SAS PROC MIXED for Repeated Measures
A practical guide to SAS PROC MIXED for repeated-measures analysis, including longitudinal clinical-trial data, fixed effects, covariance structures, repeated statements, LS-means, Kenward-Roger degrees of freedom, missing data, model selection, interpretation, and validation.
SAS PROC LOGISTIC Walkthrough
A practical, end-to-end walkthrough of SAS PROC LOGISTIC for binary, ordinal, and multinomial logistic regression, including CLASS variables, odds ratios, model selection, diagnostics, ROC analysis, predicted probabilities, clinical-trial examples, and reporting.
SAS PROC LIFETEST for Survival Analysis
A practical guide to SAS PROC LIFETEST for clinical trial survival analysis, including Kaplan-Meier estimation, censoring, median survival, confidence intervals, log-rank tests, stratified analysis, output data sets, survival plots, and practical SAS programming patterns.
SAS PROC GLIMMIX Explained
A practical guide to SAS PROC GLIMMIX for generalized linear mixed models, including binary, count, ordinal, repeated-measures, clustered, longitudinal, fixed-effect, random-effect, covariance, estimation, odds-ratio, least-squares-means, and clinical-trial applications.
SAS PROC FREQ for Categorical Data Analysis
A practical guide to SAS PROC FREQ for categorical data analysis, including one-way frequency tables, two-way contingency tables, chi-square tests, Fisher
RECIST 1.1 Endpoints in ADaM Datasets
An advanced guide to representing RECIST 1.1 oncology endpoints in ADaM datasets, including overall response, best overall response, objective response rate, duration of response, time to response, progression-free survival, response derivation, BDS structures, ADTTE, traceability, programming examples, 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.