gtsummary Package for Clinical Tables
A practical guide to the gtsummary R package for clinical trial tables, including baseline characteristics, descriptive statistics, p-values, regression tables, survival summaries, formatting, customization, reporting workflows, and validation.
ggplot2 for Clinical Trial Visualizations
A practical guide to using ggplot2 for clinical trial visualizations, including longitudinal laboratory plots, adverse event charts, boxplots, spaghetti plots, forest plots, Kaplan-Meier displays, treatment comparisons, clinical reporting standards, and R implementation.
Writing Reusable R Functions for TLF Generation
A practical guide to writing reusable R functions for clinical trial Tables, Listings, and Figures (TLFs), including function design, arguments, validation, formatting, parameterization, debugging, testing, and production-ready reporting workflows.
Version Control with Git for Biostatisticians
A practical guide to using Git version control in clinical biostatistics, including repositories, commits, branches, merges, pull requests, code review, R and RStudio workflows, TLF development, reproducibility, renv, .gitignore, merge conflicts, tagging, and regulatory considerations.
Survival Analysis in R: The survival Package
A practical guide to survival analysis in R using the survival package, including time-to-event data, censoring, Kaplan-Meier curves, log-rank tests, Cox proportional hazards models, hazard ratios, confidence intervals, model interpretation, diagnostics, clinical trial applications, and reproducible R workflows.
Simulating Clinical Trial Data in R
A practical guide to simulating realistic clinical trial data in R, including treatment assignment, baseline covariates, longitudinal endpoints, binary and time-to-event outcomes, missing data, dropout, treatment effects, reproducible simulation, Monte Carlo operating characteristics, and validation.
R vs. SAS: A Comparison for Clinical Programmers
A practical comparison of R and SAS for clinical programmers, covering data manipulation, SQL, statistical analysis, clinical trial datasets, ADaM derivations, TLF programming, macros, functions, missing values, validation, reproducibility, regulatory submissions, and practical R versus SAS examples.
R for Clinical Trial Statisticians: Getting Started
A practical introduction to R for clinical trial statisticians and statistical programmers, covering R fundamentals, RStudio projects, data manipulation, clinical trial datasets, ADaM concepts, statistical analysis, visualization, functions, validation, reproducibility, and practical R workflows.
R Package Validation for Regulatory Submissions
A practical guide to validating R packages and R-based statistical workflows for regulated clinical-trial use, including risk assessment, intended use, requirements, test cases, reference outputs, dependency control, reproducible environments, change control, validation reports, and submission-ready documentation.
R Markdown for Clinical Study Reports
A practical guide to using R Markdown for Clinical Study Reports, including reproducible analysis, YAML configuration, R code chunks, clinical tables, figures, parameterized reports, Word and PDF output, references, quality control, traceability, and regulatory reporting workflows.