SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data.
Patterns (N Y)
; 3(4): 100453, 2022 Apr 08.
Article
in English
| MEDLINE | ID: covidwho-1670996
ABSTRACT
One of the impacts of the coronavirus disease 2019 (COVID-19) pandemic has been a push for researchers to better exploit synthetic data and accelerate the design, analysis, and modeling of clinical trials. The unprecedented clinical efforts caused by COVID-19's emergence will certainly boost future robust and innovative approaches of statistical sciences applied to clinical fields. Here, we report the development of SASC, a simple but efficient approach to generate COVID-19-related synthetic clinical data through a web application. SASC takes basic summary statistics for each group of patients and attempts to generate single variables according to internal correlations. To assess the "reliability" of the results, statistical comparisons with Synthea, a known synthetic patient generator tool, and, more importantly, with clinical data of real COVID-19 patients are provided. The source code and web application are available on GitHub, Zenodo, and Mendeley Data.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Cohort study
/
Observational study
/
Prognostic study
Language:
English
Journal:
Patterns (N Y)
Year:
2022
Document Type:
Article
Affiliation country:
J.patter.2022.100453
Similar
MEDLINE
...
LILACS
LIS