choxos

Bayesian modeling in R with brms, rstanarm, priors, diagnostics, posterior checks, and model comparison.

Deep methodology knowledge for STC including outcome regression, effect modifier selection, covariate centering, and comparison with MAIC. Use when conducting or reviewing STC analyses.

Network meta-analysis in R, including network setup, consistency, treatment rankings, and league tables.

Bayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.

Pharmacokinetic and pharmacodynamic analysis in R, including NCA, compartmental modeling, and bioequivalence.

R documentation patterns with roxygen2, pkgdown, vignettes, examples, and package site structure.

Individual participant data meta-analysis in R, including one-stage, two-stage, survival, and IPD with aggregate data.

Core simtrial package functions for time-to-event clinical trial simulation. Use when generating survival data, performing weighted logrank tests, or running TTE simulations.

Mendelian randomization in R, including instrument selection, two-sample MR, pleiotropy checks, and sensitivity analysis.

Model evaluation in R with performance metrics, calibration, ROC analysis, decision curves, and validation.

Bayesian time series models including AR, MA, ARMA, state-space models, and dynamic linear models in Stan and JAGS.

Diagnostic accuracy analysis in R, including sensitivity, specificity, ROC curves, likelihood ratios, and decision curves.