Understanding ICH E9 Statistical Principles
A practical guide to ICH E9 and ICH E9(R1) statistical principles for clinical trials, including trial objectives, endpoints, estimands, randomization, analysis populations, bias, missing data, multiplicity, interim analyses, sample size, sensitivity analyses, and reporting.
Subgroup Analysis: Regulatory Expectations
A practical guide to subgroup analysis in clinical trials, including FDA and EMA regulatory expectations, prespecification, treatment-by-subgroup interactions, heterogeneity, multiplicity, demographic subgroups, forest plots, small sample sizes, interpretation, reporting, and implementation in R.
Statistical Considerations for Rare Disease Trials
A practical guide to statistical considerations for rare disease clinical trials, including small sample sizes, endpoint selection, natural history, external controls, randomization, Bayesian methods, adaptive designs, missing data, multiplicity, estimands, and regulatory considerations.
Real-World Evidence in Regulatory Submissions
A practical guide to using real-world data and real-world evidence in regulatory submissions, including regulatory questions, data fitness, study design, target trial emulation, confounding, missing data, causal inference, transportability, FDA and EMA expectations, submission documentation, and R implementation.
Multiplicity Guidance from FDA and EMA
A practical guide to FDA and EMA expectations for multiplicity in clinical trials, including Type I error control, multiple primary endpoints, hierarchical testing, gatekeeping, alpha recycling, subgroup analyses, interim analyses, confidence intervals, estimands, SAP planning, and R implementation.
ICH E9(R1) Estimands: Practical Implementation
A practical guide to implementing ICH E9(R1) estimands in clinical trials, including the five estimand attributes, intercurrent events, treatment-policy, hypothetical, composite, while-on-treatment and principal-stratum strategies, estimators, sensitivity analyses, SAP implementation, and practical examples.
FDA Guidance on Missing Data in Clinical Trials
A practical guide to FDA expectations for preventing, documenting, analyzing, and assessing the sensitivity of missing data in clinical trials, including estimands, intercurrent events, MAR and MNAR assumptions, multiple imputation, pattern-mixture models, tipping-point analyses, and clinical-trial reporting.
FDA Guidance on Adaptive Trial Designs
A practical guide to FDA
EMA Statistical Guidance Overview
A practical overview of EMA statistical guidance for clinical trials, including ICH E9, estimands, multiplicity, missing data, subgroup analysis, covariate adjustment, survival analysis, adaptive designs, confirmatory trials, and practical implementation.
Biosimilar Statistical Assessment Requirements
A practical guide to statistical assessment requirements for biosimilar development, covering analytical similarity, quality attributes, acceptance ranges, PK and PD equivalence, clinical efficacy, immunogenicity, sample size, confidence intervals, FDA and EMA expectations, estimands, sensitivity analyses, and R implementation.