Characterise Transitions in Test Result Status in Longitudinal Studies
Analyse data from longitudinal studies to characterise changes in values of semi-quantitative outcome variables within individual subjects, using high performance C++ code to enable rapid processing of large datasets. A flexible methodology is available for codifying these state transitions.
Installation
You can install the currently-released version from CRAN with this R command:
install.packages("Transition")Alternatively, you can install the latest development version of Transition from GitHub with:
# install.packages("devtools")
devtools::install_github("Mark-Eis/Transition")Authors: Mark C. Eisler and Ana V. Rabaza
eMail: Mark.Eisler@bristol.ac.uk, arabaza@pasteur.edu.uy
ORCID = 0000-0001-6843-3345, 0000-0002-9713-0797
Transition Package Overview: –
Identify temporal transitions in test results for individual subjects in a longitudinal study with
get_transitions().Interpolate these transitions into a data frame for further analysis with
add_transitions().Identify the previous test result for individual subjects and timepoints in a longitudinal study with
get_prev_result().Interpolate these previous test results into a data frame for further analysis with
add_prev_result().Identify the previous test date for individual subjects and timepoints in a longitudinal study
get_prev_date().Interpolate these previous test dates into a data frame for further analysis with
add_prev_date().Identify unique values for subjects, timepoints and test results in longitudinal study data with
uniques().
Methodology
Transition uses high performance C++ code seamlessly integrated into R using Rcpp to enable rapid processing of large longitudinal study datasets.