Creating ADAE: Adverse Event Analysis Datasets
A practical guide to creating ADAE adverse event analysis datasets for clinical trials, including SDTM AE to ADAE derivation, treatment-emergent adverse events, analysis dates, analysis days, severity, seriousness, relationship, occurrence flags, subject-level treatment variables, data structures, derivations, quality control, and R implementation.
Cox Proportional Hazards: Model Diagnostics
A deep, computationally grounded guide to the Cox proportional hazards model: the partial likelihood derived from first principles, a real Newton-Raphson fit, the exact log-rank/score-test equivalence, Breslow vs Efron tie handling, Schoenfeld residuals, and a genuine non-proportional-hazards worked example.
Continual Reassessment Method (CRM)
A computational guide to the Continual Reassessment Method (CRM) for dose-finding clinical trials, including Bayesian dose-toxicity modeling, prior dose skeletons, posterior updating, dose selection, cohort escalation, operating characteristics, and a complete worked example.
Competing Risks Analysis in Clinical Trials
A deep, computationally grounded guide to competing risks: the real bias from treating competing events as censoring, the Aalen-Johansen cumulative incidence function, and cause-specific vs. Fine-Gray subdistribution hazard models implemented and compared from scratch.
Common CDISC Submission Pitfalls
A practical guide to common CDISC regulatory submission pitfalls, including SDTM, ADaM, Define-XML, controlled terminology, traceability, validation findings, metadata, dataset structure, reviewer usability, and submission readiness.
Combination Dose-Finding Designs
A practical guide to combination dose-finding designs for early-phase clinical trials, including dose-combination matrices, toxicity targets, escalation rules, model-assisted and model-based approaches, operating characteristics, and a worked example.
CDISC Controlled Terminology Explained
A practical guide to CDISC Controlled Terminology, including codelists, submission values, extensible terminology, CT packages, SDTM and ADaM implementation, validation, common programming mistakes, and practical examples.
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.
Bayesian vs. Frequentist Inference: Key Differences
A practical and mathematical guide to Bayesian versus frequentist inference, including parameters, probability, priors, likelihoods, posterior distributions, p-values, confidence intervals, credible intervals, hypothesis testing, clinical trial examples, and when each framework is useful.
Bayesian Sample Size Determination
A practical and computational guide to Bayesian sample size determination, including prior distributions, posterior probabilities, assurance, posterior precision, decision criteria, a complete binary-outcome worked example, and R implementation.