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visae

Implementation of Shiny apps to visualize adverse events.

https://github.com/dnzmarcio/visae

Science Score: 26.0%

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    Found 3 DOI reference(s) in README
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    Low similarity (7.1%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Implementation of Shiny apps to visualize adverse events.

Basic Info
  • Host: GitHub
  • Owner: dnzmarcio
  • License: gpl-2.0
  • Language: R
  • Default Branch: master
  • Size: 462 KB
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 2
  • Open Issues: 0
  • Releases: 1
Created about 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog License

README.md

Visualizing Adserve Events

CRAN\_Status\_Badge

The R package visae implements 'shiny' apps to visualize adverse events (AE) based on the Common Terminology Criteria for Adverse Events (CTCAE).

Installation

r instal.packages("visae") The latest version can be installed from GitHub as follows: r devtools::install_github("dnzmarcio/visae")

Stacked Correspondence Analysis

Generating minimal dataset

```r patientid <- 1:4000 group <- c(rep("A", 1000), rep("B", 1000), rep("C", 1000), rep("D", 1000)) aegrade <- c(rep("AE class 01", 600), rep("AE class 02", 300), rep("AE class 03", 100), rep("AE class 04", 0), rep("AE class 01", 100), rep("AE class 02", 400), rep("AE class 03", 400), rep("AE class 04", 100), rep("AE class 01", 233), rep("AE class 02", 267), rep("AE class 03", 267), rep("AE class 04", 233), rep("AE class 01", 0), rep("AE class 02", 100), rep("AE class 03", 300), rep("AE class 04", 600))

dt <- tibble(patientid = patientid, trt = group, aeg = aegrade) ```

Investigating different CA configurations using the Shiny application

r library(visae) library(magrittr) library(dplyr) dt %>% run_ca(., group = trt, id = patient_id, ae_grade = ae_g)

Plotting CA biplot as ggplot object

r ca <- dt %>% ca_ae(., group = trt, id = patient_id, ae_class = ae_g, contr_indicator = FALSE, mass_indicator = TRUE, contr_threshold = 0, mass_threshold = 0) ca$asymmetric_plot

Interpreting biplots for Correspondence Analysis

Investigators often interpret CA biplots erroneously assuming that the distance between AE classes dots and treatments dots is an indicative of association. See step by step to interpret biplots correctly are below:

1. Minimum example dataset

2. Interpreting percentage of explained variability by dimensions, center average treatment and AE dot sizes

3. Interpreting dimensions and associations between treatments and AEs

4. Comparing treatments and avoiding misleading interpretations

References

Owner

  • Name: Marcio Augusto Diniz
  • Login: dnzmarcio
  • Kind: user
  • Location: Los Angeles, CA

Ph.D. in Statistics. R Enthusiastic. Bayesian.

GitHub Events

Total
  • Push event: 7
  • Fork event: 1
Last Year
  • Push event: 7
  • Fork event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 52
  • Total Committers: 3
  • Avg Commits per committer: 17.333
  • Development Distribution Score (DDS): 0.404
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Marcio Augusto Diniz d****o@g****m 31
dnzmarcio m****z@c****g 16
Diniz M****z@c****g 5
Committer Domains (Top 20 + Academic)
cshs.org: 2

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 191 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 3
  • Total maintainers: 1
cran.r-project.org: visae

Visualization of Adverse Events

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 191 Last month
Rankings
Forks count: 21.9%
Dependent packages count: 29.8%
Stargazers count: 31.7%
Dependent repos count: 35.5%
Average: 39.5%
Downloads: 78.5%
Maintainers (1)
marcio.diniz@mountsinai.org
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • dplyr >= 1.0.0 depends
  • ggplot2 >= 3.3.0 depends
  • magrittr >= 1.5.0 depends
  • shiny >= 1.4.0 depends
  • DT >= 0.13 imports
  • ca >= 0.71 imports
  • ggrepel >= 0.8.2 imports
  • rlang >= 0.4.6 imports
  • shinyjs >= 1.1 imports
  • tidyr >= 1.1.0 imports
  • knitr * suggests
  • rmarkdown * suggests
.github/workflows/rhub.yaml actions
  • r-hub/actions/checkout v1 composite
  • r-hub/actions/platform-info v1 composite
  • r-hub/actions/run-check v1 composite
  • r-hub/actions/setup v1 composite
  • r-hub/actions/setup-deps v1 composite
  • r-hub/actions/setup-r v1 composite