choxos/biostatagent
44 skills
$npx skills add choxos/biostatagentSkillInstalls
advanced-adaptive-trialsAdaptive trial designs in R, including platform, basket, MAMS, response-adaptive, and interim decision methods.bayesian-modelingBayesian modeling in R with brms, rstanarm, priors, diagnostics, posterior checks, and model comparison.bugs-fundamentalsFoundational knowledge for writing BUGS/JAGS models including precision parameterization, declarative syntax, distributions, and R integration.causal-mediationCausal mediation analysis in R, including direct and indirect effects, assumptions, and sensitivity analysis.clinical-trial-design-patternsCommon clinical trial design patterns including multi-arm, multi-endpoint, adaptive, and stratified designs. Use when selecting or implementing trial designs.clinical-trialsClinical trial design and analysis methods in R, including randomization, estimands, multiplicity, and reporting.diagnostic-accuracyDiagnostic accuracy analysis in R, including sensitivity, specificity, ROC curves, likelihood ratios, and decision curves.epidemiology-methodsEpidemiological analysis methods in R for cohort, case-control, confounding control, and causal inference.genomics-analysisGenomics analysis in R with Bioconductor, differential expression, enrichment, batch correction, and single-cell workflows.group-sequential-methodsGroup sequential design methods for interim analyses, alpha spending, and futility stopping.health-economicsHealth economic analysis in R, including cost-effectiveness, QALYs, decision models, and budget impact.hierarchical-modelsPatterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.ipd-meta-analysisIndividual participant data meta-analysis in R, including one-stage, two-stage, survival, and IPD with aggregate data.maic-methodologyDeep methodology knowledge for MAIC including assumptions, weight diagnostics, ESS interpretation, and anchored vs unanchored decisions.mediana-fundamentalsCore Mediana package functions for Clinical Scenario Evaluation (CSE).mendelian-randomizationMendelian randomization in R, including instrument selection, two-sample MR, pleiotropy checks, and sensitivity analysis.meta-analysisBayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.ml-nmr-methodologyDeep methodology knowledge for ML-NMR including IPD/AgD integration, population adjustment, numerical integration, and prediction to target populations.model-diagnosticsMCMC diagnostics for Bayesian models including convergence assessment, effective sample size, divergences, and posterior predictive checks.model-evaluationModel evaluation in R with performance metrics, calibration, ROC analysis, decision curves, and validation.model-tuningHyperparameter tuning in tidymodels with grids, Bayesian optimization, racing, and workflow finalization.multiplicity-methodsMultiple testing procedures reference for clinical trials. Use when selecting or implementing multiplicity adjustments, gatekeeping procedures, or graphical…network-meta-analysisNetwork meta-analysis in R, including network setup, consistency, treatment rankings, and league tables.nma-methodologyDeep methodology knowledge for network meta-analysis including transitivity, consistency assessment, treatment rankings, and model selection.pairwise-ma-methodologyDeep methodology knowledge for pairwise meta-analysis including fixed vs random effects, heterogeneity assessment, publication bias, and sensitivity analysis.pharmacokineticsPharmacokinetic and pharmacodynamic analysis in R, including NCA, compartmental modeling, and bioequivalence.power-optimization-patternsDirect and tradeoff-based optimization strategies for clinical trial design.pymc-fundamentalsFoundational knowledge for writing current PyMC models including syntax, distributions, sampling, and ArviZ diagnostics.r-documentation-patternsR documentation patterns with roxygen2, pkgdown, vignettes, examples, and package site structure.real-world-evidenceReal-world evidence analysis in R, including target trial emulation, propensity scores, external controls, and bias analysis.recipes-patternsFeature engineering patterns with recipes, including imputation, encoding, normalization, interactions, and leakage control.regression-modelsBayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.resampling-strategiesResampling strategies in tidymodels, including validation splits, cross-validation, bootstrap, nested resampling, and grouped data.roxygen2-pkgdownR package documentation with roxygen2 and pkgdown, including reference topics, articles, and site configuration.simtrial-fundamentalsCore simtrial package functions for time-to-event clinical trial simulation.stan-fundamentalsFoundational knowledge for writing modern Stan models including program structure, type system, distributions, and best practices.stc-methodologyDeep methodology knowledge for STC including outcome regression, effect modifier selection, covariate centering, and comparison with MAIC.survival-analysisSurvival analysis in R, including Kaplan-Meier, Cox models, competing risks, RMST, and multi-state models.survival-modelsBayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.tidy-itc-workflowMaster tidy modelling patterns for ITC analyses following TMwR principles.tidymodels-review-patternsReview patterns for tidymodels workflows, including leakage, resampling, tuning, metrics, and reproducibility.tidymodels-workflowTidymodels workflow patterns with recipes, models, workflows, resampling, tuning, and final evaluation.time-series-modelsBayesian time series models including AR, MA, ARMA, state-space models, and dynamic linear models in Stan and JAGS.time-to-event-methodsSurvival analysis methods including weighted logrank, MaxCombo, RMST, and milestone tests.