Package: CohortSymmetry 0.4.0.9000

Danielle Newby

CohortSymmetry: Sequence Symmetry Analysis Using the Observational Medical Outcomes Partnership Common Data Model

Calculating crude sequence ratio, adjusted sequence ratio and confidence intervals using data mapped to the Observational Medical Outcomes Partnership Common Data Model.

Authors:Danielle Newby [aut, cre], Xihang Chen [aut], Tyman Stanford [aut], Berta Raventós [aut], Nicole Pratt [aut], Ed Burn [aut], Marti Català [aut], Núria Mercadé-Besora [aut], Mike Du [aut], Yuchen Guo [aut], Kim Lopez [aut], Marta Alcalde-Herraiz [aut]

CohortSymmetry_0.4.0.9000.tar.gz
CohortSymmetry_0.4.0.9000.zip(r-4.7-any)CohortSymmetry_0.4.0.9000.zip(r-4.6-any)CohortSymmetry_0.4.0.9000.zip(r-4.5-any)
CohortSymmetry_0.4.0.9000.tgz(r-4.6-any)CohortSymmetry_0.4.0.9000.tgz(r-4.5-any)
CohortSymmetry_0.4.0.9000.tar.gz(r-4.7-any)CohortSymmetry_0.4.0.9000.tar.gz(r-4.6-any)
CohortSymmetry_0.4.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CohortSymmetry/json (API)

# Install 'CohortSymmetry' in R:
install.packages('CohortSymmetry', repos = c('https://ohdsi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ohdsi/cohortsymmetry/issues

Pkgdown/docs site:https://ohdsi.github.io

On CRAN:

Conda:

7.35 score 2 stars 89 scripts 430 downloads 8 exports 42 dependencies

Last updated from:6de9a9a696. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK182
source / vignettesOK314
linux-releaseOK184
macos-releaseOK134
macos-oldrelOK153
windows-develOK151
windows-releaseOK131
windows-oldrelOK121
wasm-releaseOK121

Exports:generateSequenceCohortSetmockCohortSymmetryplotSequenceRatiosplotTemporalSymmetrysummariseSequenceRatiossummariseTemporalSymmetrytableSequenceRatiostableTemporalSymmetry

Dependencies:arrowassertthatbitbit64blobclicliprclockcpp11crayonDBIdbplyrdplyrgenericsglueherehmslifecyclemagrittromockomopgenericsPatientProfilespillarpkgconfigprettyunitsprogresspurrrR6readrrlangrprojrootsnakecasestringistringrtibbletidyrtidyselecttzdbutf8vctrsvroomwithr

Introduction to CohortSymmetry
As a diagram
Rendered froma01_Introduction.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2024-04-22

Step 1. Generate a sequence cohort
Introduction | Create a cdm object | Instantiate two cohorts in the cdm reference | Generate a sequence cohort | No specific requirements | Important Observations | Specified study period | Specified study period and prior history requirement | Specified study period, prior history requirement and washout period | Specified study period, prior history requirement and combination window | Specified study period, prior history requirement and index marker gap
Rendered froma02_Generate_a_sequence_cohort.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2024-10-15

Step 2. Obtain the sequence ratios
Introduction | Obtain sequence ratios
Rendered froma03_Summarise_sequence_ratios.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2024-05-28

Step 3. Visualise the sequence ratios
Introduction | Table output of the sequence ratio results | Modify type | Plot output of the sequence ratio results | Modify onlyASR and colours
Rendered froma04_Visualise_sequence_ratios.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2024-05-28

Step 4: Obtain aggregated data on temporal symmetry
Introduction | Obtaining temporal symmetry | Modify days
Rendered froma05_Summarise_temporal_symmetry.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2024-05-28

Step 5: Visualise temporal symmetry
Introduction
Rendered froma06_Visualise_temporal_symmetry.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2024-05-28