Conducting Testing By Nic 2nd-March-2022

This dataset comprises viral loads, CD4 counts, and drug regimen information for 8,916 patients with HIV.

What you should know about this dataset

Testing testing testing

Variables

The following tables contain a list of variables in the synthetic data together with descriptive statistics.

wdt_ID Variable name Data Type Unit Numeric Statistics
1 Viral Load (VL) numeric copies/mL Median: 54.77 (Q1: 16.51, Q3: 209.03)
2 Absolute Count for CD4 (CD4) numeric cells/mm3 Median: 465.81 (Q1: 279.26, Q3: 840.34)
3 Relative Count for CD4 (Rel CD4) numeric cells/mm3 Median: 25.57 (Q1: 18.20, Q3: 35.72)
4 Gender binary - Male: 93.42%, Female: 6.58%
5 Ethnic categorical - 4 Classes Asian: 0.47%; Afro: 2.55%; Caucasian: 26.81%; Other: 70.17%
6 Base Drug Combination categorical - 6 Classes FTC + TDF: 73.66%; 3TC + ABC: 14.08%; FTC + TAF: 0.98%; DRV + FTC + TDF: 5.50%; FTC + RTVB + TDF: 2.30%; Other: 3.47%
7 Complementary INI categorical - 4 Classes DTG: 11.96%; RAL: 0.49%; EVG: 4.69%; Not Applied: 82.86%
8 Complementary NNRTI categorical - 4 Classes NVP: 0.19%; EFV: 9.27%; RPV: 43.76%; Not Applied: 46.78%
9 Extra PI categorical - 6 Classes DRV: 0.69%; RTVB: 4.02%; LPV: 1.08%; RTV: 2.02%; ATV: 4.26%; Not Applied: 87.92%
10 Extra pk Enhancer (Extra pk-En) binary - False: 96.70%, True: 3.30%

Comparison of marginal distributions

Previously the Carousal wasn’t working so here I am back at testing it

Numeric variable comparisons

VL, CD4, and Rel CD4

Categorical variable comparisons

Ethnicity and Components of the Drug Regimen

Binary variable comparisons

Gender, use of pk Enhancer, and the measurement (M) variables

Comparison of correlations

The matrices below show:

1) Static correlations between pairs of variables for all patients at any time, in the real and synthetic data.

2) Dynamic correlations between variables in the real and synthetic data. Time series for each patient were linearly decomposed into trends (indicating a general upward or downward slope) and cycles (indicating periodic patterns). Correlations were then computed between trends and cycles for all patients.

Real Data

Synthetic

Real Data

Synthetic

Real Data

Synthetic

Setup

Health Gym data can be downloaded either directly from this website or using the Python API.

Install the health gym python package through pip by running the following in your terminal.

				
					pip install healthgym
				
			

To download and access the data from within python.

				
					import healthgym as hg

hypotension_data = hg.datasets.HIV(root: 'path/to/data/', download = True)

				
			

All datasets support the following parameters.

				
					root (string)
# Root directory of dataset where dataset exists or will be saved to if download is set to True. 

download (bool, optional) 
# If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.
				
			

Download the dataset

Direct download as CSV file or view GitHub repository

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