After registration, you will have created an account on U-Design website. Once logged into your account, you will be able to access all the software functions, such as does-finding designs, decision table generation and sample size calculations, etc. A complete history of results of running simulations for various clinical trial designs on U-Design will be stored in your account.
The only SaaS platform enabling head-to-head comparison of different innovative adaptive designs on demand.
Ready generation of enterprise-grade protocol templates that have statistical sections filled in with simulation results for real-world clinical trials.
The simulation results are reproducible and can be shared with regulatory agencies seamlessly.
U-Design is 100% free to sign up and all functions are accessible once logged in.
FDA Advancing Complex Adaptive, Bayesian & Other Novel Designs
FDA conducted a public workshop on March 20th, 2018 to discuss the use of complex innovative designs (CID) in clinical trials of drugs and biological products to inform regulatory decision making.
FDA launched a pilot program in August 2018 for sponsors planning to use innovative trial designs that would provide substantial evidence of effectiveness.
“The adoption of novel clinical trial designs and methods for analyzing data are a key to advancing innovation in the development of drug and biologics for hard to treat medical conditions”, said former FDA Commissioner Scott Gottlieb, M.D. in August 2018.
Novel Designs Adoption Made Easy
Greatly simplifies the adoption of novel designs for dose finding, making it possible to create such designs without programming efforts or even by non-statisticians, all with a few button clicks.
Cost Saving & Patient Safety
More efficient dose finding, enhanced patient safety and significant overall cost savings for phase 1 clinical trial resulted from shortened trial duration and optimized sample size, by readily applying novel designs.
Better Decision Making
Enables thorough examination and comparison of operating characteristics of different dose finding designs effected by convenient simulations.
Difficult To Implement
Novel designs for dose finding are often based on complex mathematical modeling and algorithms, which are difficult and time-consuming to implement
Currently, the use of novel designs for real-world trials requires ad-hoc computer programs written by advanced statisticians and reviewed by regulatory agents in a case-by-case fashion
HOW IT WORKS
Statistically Reliable Results
It takes a large number of simulations of model based complex designs to generate statistically reliable results, which helps to make sure that clinical and scientific questions can be addressed with high probability
FDA requires computer simulations for complex adaptive and Bayesian designs to determine their operating characteristics
Help these involved to gain clear understanding of a complex design’s operating characteristics and how design choices affect the outcome of the trial
Facilitate communications and provide justification of the design for the study team, regulator and sponsor, etc.
Bayesian Adaptive Designs
Offers a wide selection of best-in-class innovative Bayesian adaptive designs
A simple user interface for both clinicians and statisticians to run simulations with a few button clicks
Automatic generation of simulation results report that can be used for trial protocol statistical section
A set of tools and utilities to facilitate dose finding design executions
Cloud based and runs in AWS with high availability and tight security
Accessible from any device with a browser (PC, Laptops, iPad, smart phone, etc.) at any time
Statistical modules built in C++ and deployed as distributed services that can be scaled up easily
Subscription based individual account and multi-user corporate account
And many more ...
This is the simplified version of the single-agent cohort-based dose finding designer. Many inputs are preset, such as scenarios and the number of simulations to run. However, it produces and presents results the same way as that for the full version. It is intended as a quick demonstration of how U-Design works.
Single-Agent Cohort-Based Designs
An integrated tool supporting the simulation-based comparison among 7 main-stream dose-finding designs. This module provides both the modern Bayesian model-based designs, including the i3+3 design (Liu et al., 2019), the mTPI design (Ji et al., 2010), the mTPI-2 design (Guo et al., 2017), the continual reassessment method (CRM) (O'Quigley et al., 1990), and the Bayesian logistic regression method (BLRM) (Neuenschwander et al., 2008), and the algorithm-based designs, including the 3+3 design and the modified cumulative cohort design (mCCD; the original CCD design was introduced in (Ivanova et al., 2007).
Single-Agent Rolling-Based Designs
Targeting the key point of time-consuming clinical trials, the module of Rolling-Based Designs is an innovative tool that allows users to compare how long a trial would take under different designs in real-life enrollment settings. This module includes rolling-based designs (rolling six (Skolnik et al., 2008) and R-TPI (Guo et al., 2019) that aim to accelerate phase 1 trials, and cohort-based designs (3+3 and mTPI-2 (Guo et al., 2017)) in which an additional “decision-in-advance” rule is applied to further mimic the real-life trials. This module for rolling-based designs is the only tool on the market that incorporates the comparison of trial duration among different designs and up to four designs can be compared side-by-side.
