This is a Shiny app using some (simulated) clinical data to visualize the results of a clinical trial for the fictive drug “Drug X”.
An online demo of the app can be found at https://teebusch.shinyapps.io/drugx/
You can install and run the app locally from Github with:
# Install devtools if needed
if(!require(devtools)) install.packages("devtools")
devtools::install_github("Teebusch/drugx")
library(drugx)
drugx::run_app()- One study, 9492 observations
- 3 arms (A = drug, B = placebo, C = combination)
- ~150 patients in each arm
- 7 Repeated measures: screening, baseline, 5 weekly follow-ups (day 8-36)
- consistent, complete data (except some “undifferentiated” / “other” patient info)
- Age
- Sex
- Race
- Biomarker 1 (numerical; Mean around 6, range 0.3-22.4)
- Biomarker 2 (categorical; low, medium, high)
- ALT is increased with liver damage and is used to screen for and/or monitor liver disease
- CRP is a blood test marker for inflammation in the body (e.g., chronic inflammatory diseases such as lupus, vasculitis, or rheumatoid arthritis (RA)
- IGA high levels might be caused by allergies, chronic infections, autoimmune disorders such as RA
- change compared to baseline
- difference between drug X / placebo / combination (group difference between arms)
- Age
- Sex
- 2 Biomarkers
- Screening / Baseline values
- Shiny application to explore the properties of the data sources.
- minimum of 2 different visualization plots
- use of a version control
- formated as a RStudio project, or gzip R Package.
- delivered within 8 days of receiving these instructions.
- 4-5 hours of work
- help explore associations between background variables and treatment
- filter data to visualize subgroups
- allow high level overview as well as investigating subgroups and individuals
- comparing data in different study arms
- sanity checks / cleaning of supplied data
- graphs of background variables (for all data and by study arm)
- allow to filter data by age, race, sex, biomarkers 1 and 2, performance at baseline subjects
- graph that shows development of ALT, CRP, IGA for individuals
- graph that shows development of ALT, CRP, IGA by study arm (summary)
- deliver as R package
- allow data to filter by performance of screening, individual patient
- complete function documentation
- optimize loading times by refactoring data transforms and filtering
- comprehensive test suite
- statistical models with significance tests
- interactive plotly plots with hover functionality
- tables with summary statistics
- What is being done in the “combination” condition?