viralrates: Viral Rates Dataset

Description

The 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 recovery and viral load persistence or suppression, column cd_2023 is identified as the outcome variable for CD4 cell counts, and column vl_2023 is identified as the outcome variable for viral load. The data also contains information about variables related to adherence to antiretroviral therapy (ART) and cell recovery and viral change rates.

Usage

viralrates

Arguments

Format

A data frame with 87 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("viralrates", package = "viruslearner")
  # Explore the dataset
  library(dplyr)
  dplyr::glimpse(viralrates)
# }

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