Speed Matters, Quality Matters

Shaping The Future Of Drug Development

A pioneering Software-as-a-Service biostatistics design web platform that delivers innovative optimized statistical designs to improve the efficiency, speed, and safety of drug development


A team of world-class Bayesian statisticians, including PhDs from University of Chicago, University of Texas, Fudan University and Rice University, specializing in Bayesian adaptive designs and implementation for drug and device clinical trials



Why U-Design
  • Features best-in-class innovative Bayesian adaptive designs based on years of research and continuous refinement that will improve safety, efficiency, and speed of clinical trials, and increase the probability of successful drug development programs
  • Offers a simple user interface that allows both clinicians and statisticians to easily create designs, run simulations and compare results with a few clicks of buttons
  • Automatically generates submission-ready protocol section related to designs and statistical plan


It's free to run up to 10 simulations for a scenario of any design!


Cohort-Based Designs

An integrated tool supporting the simulation-based comparison among six main-stream dose-finding designs. This module provides both the modern Bayesian model-based designs, including 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).



Targeting the key paint 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., submitted) that aim to accelerate phase 1 trials, and cohort-based designs (3+3 and mTPI-2 (Guo et al., 2017)). This module for rolling-based designs is the only tool on the market that incorporates the comparison of trial duration among different designs.


Decision & MTD

A simple-to-use tool that allows users 1) to generate and examine the transparent dose-finding decision tables for four designs, mTPI, mTPI-2, mCCD and 3+3, 2) to make the mTPI-2 decision of MTD selection based on accumulated data for a real trial.


Dual-Agent Dose Finding Based on Toxicity

Combination drugs are important in oncology. By attacking the cancer at multiple points on cell signaling pathways, or by attacking multiple pathways, combination drugs can overcome resistance and gain greater potency. This module provides simulation-based comparison of two Bayesian model-based dose-finding designs, dual-agent BLRM (Neuenschwander et al., 2015) and PIPE (Mander and Sweeting, 2015). These two designs only model the toxicity outcomes and aim to identify the Maximum Tolerant Dose.


We are working hard to bring these to U-Design. Stay tuned!

A simple and effective dose-finding design for CAR-T phase I trials

Subgroup enrichment designs and methods for precision clinical trials

Adaptive dose insertion allowing new doses to be inserted during the trial to increase the probability of success (for finding the best dose)

A sample size calculator for dose-finding trials


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?


It's free to run up to 10 simulations for a scenario of any design!

If you are an academic user, you could request a discount by sending an email to admin@laiyaconsulting.com from your organization email address with a subject line 'Academic Discount Request'. We will email you a discount code that you could enter at the checkout to enjoy a 30% discount off the regular subscription price, if you are eligible.

Subscription Plans


$200 month

Full access to all designs

  • Single Agent
  • Dual Agents
  • Subgroup Design
  • Decision Tables
Get It


$1,140 half year

Full access to all designs

  • Single Agent
  • Dual Agents
  • Subgroup Design
  • Decision Tables
Get It


$2,160 year

Full access to all designs

  • Single Agent
  • Dual Agents
  • Subgroup Design
  • Decision Tables
(313) 608-1027
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And many more ...



  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.

The BEST for Early-Phase Drug Development

The Bayesian Early-Phase Seamless Transformation is a novel Bayesian early-phase seamless transformation (BEST) platform design that combine a phase 1a dose-escalation stage, phase 1b cohort expansion stage with multiple indication cohorts, or even a phase 2 stage. It utilizes a Bayesian hierarchical model that can improve the overall study power in terms of selecting the promising dose and indication for later-stage drug development

The BEST Platform Is A Flexible Modular Design

Solution Suites

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Service Highlights
  • Single-Agent Dose-Finding Design
  • Rapid Patient Enrollment
  • Adaptive Dose Insertion
  • Drug Combination Dose-Finding Design
  • Immune Oncology Trial Design
  • Phase 1-2 Seamless Design
  • Subgroup Analysis and Enrichment Design
  • Real World Data/Evidence Modeling and Design
  • Dose Cycle Dose-Finding Design


  • Protocol Development
  • Statistical Analysis Plans
  • Statistical Reports
  • Randomization
  • Data Monitoring Committee Support
Biostatistic Programming
  • Independent Statistical Center Support
  • Regulatory Consulting & Representation
  • PK/PD Analysis
  • Data Transfer, QC & Lock
  • Programming TLFs



LAIYA delivered an innovative dosefinding trial design to a client, a top US CAR-T cell drug company, to incorporate both efficacy and toxicity data in dose finding. The method is published in a top medical journal. See Li et al., (2016, Clinical Cancer Research)


A top pharmaceutical company in China sought an efficient trial design for PD-L1 combinational trial conducted in US. LAIYA assisted to communicate with the clinical team in US and proposed a phase I/II seamless basket design with Go/No- Go decisions under a master protocol for the comprehensive trial.