/OpenLong

Shares Synthetic Longitudinal Data And Code For Formatting Real Data

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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

  1. 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