CURRENT SOFTWARE

It's free to run up to 10 simulated trials at one time for a trial scenario of a design

Phase I

Dose Finding - Single-Agent  

  • Cohort Based Designs  

    An integrated tool supporting the simulation-based comparison among 7 main-stream dose-finding designs...
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  • Rolling Based Designs  

    An innovative tool that allows users to compare how long a trial would take under different designs in real-life enrollment settings...
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  • Decision & MTD  

    A simple-to-use tool that includes Decision Table and MTD Estimation...
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Dose Finding - Dual-Agents  

Cohort Expansion

  • MUCE (MUltiple Cohort Expansion)  

    MUCE is able to save the patient resources without sacrificing the chance of finding the efficacious doses and indications...
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Phase II

Monitoring  

  • Bayesian Efficacy Monitoring with Predictive Probability  

    Bayesian efficacy monitoring with options of early futility and/or efficacy stopping using predictive probability. This design assumes a binary efficacy endpoint.
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  • Bayesian Efficacy Monitoring with Posterior Probability  

    Bayesian efficacy monitoring with options of early futility and/or efficacy stopping using posterior probability.
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  • Bayesian Toxicity Monitoring  

    Bayesian toxicity monitoring for evaluating drug safety.
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Seamless Phase II/III  

  • Continuous Outcome  

    Seamless phase II/III design with continuous outcome using Bayesian go/no-go and dose selection criteria .
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  • Binary Outcome  

    Seamless phase II/III design with binary outcome using Bayesian go/no-go and dose selection criteria .
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Master Protocols

Basket Trials  


Sample Size Calculation  
  • Continuous Outcome  

    Includes equality, equivalence, superiority, and non-inferiority tests for one sample and two independent samples (both z-test abd t-test are provided). Correlation (t-test and z-test). Paired t-test. ANOVA test.
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  • Binary Outcome  

    Includes equality, equivalence, superiority, and non-inferiority tests for one sample and two independent samples. McNemar’s test for wo paired samples. Cohen’s Kappa for agreement.
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  • Time To Event  

    Includes tests under exponential assumption for One Sample, and log-rank test for two independent samples.
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  • Simon's Two-Stage Design  

    Sample size calculation for Phase Ib/II clinical trial using Simon’s 2-stage design
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UPCOMING SOFTWARE

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

CAR-T Phase I Trial Design

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

Subgroup Design

Subgroup enrichment designs and methods for precision clinical trials

Adaptive Dose Insertion

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

PoD-TPI

Time-to-event toxicity probability interval design based on probabilities of decisions

MUCE (MUltiple Cohort Expansion)

We offer consulting service to create or customize MUCE designs and conduct MUCE data analysis.  Contact us to inquire about it

About MUCE

In modern early-phase clinical trials, often times multiple doses of a new drug are tested in multiple indications to identify the promising doses and arms for phase II or phase III trials. Traditionally, each dose or indication is tested separately in a single trial, resulting in multiple protocols and multiple trials.

MUCE is a new Bayesian solution for cohort expansion trials or master protocol trials, in which multiple dose(s) and multiple indication(s) are expanded in parallel. It's built on Bayesian hierarchical models with multiplicity control (BHM-MC) to adaptively borrow information across patient groups to achieve three major goals:

  1. Increase the power (probability of selecting a promising drug for further development) for drug development
  2. Reduce sample size
  3. Control the type I error rate (probability of selecting an unpromising drug for further development)

MUCE Solution

As a comprehensive statistical solution, MUCE can be used to calculate the sample size or power, and to conduct interim and final data analyses for making critical decisions.

For sample size/power calculation, MUCE requires inputs of type I error, power/sample size, reference rate (historical control rate) and target rate for each arm. For data analysis, MUCE requires inputs of reference rate, number of responders and patients enrolled at the time of interim analysis or final analysis.

These can be applied in any clinical trials with 2 or more arms, including:

  • Phase 1b trials with multiple expansion cohorts
  • Phase 2 trials with multiple arms
  • Master protocols including basket, umbrella, and platform trials

MUCE Benefits

Compared to the Simon’s two-stage design and existing other designs for multiple expansion cohort trials (eg. Berry’s BHM [1], etc.), MUCE could control the family-wised type 1 error rate and maintain power with a smaller sample size.

MUCE Consulting Service

We offer MUCE consulting servcie that spans the entire trial process

Some of recent examples of MUCE consulting services

  1. In 2018, MUCE is applied to a Chinese Phase 1a-1b Seamless trial in which up to three doses and four indications will be expanded. This means the trial can expand as many as 12 arms in parallel. The IND application has been approved and trial started with a first patient enrolled. MUCE turns out to be the only design that can manage 12 arms with limited sample size.
  2. In 2019, MUCE is applied to a US Phase 1a/1b trial to help design the multiple expansion cohorts. The trial has three cohorts to expand after the dose escalation stage, to determine if the RP2D can exhibit anti-tumor activities in three different indications. The trial is submitted for IND approval in US.
  3. In 2019, MUCE is applied to a three-arm phase II trial in which each arm investigates the efficacy of the combination of two immune therapies in three lines of cancer treatments. The sample size of trial is cut into half compared to Simon’s two-stage design.
  4. In 2019, MUCE is applied to a two-arm phase 1b/II trial, in which MUCE is used to guide a two-arm expansion cohort design and go/no-go decision for each arm. MUCE is compared to the Bayesian method in Berry et al. (2013) and shows much improved control of type I error rate.

