GitHub
bugs-fundamentals
Foundational knowledge for writing BUGS/JAGS models including precision parameterization, declarative syntax, distributions, and R integration. Use when creating or reviewing BUGS/JAGS models.
choxos AI Skills
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GitHub
multiplicity-methods
Multiple testing procedures reference for clinical trials. Use when selecting or implementing multiplicity adjustments, gatekeeping procedures, or graphical approaches.
GitHub
time-series-models
Bayesian time series models including AR, MA, ARMA, state-space models, and dynamic linear models in Stan and JAGS.
GitHub
simtrial-fundamentals
Core simtrial package functions for time-to-event clinical trial simulation. Use when generating survival data, performing weighted logrank tests, or running TTE simulations.
GitHub
clinical-trial-design-patterns
Common clinical trial design patterns including multi-arm, multi-endpoint, adaptive, and stratified designs. Use when selecting or implementing trial designs.
GitHub
maic-methodology
Deep methodology knowledge for MAIC including assumptions, weight diagnostics, ESS interpretation, and anchored vs unanchored decisions. Use when conducting or reviewing MAIC analyses.
GitHub
power-optimization-patterns
Direct and tradeoff-based optimization strategies for clinical trial design. Use when optimizing sample size, selecting design parameters, or performing sensitivity analysis.
GitHub
stan-fundamentals
Foundational knowledge for writing Stan 2.37 models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.
GitHub
time-to-event-methods
Survival analysis methods including weighted logrank, MaxCombo, RMST, and milestone tests. Use when analyzing TTE data or choosing analysis methods for non-proportional hazards.
GitHub
nma-methodology
Deep methodology knowledge for network meta-analysis including transitivity, consistency assessment, treatment rankings, and model selection. Use when conducting or reviewing NMA.
GitHub
hierarchical-models
Patterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.
GitHub
model-diagnostics
MCMC diagnostics for Bayesian models including convergence assessment, effective sample size, divergences, and posterior predictive checks.
GitHub
tidy-itc-workflow
Master tidy modelling patterns for ITC analyses following TMwR principles. Covers workflow structure, consistent interfaces, reproducibility best practices, and data validation. Use when setting up ITC analysis...
GitHub
meta-analysis
Bayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.
GitHub
survival-models
Bayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.
GitHub
ml-nmr-methodology
Deep methodology knowledge for ML-NMR including IPD/AgD integration, population adjustment, numerical integration, and prediction to target populations. Use when conducting or reviewing ML-NMR analyses.
GitHub
regression-models
Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.
GitHub
mediana-fundamentals
Core Mediana package functions for Clinical Scenario Evaluation (CSE). Use when designing data models, analysis models, evaluation models, and running comprehensive trial simulations.
GitHub
pymc-fundamentals
Foundational knowledge for writing PyMC 5 models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.
GitHub
pairwise-ma-methodology
Deep methodology knowledge for pairwise meta-analysis including fixed vs random effects, heterogeneity assessment, publication bias, and sensitivity analysis. Use when conducting or reviewing pairwise MA.
GitHub
group-sequential-methods
Group sequential design methods for interim analyses, alpha spending, and futility stopping. Use when designing trials with interim looks or implementing spending functions.
GitHub
stc-methodology
Deep methodology knowledge for STC including outcome regression approach, effect modifier selection, covariate centering, and comparison with MAIC. Use when conducting or reviewing STC analyses.
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