SCCRIP (Sickle Cell Clinical Research and Intervention Program) established a longitudinal cohort at multiple sites with Sickle Cell Disease (SCD) in 2014 managed by St. Jude Clinical Hematology. A new collaborator for SCCRIP has longitudinal data for 600 SCD patients in OMOP CDM format and this effort is to convert OMOP CDM to SCCRIP format.
HADES (formally known as the OHDSI Methods Library) is a set of open source R packages for large scale analytics, including population characterization, population-level causal effect estimation, and patient-level prediction.
The packages offer R functions that together can be used to perform an observation study from data to estimates and supporting statistics, figures, and tables. The packages interact directly with observational data in the Common Data Model (CDM), and are designed to support both large datasets and large numbers of analyses (e.g. for testing many hypotheses including control hypotheses, and testing many analyses design variations). For this purpose, each Method package includes functions for specifying and subsequently executing multiple analyses efficiently. HADES supports best practices for use of observational data as learned from previous and ongoing research, such as transparency, reproducibility, as well as measuring of the operating characteristics of methods in a particular context and subsequent empirical calibration of estimates produced by the methods. For more information about HADES’ design considerations, please refer to the HADES paper.
HADES has already been used in many published clinical and methodological studies, as can be seen in the Publications section.
HADES (formally known as the OHDSI Methods Library) is a set of open source R packages for large scale analytics, including population characterization, population-level causal effect estimation, and patient-level prediction.
The packages offer R functions that together can be used to perform an observation study from data to estimates and supporting statistics, figures, and tables. The packages interact directly with observational data in the Common Data Model (CDM), and are designed to support both large datasets and large numbers of analyses (e.g. for testing many hypotheses including control hypotheses, and testing many analyses design variations). For this purpose, each Method package includes functions for specifying and subsequently executing multiple analyses efficiently. HADES supports best practices for use of observational data as learned from previous and ongoing research, such as transparency, reproducibility, as well as measuring of the operating characteristics of methods in a particular context and subsequent empirical calibration of estimates produced by the methods. For more information about HADES’ design considerations, please refer to the HADES paper.
HADES has already been used in many published clinical and methodological studies, as can be seen in the Publications section.
Installation
https://ohdsi.github.io/Hades/ https://ohdsi.github.io/Hades/rSetup.html Learn how to use HADES to produce reliable evidence from real-world data with The Book of OHDSI. Read it online.
Important https://ohdsi.github.io/Hades/installingHades.html
HADES-wide releases At the end of quarter 1 and 3 of each year a HADES-wide release is created.
https://forums.ohdsi.org/t/picking-a-target-r-version-for-hades/18989
not resolved as of3/5/2024 (initial install) but may not be a show stopper
renv 1.0.5 was loaded from project library, but this project is configured to use renv 1.0.3.
renv::record("renv@1.0.5")to record renv 1.0.5 in the lockfile.renv::restore(packages = "renv")to install renv 1.0.3 into the project library.renv::status()for more details. [Workspace loaded from ~/.RData]