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Recently, interest has grown in the development of dose-finding methods that consider both toxicity and efficacy as endpoints. Along with responses on these, the incorporation of pharmacokinetic (PK) data can be beneficial in terms of patients’ safety and can also increase the efficiency of the design for finding the best dose for the next phase. In this paper, the maximum concentration (${C_{\max }}$Cmax) is used as the PK measure guiding the dose selection. The ethically attractive approach, which is based on the probability of efficacy, is used as a dose optimisation criterion. At each stage of an adaptive trial, that dose is selected for which the criterion is maximised, subject to the constraints imposed on the ${C_{\max }}$Cmax and the probability of toxicity. The inter-patient variability of the PK model parameters is considered, and population $D$D-optimal sampling time points for measuring the concentration of a drug in the blood are calculated. The method is illustrated with a one-compartment PK model with first-order absorption, with the parameters being assumed to be random. The Cox model for bivariate binary responses is employed to model the dose–response outcomes. The results of a simulation study for several plausible dose–response scenarios show a significant gain in the efficiency of the design, as well as a reduction in the proportion of toxic responses. [ABSTRACT FROM AUTHOR]

Adaptive seamless phase 2/3 subgroup enrichment design plays a pivotal role in streamlining efficient drug development within a competitive landscape, while also enhancing patient access to promising treatments. This design approach identifies biomarker subgroups with the highest potential to benefit from investigational regimens. The seamless integration of Phase 2 and Phase 3 ensures a timely confirmation of clinical benefits. One significant challenge in adaptive enrichment decisions is determining the optimal timing and maturity of the primary endpoint. In this paper, we propose an adaptive seamless 2-in-1 biomarker-driven subgroup enrichment design that addresses this challenge by allowing subgroup selection using an early intermediate endpoint that predicts clinical benefits (i.e. a surrogate endpoint). The proposed design initiates with a Phase 2 stage involving all participants and can potentially expand into a Phase 3 study focused on the subgroup demonstrating the most favorable clinical outcomes. We will show that, under certain correlation assumptions, the overall type I error may not be inflated at the end of the study. In scenarios where the assumptions may not hold, we present a general framework to control the multiplicity. The flexibility and efficacy of the proposed design are highlighted through an extensive simulation study and illustrated in a case study in multiple myeloma. [ABSTRACT FROM AUTHOR]

Adaptive designs, such as group sequential designs (and the ones with additional adaptive features) or adaptive platform trials, have been quintessential efficient design strategies in trials of unmet medical needs, especially for generating evidence from global regions. Such designs allow interim decision making and making adjustment to study design when necessary, meanwhile maintaining study integrity and operating characteristics. However, driven by the heightened competitive landscape and the desire to bring effective treatment to patients faster, innovation in the already functional designs is still germane to further propel drug development to a more efficient path. One way to achieve this is by leveraging external real-world data (RWD) in the adaptive designs to support interim or final decision making. In this paper, we propose a novel framework of incorporating external RWD in adaptive design to improve interim and/or final analysis decision making. Within this framework, researchers can prespecify the decision process and choose the timing and amount of borrowing while maintaining objectivity and controlling of type I error. Simulation studies in various scenarios are provided to describe power, type I error, and other performance metrics for interim/final decision making. A case study in non-small cell lung cancer is used for illustration on proposed design framework. [ABSTRACT FROM AUTHOR]

Combination therapy, a treatment modality that involves multiple treatment agents, has become imperative for improving treatment effectiveness and addressing resistance in the field of oncology. However, determining the most effective dose for these combinations, particularly when dealing with intricate drug interactions and diverse toxicity patterns, presents a substantial challenge. This paper introduces a novel Bayesian dose-finding design for combination therapies with information borrowing, named the DOD-Combo design. Leveraging historical single-agent trials and the meta-analytic-predictive (MAP) power prior, our approach utilizes a copula-type model to connect individual drug priors with joint toxicity probabilities in combination treatments. The MAP power prior allows the integration of information from multiple historical trials, constructing informative priors for each agent. Extensive simulations confirm our method’s superior performance compared to combination designs with no information borrowing. By adaptively incorporating historical data, our approach reduces sample sizes and enhances efficiency in selecting the maximum tolerated dose (MTD), effectively addressing the intricate challenges presented by combination trials. [ABSTRACT FROM AUTHOR]

Seamless phase II/III clinical trials have attracted increasing attention recently. They mainly use Bayesian response adaptive randomization (RAR) designs. There has been little research into seamless clinical trials using frequentist RAR designs because of the difficulty in performing valid statistical inference following this procedure. The well-designed frequentist RAR designs can target theoretically optimal allocation proportions, and they have explicit asymptotic results. In this paper, we study the asymptotic properties of frequentist RAR designs with adjusted target allocation proportions, and investigate statistical inference for this procedure. The properties of the proposed design provide an important theoretical foundation for advanced seamless clinical trials. Our numerical studies demonstrate that the design is ethical and efficient. [ABSTRACT FROM AUTHOR]

Adaptive population enrichment designs enable the data-driven selection of one or more pre-specified subpopulations in an interim analysis, and the confirmatory proof of efficacy in the selected subset at the end of the trial. Sample size reassessment and other adaptive design changes can be performed as well. Strong control of the experimentwise Type I error rate is guaranteed by use of the combination testing principle together with the closed testing argument. In this paper the general methodology and designing issues when planning such a design are reviewed. It is shown how to derive overall confidence intervals andp-values. Criteria for assessing the operating characteristics of these designs are given, and the application is illustrated by examples. [ABSTRACT FROM AUTHOR]