Heterogeneity in Meta-Analysis: I-squared and Tau-squared
A practical guide to heterogeneity in meta-analysis, explaining I-squared versus tau-squared, Cochran
Handling Multiple Visits in SDTM
A practical guide to handling multiple visits in SDTM, including scheduled and unscheduled visits, VISIT, VISITNUM, VISITDY, study day, Subject Visits, repeated assessments, multi-day visits, visit windows, treatment cycles, mapping strategies, programming examples, R and SAS implementation, and validation.
Generalized Estimating Equations (GEE) Explained
A deep, computationally grounded guide to GEE: Fisher scoring and the sandwich estimator derived and implemented from scratch, validated against production software, with a real demonstration of population-averaged vs. subject-specific coefficient divergence and GEE
Fixed-Effect vs. Random-Effects Meta-Analysis
A practical guide to fixed-effect and random-effects meta-analysis, including study weights, inverse-variance weighting, heterogeneity, tau-squared, I-squared, prediction intervals, forest plots, model interpretation, and R implementation.
Dose Expansion Cohorts in Oncology Trials
A practical guide to dose expansion cohorts in oncology trials, covering the transition from dose escalation to expansion, cohort objectives, sample size considerations, safety, efficacy, PK/PD, biomarkers, statistical operating characteristics, and a complete worked example.
Define.xml: What It Is and Why It Matters
A practical guide to Define-XML for clinical programmers, covering what Define-XML is, why it matters, datasets, variables, codelists, value-level metadata, origins, computational methods, document references, validation, regulatory submission, and practical implementation.
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.
Credible Intervals vs. Confidence Intervals
A practical guide to credible intervals versus confidence intervals, explaining the Bayesian and frequentist interpretations, mathematical differences, worked examples, posterior probability, coverage probability, and implementation in R.
Creating RTF Outputs with SAS ODS
A practical guide to creating RTF outputs with SAS ODS, including ODS RTF syntax, styles, titles, footnotes, page breaks, PROC REPORT, ODS Graphics, multi-table documents, debugging, and validation.
Creating ADTTE: Time-to-Event Analysis Datasets
A practical guide to creating ADTTE time-to-event analysis datasets for clinical trials, including AVAL, CNSR, STARTDT, ADT, PARAMCD, event dates, censoring, derivations, analysis populations, Kaplan-Meier analysis, SAS and R implementation, and quality control.