studysim: generate simulated medical studies for jamovi
A jamovi module for teaching epidemiology and clinical research. Pick a clinical
scenario, an outcome, a study design and a sample size, and get a
plausible dataset in a new jamovi window. A hidden answer key gives the true
parameters and what the analyses should find.
Analyses (menu: Simulate)
- Simulated study: one dataset from a scenario, outcome and design
(observational, trials, diagnostic), with optional realism and biases, and
its answer key.
- Compare designs: one scenario under several designs, estimates against
the truth.
- Library: lists, checks, explores and exports scenarios.
- Derive scenario: estimates a new scenario from a real dataset.
Install
Download the .jmo for your computer from the Releases page
(x64 for Intel/AMD, arm64 for Apple silicon) and in jamovi choose
Modules (⊕) → Sideload. Requires jamovi 28.4 or newer; no other packages.
Developers
Rscript tools/build-library-js.R # after editing inst/library
bash tools/build-jmo.sh current # dist/*.jmo
bash tools/build-ai-skill.sh # dist/studysim-scenario-skill.zip
Rscript -e 'pkgload::load_all("."); testthat::test_dir("tests/testthat")'
The engine (R/engine_*.R, R/derive.R) is plain R, independent of jamovi.
tests/ui/*.py and tutorial/screenshots/capture.py drive jamovi in Docker with
the jamovi-automation helpers; the tutorial is a Quarto site in tutorial/,
rendered to docs/.
Acknowledgements
Designed and built together with Claude Code, over several rounds of design,
implementation and testing in jamovi. Ideas come from simstudy, simDAG, PRIME-CVD
and The Islands; no code was copied. Survival models use the R package survival
by Terry Therneau; mixed models in the answer key use nlme (Pinheiro, Bates and
the R Core Team). Scenario parameters were estimated from datasets distributed
with the R packages survival (Therneau), MASS (Venables and Ripley), datasets
(R Core Team), HSAUR3 (Everitt and Hothorn), TH.data (Hothorn), pROC (Robin et
al.), medicaldata (Higgins), aplore3 (Braglia) and NHANES (Pruim); we thank the
original investigators, cited in each scenario.
This module has been developed with support of the Instituto de Salud Carlos III
(ISCIII), “Programa FORTALECE del Ministerio de Ciencia e Innovación”, through the
project number FORT23/00032 and the Consortium for Biomedical Research in
Epidemiology and Public Health (CIBERESP), action Genrisk.
Licence
GPL (>= 2).
