Shaping The Future Of Drug Development

U-Design is a Software-as-a-Service biostatistics platform that delivers innovative optimized statistical designs to improve the efficiency, speed, and safety of drug development

About U-Design

Design Comparison

The only SaaS platform enabling head-to-head comparison of different innovative adaptive designs on demand.

Report Generation

Ready generation of enterprise-grade protocol templates that have statistical sections filled in with simulation results for real-world clinical trials.

Reproducible Results

The simulation results are reproducible and can be shared with regulatory agencies seamlessly.

Free Sign-up

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.

Benefits of U-Design

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

Proprietary Programming

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


Benefits of U-Design

About Simulations

  • 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 Requirements

    FDA requires computer simulations for complex adaptive and Bayesian designs to determine their operating characteristics

  • Insight

    Help these involved to gain clear understanding of a complex design’s operating characteristics and how design choices affect the outcome of the trial

  • Communication

    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

Simple UI

A simple user interface for both clinicians and statisticians to run simulations with a few button clicks

Report Generation

Automatic generation of simulation results report that can be used for trial protocol statistical section

Accompanying Tools

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.

1. What is the maximum sample size (total number of patients to be enrolled) of the trial?
2. What is the toxicity rate of the MTD? For example, if the MTD is defined as the highest dose with no more than 1 patient out of 6 having DLT, the toxicity rate of the MTD is 1/6, or 0.17.
3. How many dose levels will be investigated in the trial?
4. What Dose-finding design(s) do you want to implement?


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

  1. 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.
  2. MTD Estimation: Based on the Pool Adjacent Violators Algorithm (PAVA), the MTD can be estimated when the trial is completed and data collected.



  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. O′Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 33-48.
  6. Storer, B. E. (1989). Design and analysis of phase I clinical trials. Biometrics, 925-937.
  7. 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.
  8. Ivanova, A., Flournoy, N., & Chung, Y. (2007). Cumulative cohort design for dose-finding. Journal of Statistical Planning and Inference, 137(7), 2316-2327.
  9. 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.
  10. 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.
  11. 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.
  12. 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.