choxos

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

Core Mediana package functions for Clinical Scenario Evaluation (CSE). Use when designing data models, analysis models, evaluation models, and running comprehensive trial simulations.

Deep methodology knowledge for network meta-analysis including transitivity, consistency assessment, treatment rankings, and model selection. Use when conducting or reviewing NMA.

Foundational knowledge for writing modern Stan models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.

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

Survival analysis in R, including Kaplan-Meier, Cox models, competing risks, RMST, and multi-state models.

Direct and tradeoff-based optimization strategies for clinical trial design. Use when optimizing sample size, selecting design parameters, or performing sensitivity analysis.

Adaptive trial designs in R, including platform, basket, MAMS, response-adaptive, and interim decision methods.

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

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

Real-world evidence analysis in R, including target trial emulation, propensity scores, external controls, and bias analysis.

Common clinical trial design patterns including multi-arm, multi-endpoint, adaptive, and stratified designs. Use when selecting or implementing trial designs.

Deep methodology knowledge for MAIC including assumptions, weight diagnostics, ESS interpretation, and anchored vs unanchored decisions. Use when conducting or reviewing MAIC analyses.

Causal mediation analysis in R, including direct and indirect effects, assumptions, and sensitivity analysis.

Health economic analysis in R, including cost-effectiveness, QALYs, decision models, and budget impact.

Foundational knowledge for writing BUGS/JAGS models including precision parameterization, declarative syntax, distributions, and R integration. Use when creating or reviewing BUGS/JAGS models.

MCMC diagnostics for Bayesian models including convergence assessment, effective sample size, divergences, and posterior predictive checks.

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

Survival analysis methods including weighted logrank, MaxCombo, RMST, and milestone tests. Use when analyzing TTE data or choosing analysis methods for non-proportional hazards.

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

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

Genomics analysis in R with Bioconductor, differential expression, enrichment, batch correction, and single-cell workflows.

Tidymodels workflow patterns with recipes, models, workflows, resampling, tuning, and final evaluation.

Bayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.

Multiple testing procedures reference for clinical trials. Use when selecting or implementing multiplicity adjustments, gatekeeping procedures, or graphical approaches.

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.

Epidemiological analysis methods in R for cohort, case-control, confounding control, and causal inference.

Review patterns for tidymodels workflows, including leakage, resampling, tuning, metrics, and reproducibility.

Patterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.

Foundational knowledge for writing current PyMC models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.

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

Resampling strategies in tidymodels, including validation splits, cross-validation, bootstrap, nested resampling, and grouped data.

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

Group sequential design methods for interim analyses, alpha spending, and futility stopping. Use when designing trials with interim looks or implementing spending functions.

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.

Clinical trial design and analysis methods in R, including randomization, estimands, multiplicity, and reporting.

Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.

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

R package documentation with roxygen2 and pkgdown, including reference topics, articles, and site configuration.

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 projects or building pipelines.

Pairwise meta-analysis in R, including fixed and random effects, heterogeneity, bias checks, and forest plots.

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

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

Hyperparameter tuning in tidymodels with grids, Bayesian optimization, racing, and workflow finalization.

Feature engineering patterns with recipes, including imputation, encoding, normalization, interactions, and leakage control.