Single-Agent Decision & MTD
A simple-to-use tool that includes
- Decision Table: The decision tables can be generated for the i3+3, mTPI, mTPI-2, mCCD and 3+3 designs, which can be used to conduct a phase I dose-finding trial. The CRM and BLRM designs do not provide decision tables before the trial is started. However, for these designs we provide empirical decision tables after running simulations.
- MTD Estimation: Based on the Pool Adjacent Violators Algorithm (PAVA), the MTD can be estimated when the trial is completed and data collected.
FREQUENTLY ASKED QUESTIONS
Yes, registration is completely free. Please go to the Registration page to register.
Yes, all software functions available on U-Design are free to use except a paid subscription plan is needed to run more than 10 simulated trials at one time for a trial scenario for the selected design. To fully evaluate the operating characteristics of a design, typically 1,000 simulated trials are needed for a trial scenario as consented in the majority of literature. This is because only with a large number of simulated trials can the average performance of a design be meaningfully evaluated when patient outcomes are random. Running 10 simulated trials helps to illustrate how U-Design will generate and display simulation results, but to make meaningful comparison and assessment of different designs, such a small number is not sufficient.
Yes, you could cancel your subscription at any time. Please go to My Orders, at the top of which you will find your current subscription and then click the "Cancel" button next to it.
By purchasing a subscription plan, your credit card on file will be charged periodically depending on the type of subscription you have. If you received a notice regarding payment failure, it's possible your card has expired or something happened to it that the charge on it was denied by your card issuer. Please contact your card issuer to see what might have happened. You could update your card information by going to My Profile page and scroll down to "Credit Card On File" section.
No, we don't keep your credit card information. We use a reputable, PCI compliant third-party to process your credit card. We encrypt your card information and send it over. You could always remove your credit card information completely by going to the Profile page and scroll down to the bottom. Then click the "Remove Card" button.
Yes, our dedicated and talented team of statisticians are continuously improving U-Design and adding more software functions to it. Please go to the Upcoming Products section for details.
Please let us know the problem you encountered by either emailing us: email@example.com or filling out the contact form at the bottom of each page.
- Ji, Y., Liu, P., Li, Y., & Nebiyou Bekele, B. (2010). A modified toxicity probability interval method for dose-finding trials. Clinical Trials, 7(6), 653-663.
- Ji, Y., & Wang, S. J. (2013). Modified toxicity probability interval design: a safer and more reliable method than the 3+ 3 design for practical phase I trials. Journal of Clinical Oncology, 31(14), 1785.
- Yang, S., Wang, S. J., & Ji, Y. (2015). An integrated dose-finding tool for phase I trials in oncology. Contemporary clinical trials, 45, 426-434.
- Guo, W., Wang, S. J., Yang, S., Lynn, H., & Ji, Y. (2017). A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2. Contemporary clinical trials, 58, 23-33.
- O′Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 33-48.
- Storer, B. E. (1989). Design and analysis of phase I clinical trials. Biometrics, 925-937.
- Neuenschwander, B., Branson, M., & Gsponer, T. (2008). Critical aspects of the Bayesian approach to phase I cancer trials. Statistics in medicine, 27(13), 2420-2439.
- Ivanova, A., Flournoy, N., & Chung, Y. (2007). Cumulative cohort design for dose-finding. Journal of Statistical Planning and Inference, 137(7), 2316-2327.
- Guo W., Ji Y., and Li, D. R-TPI: Rolling Toxicity Probability Interval Design to Shorten the Duration and Maintain Safety of Phase I Trials. (Submitted) Journal of Biopharmaceutical Statistics.
- Skolnik, J. M., Barrett, J. S., Jayaraman, B., Patel, D., & Adamson, P. C. (2008). Shortening the timeline of pediatric phase I trials: the rolling six design. Journal of Clinical Oncology, 26(2), 190-195.
- Neuenschwander, B., Matano, A., Tang, Z., Roychoudhury, S., Wandel, S., & Bailey, S. (2015). A Bayesian industry approach to phase I combination trials in oncology. Statistical Methods in Drug Combination Studies, 2015, 95-135.
- Mander, A. P., & Sweeting, M. J. (2015). A product of independent beta probabilities dose escalation design for dual‐agent phase I trials. Statistics in medicine, 34(8), 1261-1276.