choxos/BiostatAgent

meta-analysis

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

Quellsprache: Englisch

pymc-fundamentals

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

Quellsprache: Englisch

stan-fundamentals

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

Quellsprache: Englisch

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.

Quellsprache: Englisch

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.

Quellsprache: Englisch

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.

Quellsprache: Englisch

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.

Quellsprache: Englisch

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.

Quellsprache: Englisch

nma-methodology

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

Quellsprache: Englisch

stc-methodology

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

Quellsprache: Englisch

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 projects or building pipelines.

Quellsprache: Englisch

advanced-adaptive-trials

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

Quellsprache: Englisch

bayesian-modeling

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

Quellsprache: Englisch

causal-mediation

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

Quellsprache: Englisch

clinical-trials

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

Quellsprache: Englisch

diagnostic-accuracy

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

Quellsprache: Englisch

epidemiology-methods

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

Quellsprache: Englisch

genomics-analysis

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

Quellsprache: Englisch

health-economics

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

Quellsprache: Englisch

ipd-meta-analysis

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

Quellsprache: Englisch

mendelian-randomization

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

Quellsprache: Englisch

meta-analysis

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

Quellsprache: Englisch

model-evaluation

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

Quellsprache: Englisch

model-tuning

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

Quellsprache: Englisch

network-meta-analysis

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

Quellsprache: Englisch

pharmacokinetics

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

Quellsprache: Englisch

r-documentation-patterns

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

Quellsprache: Englisch

real-world-evidence

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

Quellsprache: Englisch

recipes-patterns

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

Quellsprache: Englisch

resampling-strategies

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

Quellsprache: Englisch

roxygen2-pkgdown

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

Quellsprache: Englisch

survival-analysis

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

Quellsprache: Englisch

tidymodels-review-patterns

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

Quellsprache: Englisch

tidymodels-workflow

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

Quellsprache: Englisch

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.

Quellsprache: Englisch

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.

Quellsprache: Englisch

hierarchical-models

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

Quellsprache: Englisch

model-diagnostics

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

Quellsprache: Englisch

regression-models

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

Quellsprache: Englisch

survival-models

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

Quellsprache: Englisch