Laiya-One: A 21st Century Phase 1 Trial Design Suite

We offer Laiya-ONE consulting service.  Contact us to inquire about it

A proposal to transform phase 1 oncology trials at company xyz
Laiya Consulting, Inc.
September 28, 2019

Purpose:

to promote the use of a novel phase 1 trial design suite that tailors to modern needs of phase 1 clinical trials in oncology.

Introduction:

Based on a set of innovative statistical designs and approaches, Laiya proposes a suite of solutions to transform phase 1 trials for new drug development (Figure 1). The solutions aim to short the trial duration, reduce sample size and cost, but still maintain safety and sufficient power. Innovatively, we also provide decision tools that help control the type I error of critical decision making to reduce the failure rates of phase 2 and 3 trials.

Figure 1: Laiya One -- A 21st Century Phase 1 Trial Design Suit

How/Where can Laiya-One Help During Phase 1 Development

Laiya-One provides end-to-end services on the entire phase 1 trial and drug develop (Figure 2)

Figure 2 Laiya-One main functions for the phase 1 trial and drug development

Available Designs and Benefits:

The suite includes four novel designs.

  1. The i3+3 design (and extensions). It is a cohort-based dose-finding design suitable for standard DLT-based phase 1a trials. Below are some notable features and benefits.
    • i3+3 has much higher chance of identifying the true MTD
    • i3+3 has proven success at US and Chinese IND applications
    • i3+3 is operationally as simple as the 3+3 design
    • i3+3 variations (mi3+3 and CI3+3) allows biomarker subgroups and combo dose finding
  2. The PoD-TPI design. It is a break-through type of design allowing faster patient enrollment for standard DLT-based phase 1a trials. Below are some notable features and benefits. It is the first type of dose-finding designs that offers the following features and benefits.
    • PoD-TPI quantifies the risk of dose-finding decisions – this is critical if we want to speed up enrollment
    • PoD-TPI accelerates patient enrollment while maintaining safety
    • PoD-TPI assesses existing designs and their risks – important to benchmark future designs
  3. The MUCE design. It is a novel design and decision-making tool allowing more efficient sample size/power/decision making process for phase 1b multiple expansion cohorts studies.
    • MUCE can accommodate multiple doses, schedules, combinations, and indications to be included in a single study.
    • MUCE results in a smaller sample size (as much as half) than the standard designs, such as the Simon’s two-stage design
    • MUCE has a build-in function to control the overall type I error rate – to reduce false selection of indication and dose for phase 2 trials

Examples:

The i3+3 design and its alternatives

  1. Laiya has helped a US company to secure an IND approval including an mi3+3 design is used to help guide all comers and a biomarker-positive subpopulation go through dose finding states simultaneously. This greatly reduced the time and cost since without the mi3+3 design, two sequential dose-finding trials will be needed, one for the all comers and one for the subpopulation.
  2. Laiya has helped multiple Chinese and US companies design seamless phase 1a dose-escalation and multiple expansion cohort trials. The dose-escalation part is based on the i3+3 design and the expansion cohort part is based on the MUCE design. This helps speed up the drug development with a single protocol, and the MUCE design results in 10-40% sample size reduction.
  3. Laiya has helped with investigator initiated trials (IITs) in which the MUCE design is applied to multi-arm phase 2 basket trials. In a three-arm trial targeting different lines of therapy, the MUCE design results in a 50% sample size reduction when compared to the Simon’s two-stage design.

REFERENCES

  1. Guo, W., Wang, S. J., Yang, S., Lynn, H., & Ji, Y. (2017a). A Bayesian interval dose-finding design addressing Ockham's razor: mTPI-2. Contemporary clinical trials, 58, 23-33.
  2. Guo, W., Ji, Y., & Li, D. (2018). R-TPI: Rolling Toxicity Probability Interval Design to Shorten the Duration and Maintain Safety of Phase I Trials. Journal of Biopharmaceutical Statistics.
  3. Lyu, J., Ji, Y., Zhao, N., & Catenacci, D. V. (2018). AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials. Journal of the Royal Statistical Society: Series C (Applied Statistics).
  4. Guo, W., Ji, Y., & Catenacci, D. V. (2017b). A subgroup cluster‐based Bayesian adaptive design for precision medicine. Biometrics, 73(2), 367-377.
  5. Berry, S. M., Broglio, K. R., Groshen, S., & Berry, D. A. (2013). Bayesian hierarchical modeling of patient subpopulations: efficient designs of phase II oncology clinical trials. Clinical Trials, 10(5), 720-734.
  6. Simon R (1989). Optimal two-stage designs for phase II clinical trials, Controlled Clinical Trials 10: 1-10.
  7. Chu, Y., & Yuan, Y. (2018). A Bayesian basket trial design using a calibrated Bayesian hierarchical model. Clinical Trials, 15(2), 149-158.
  8. Neuenschwander, B., Wandel, S., Roychoudhury, S., & Bailey, S. (2016). Robust exchangeability designs for early phase clinical trials with multiple strata. Pharmaceutical statistics, 15(2), 123-134.