Sample size estimation for clinical trials is challenging when hypothesis tests involve comparisons across multiple endpoints and/or treatments. This situation commonly arises in biosimilar trials, where pharmacokinetic (PK) parameters of interest, bioequivalence criteria and reference products may differ between regulatory bodies. We developed the simsamplesize R package to facilitate sample size estimation for Phase 1 randomized bioequivalence trials where multiple hypotheses, treatments and/or correlated endpoints are being investigated. Unlike deterministic approaches commonly used in existing R packages and sample size software, simsamplesize focuses on simulation-based sample size estimations. This enables researchers to address the complexities of evaluating multiple hypotheses across diverse (co-)primary endpoints, as is commonly the case in biosimilar trials. Users can configure various parameters with respect to randomization scheme, distributional assumptions, multiplicity corrections, selection and integration of hypothesis tests, and missing data. We illustrate the use of the R package and Shiny app for a trial assessing PK biosimilarity against two reference products with respect to PK parameters: AUC from Time Zero to Infinity, Cmax, AUC From Time Zero to the Last Quantifiable Concentration, and/or Last Serum Concentration.

The R package is intended for clinical trial statisticians with a basic understanding of R, clinical trial design and sample size calculations. The package also features a Shiny app that can be used by researchers and decision makers (e.g., medical affairs/clinical) to visualize results and explore the impact of various assumptions on the sample size.

Find out more about the simsamplesize solution here.