speiser10 / openlong

Shares Synthetic Longitudinal Data And Code For Formatting Real Data

OpenLong

OpenLong harmonizes commonly used longitudinal data sets on aging. Its purpose is to facilitate machine learning benchmark studies, prediction modeling, and meta-analyses, enabling researchers to perform more efficient and accurate analyses.

The cohorts currently available for harmonization are:

1. Health ABC: The Health, Aging and Body Composition Study ABC

2. CHS: The Cardiovascular Health Study

3. MESA: The Multi-Ethnic Study of Atheroscelerosis

4. ARIC: Atherosclerosis Risk in Communities Study

Any combination of these data sets can be harmonized and output in a standardized format which consists of two data sets:

1. baseline.csv: A cross-sectional data set with information on each patient at the baseline of the study.

2. long.csv: A longitudinal data set with information on each patient collected as a sequence of time points.

Installation

You can install the development version of OpenLong like so:

remotes::install_github("briannathanwhite/OpenLong")

Example

TBA

library(OpenLong)
## basic example code