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.
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.
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:
Ready to simplify your bioequivalence trial design? Install simsamplesize today and explore how our solution can take your clinical trials to the next level.
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:
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17
Jun
2024 PSI Annual Conference, Amsterdam, The Netherlands
The best in the field of simulation studies for clinical trials, Thomas Debray, SDAS. Director of Biostatistics, Drug Sponsor
Traditional statistical power often required larger patient samples, leading to longer, more expensive trials and potentially delayed or reduced revenue opportunities. Director of Biostatistics, Drug Sponsor
We knew existing tools were too simple. We knew the complexity we wanted. SDAS got us there. Director of Biostatistics, Drug Sponsor
Our Head of R&D, along with the Heads of Clinical Development and Operations, are very proud of what we've achieved with SDAS. Director of Biostatistics, Drug Sponsor
Such an elegant solution. Even though on the surface it looks simple. Director of Biostatistics, Drug Sponsor
SDAS designed it to be extremely user-friendly. It can be used on other trials, and also by non-statisticians. Director of Biostatistics, Drug Sponsor
When designing a traditional trial, estimating sample size is relatively straightforward — simply input a few parameters into standard software packages. However, without the right tools and expertise to add layers of complexity, you're forced to rely on strong assumptions. In drug development, no one wants to make decisions based on such assumptions. Fortunately, with modern advancements in clinical trial methodology, this reliance is no longer necessary. Director of Biostatistics, Drug Sponsor