Sample size estimation in clinical trials is particularly complex when multiple endpoints, treatments, and hypotheses are involved. This challenge is commonly encountered in bioequivalence trials, where pharmacokinetic parameters, bioequivalence criteria, and reference products often vary across regulatory bodies. Traditional deterministic methods used in existing tools are limited in handling the intricacies of such trials, leading to potential underpowered or overpowered studies.

Introducing simsamplesize

A Game-Changing Tool for Bioequivalence Trial Design

We developed the simsamplesize R package and Shiny app to streamline sample size estimation for Phase 1 randomized bioequivalence trials. This powerful solution leverages simulation-based methods, allowing researchers to handle multiple hypotheses, treatments, and correlated endpoints with greater flexibility and accuracy. Unlike conventional methods, simsamplesize addresses the complexities of biosimilar trials, offering more accurate and reliable estimations.

Features that Make the Difference

Discover our user-friendly tool to streamline clinical trial design. It tackles the unique challenges of biosimilar studies, offering enhanced flexibility, customization, and ease of use for researchers at all levels. Key features include the following:

• Simulation-Based Approach: Achieve greater flexibility by aligning sample size calculations with the specific needs of your trial. Our approach allows you to handle scenarios where no closed-form equation exists, providing more tailored and adaptable solutions compared to traditional approaches.
• Multi-Endpoint Support: Seamlessly handle multiple (co-)primary endpoints and hypotheses, a critical need in biosimilar trials.
• Customizable to Regulatory Requirements: Tailor your analysis to specific pharmacokinetic parameters and bioequivalence criteria, ensuring compliance with regulatory demands across different countries.
• User-Friendly Interface: An integrated Shiny app makes it easy for researchers of all skill levels to perform complex estimations without needing to write extensive R code.

Key Features

Call to Action

Ready to simplify your bioequivalence trial design? Install simsamplesize today and explore how our solution can take your clinical trials to the next level.

Case Studies

A mid sized global biopharmaceutical company (“Sponsor”) wanted to more strategically design its Phase 1 biosmilars program in order to broaden the asset’s market potential. Sponsor found that with a more strategic design, specifically, simulation studies for sample size estimation, the Phase 1 program could meet all market requirements in a single trial and be in a much stronger position to gain many market approvals. To do so, highly advanced clinical trial modeling and simulation was required. Sponsor looked for possible existing solutions but, despite a thorough review, the available tools were too simplistic, and and could not accommodate the program’s needs.

Drug Sponsor Predictive Modeling Options:

• Use Internal Resources: All existing resources are otherwise occupied, skills are not as readily available, and may not be available internally.
• Hire FTE/s:
  • The business unit’s operating model relies on 60-70% outsourcing, making it inefficient to add a full-time employee for a single, 1.5-year part-time project.
  • Other challenges include the administrative burden of managing new hires, the HR burden of finding talent, and the need for training.
• Use one of the two full-service CROs already engaged:
  • Each CRO would have required more team members, with only partial skills necessary for the project.
  • Engaging with traditional CROs often necessitates additional project management resources, introducing unnecessary complexity and increasing costs. Larger teams tend to be less agile, which can negatively impact both speed and budget.
• Selected Option: "The best in the field of simulation studies for clinical trials is Thomas Debray, SDAS."
  

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