choxos

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

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

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

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

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

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

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

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

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

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

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

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.