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Why Consider a Complex Innovative Trial Design

What is a Complex Innovative Trial Design (CID)? According to the FDA, CIDs include, but are not limited to, complex adaptive, Bayesian, and other novel clinical trial designs which often require simulations to determine the statistical properties of the trial. A CID might feature a novel use of external data, a formal approach to integrating prior knowledge, or pre-specified (multiple) adaptations to various trial aspects.

An innovative approach to a clinical trial aims to help streamline and advance drug development and inform regulatory decision-making. CIDs may be the most efficient way to attain regulatory approval for indications that are rare and life-threatening, have large unmet needs, are inherently complex to study, and/or are in pediatrics. CIDs are also relevant for medical devices.

In this article, we will take a deep dive into CID use cases, types of CIDs, and challenges related to adopting this type of design.

Types of CIDs

There are many types of CIDs, including:

  • Traditional adaptive designs
  • Biomarker guided adaptive designs
  • Single Arm Trial (SAT) with external control arm
  • Dynamic Bayesian Borrowing to incorporate historical/external/real-world data into trial design and analysis
  • Vaccine trials to counter pandemics
  • Model-based dose-escalation studies incorporating toxicity and early signs of efficacy; for example, mechanism of action
  • Learn-confirm study designs
  • Adaptive Design for studies under a Master Protocol: Basket Trial Designs, Umbrella Trial Designs, Platform Trial Designs
Why Use Complex Innovative Trial Designs?

Mitigate risks due to uncertainty in assumptions for overall design, sample size, endpoints, and population

Late phase trials are designed based on assumptions derived from early phase data and/or literature. However, there is uncertainty around these assumptions which may increase the risk of an under-powered trial. CIDs using one or more adaptations can reduce this uncertainty. Adaptive sample size re-estimation, seamless dose selection and confirmation, and adaptive population enrichment trial designs are examples of such risk-mitigated study designs.

Increase speed and efficiency for diseases with unmet needs

In the case of diseases with large unmet needs, where an established standard of care (SoC) is lacking, it might be unethical to randomize patients using 1:1 allocation to an investigation therapy or SoC. In this case, CIDs can improve statistical robustness and efficiency while using allocations favoring the investigational therapy.

Seamless Phase II and III trial designs are tailored for early (conditional) approval in disease areas with large unmet needs to provide speed and efficiency without compromising on robustness and interpretability of trial results. Some examples of seamless designs are seamless dose selection (Phase II) and confirmation (Phase III) designs, and trial designs under a Master Protocol, for example, Basket, Umbrella, and Platform Trial Designs. In oncology, late phase trials are often designed for early conditional approval based on early or intermediate endpoints.

Overcome statistical inefficiency in a small disease population

In rare disease trials, the small disease population may not allow for a traditional randomized control trial (RCT). In some cases, an RCT with allocation favoring the investigational therapy may be possible while in other cases, a Single Arm trial (SAT) is the only possibility. These designs inherently suffer from statistical inefficiency and interpretability which can be overcome by innovative approaches with respect to trial design, comparison with SoC, endpoints, incorporating data from natural history, or even external studies.

Enrich population and/or response or biomarker-guided adaptations in innovative and/or targeted therapies

These CIDs are commonly used for innovative therapies within the area of precision medicine. Biomarker and/or response adaptive randomization allocation probabilities can be used to efficiently capture treatment benefits for subpopulations when a validated biomarker exists. Biomarkers can also be validated along with confirmation of treatment benefit in a subpopulation in a seamless fashion via the use of CIDs, such as an adaptive population enrichment design.

Challenges Presented by CIDs

CIDs require rigorous upfront planning and communications with regulatory bodies and data monitoring committees. They may also increase the logistical and operational complexity which should be handled by clinical and statistical service providers with experience in implementation of such CIDs.

The additional complexity in CIDs comes from the implementation of complex statistical methodologies. In most cases, these designs are simulation-guided, meaning that extensive simulations are needed to optimize the design parameters (sample size, number, and timing of interim analyses, interim decision-making rules and boundaries, etc.) and to establish the operating characteristics of the design (type-I error and power).

Expert statisticians can help to build the right CID that fits your needs. Our integrated team of clinicians, regulatory consultants, and statisticians have created over 130 CIDs.

Learn more about how we can support your Complex Innovative Trial Design.

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