Mixed Models in R: lme4 and nlme
A practical and in-depth guide to mixed-effects models in R using lme4 and nlme, with clinical trial examples covering longitudinal repeated measures, random intercepts and slopes, covariance structures, treatment-by-time interactions, REML versus ML, estimated marginal means, diagnostics, convergence, missing data, and reporting.
Data Manipulation with dplyr for Clinical Data
An advanced practical guide to data manipulation with dplyr for clinical data, including filtering, selecting, mutating, grouping, joins, baseline derivations, longitudinal records, visit selection, window functions, treatment data, ADaM-style analysis datasets, validation, and robust R programming practices.
Building a Shiny App for Clinical Data Review
A practical guide to building R Shiny applications for clinical data review, including reactive filtering, subject-level review, laboratory data, adverse events, data-quality flags, interactive tables, plots, downloads, validation, security, and deployment.
Automating TLF Production Pipelines in R
A practical guide to automating clinical trial Tables, Listings, and Figures in R, including ADaM-driven programming, reusable functions, specifications, validation, QC, logging, reproducibility, batch execution, and production pipeline design.