counts (cd_2018, cd_2019, cd_2021, cd_2022, cd_2023) and viral loads (vl_2019, vl_2021, vl_2022, vl_2023). For modeling patient viral load persistence or suppression, column vl_2023 is identified as the outcome variable. The dataset also contains information about variables related to adherence to antiretroviral therapy (ART) and cell recovery and viral change rates.

Usage

vl_train

Arguments

Format

A data frame with 65 rows and 21 variables:

cd_2018: CD4 count in 2018.
cd_2019: CD4 count in 2019.
vl_2019: Viral load in 2019.
cd_2021: CD4 count in 2021.
vl_2021: Viral load in 2021.
cd_2022: CD4 count in 2022.
vl_2022: Viral load in 2022.
cd_2023: CD4 count in 2023.
vl_2023: Viral load in 2023.
recovery_rate_2019: CD4 count recovery rate from 2018 to 2019.
recovery_rate_2021: CD4 count recovery rate from 2019 to 2021.
recovery_rate_2022: CD4 count recovery rate from 2021 to 2022.
recovery_rate_2023: CD4 count recovery rate from 2023 to 2022.
rr_2021_2yr: CD4 count recovery rate from 2018 to 2021.
rr_2022_2yr: CD4 count recovery rate from 2019 to 2022.
rr_2023_2yr: CD4 count recovery rate from 2021 to 2023.
rr_2022_3yr: CD4 count recovery rate from 2018 to 2022.
rr_2023_3yr: CD4 count recovery rate from 2019 to 2023.
rr_2023_4yr: CD4 count recovery rate from 2018 to 2023.
viral_rate_2021: Viral load rate of change from 2019 to 2021 (log10).
viral_rate_2022: Viral load rate of change from 2021 to 2022 (log10).
viral_rate_2023: Viral load rate of change from 2022 to 2023 (log10).
vrate_2022_2yr: Viral load rate of change from 2019 to 2022 (log10).
vrate_2023_2yr: Viral load rate of change from 2021 to 2023 (log10).
vrate_2023_3yr: Viral load rate of change from 2019 to 2023 (log10).
adherence_1: First principal component analysis scores representing adherence to ART.
adherence_2: Second principal component analysis scores representing adherence to ART.
adherence_3: Third principal component analysis scores representing adherence to ART.
adherence_4: Fourth principal component analysis scores representing adherence to ART.
adherence_5: Fifth principal component analysis scores representing adherence to ART.

Examples

Run this code

# \donttest{
  # Load the dataset
  data("vl_train", package = "viruslearner")
  # Explore the dataset
  library(dplyr)
  dplyr::glimpse(vl_train)
# }

Run the code above in your browser using DataLab