The Rise of Adaptive Platform Trials in Critical Care.

As durable learning research systems, adaptive platform trials represent a transformative new approach to accelerating clinical evaluation and discovery in critical care. This Perspective provides a brief introduction to the concept of adaptive platform trials, describes several established and emerging platforms in critical care, and surveys some opportunities and challenges for their implementation and impact.

Bayesian adaptive model selection design for optimal biological dose finding in phase I/II clinical trials.

Identification of the optimal dose presents a major challenge in drug development with molecularly targeted agents, immunotherapy, as well as chimeric antigen receptor T-cell treatments. By casting dose finding as a Bayesian model selection problem, we propose an adaptive design by simultaneously incorporating the toxicity and efficacy outcomes to select the optimal biological dose (OBD) in phase I/II clinical trials. Without imposing any parametric assumption or shape constraint on the underlying dose-response curves, we specify curve-free models for both the toxicity and efficacy endpoints to determine the OBD. By integrating the observed data across all dose levels, the proposed design is coherent in dose assignment and thus greatly enhances efficiency and accuracy in pinning down the right dose. Not only does our design possess a completely new yet flexible dose-finding framework, but it also has satisfactory and robust performance as demonstrated by extensive simulation studies. In addition, we show that our design enjoys desirable coherence properties, while most of existing phase I/II designs do not. We further extend the design to accommodate late-onset outcomes which are common in immunotherapy. The proposed design is exemplified with a phase I/II clinical trial in chronic lymphocytic leukemia.

Regulation of Interfacial Chemistry Enabling High-Power Dual-Ion Batteries at Low Temperatures.

Interfacial chemistry plays a crucial role in determining the electrochemical properties of low-temperature rechargeable batteries. Although existing interface engineering has significantly improved the capacity of rechargeable batteries operating at low temperatures, challenges such as sharp voltage drops and poor high-rate discharge capabilities continue to limit their applications in extreme environments. In this study, an energy-level-adaptive design strategy for electrolytes to regulate interfacial chemistry in low-temperature Li||graphite dual-ion batteries (DIBs) is proposed. This strategy enables the construction of robust interphases with superior ion-transfer kinetics. On the graphite cathode, the design endues the cathode interface with solvent/anion-coupled interfacial chemistry, which yields an nitrogen/phosphor/sulfur/fluorin (N/P/S/F)-containing organic-rich interphase to boost anion-transfer kinetics and maintains excellent interfacial stability. On the Li metal anode, the anion-derived interfacial chemistry promotes the formation of an inorganic-dominant LiF-rich interphase, which effectively suppresses Li dendrite growth and improves the Li plating/stripping kinetics at low temperatures. Consequently, the DIBs can operate within a wide temperature range, spanning from -40 to 45 °C. At -40 °C, the DIB exhibits exceptional performance, delivering 97.4% of its room-temperature capacity at 1 C and displaying an extraordinarily high-rate discharge capability with 62.3% capacity retention at 10 C. This study demonstrates a feasible strategy for the development of high-power and low-temperature rechargeable batteries.

Investigator choice of standard therapy versus sequential novel therapy arms in the treatment of relapsed follicular lymphoma (REFRACT): study protocol for a multi-centre, open-label, randomised, phase II platform trial.

BACKGROUND: Relapsed or refractory follicular lymphoma (rrFL) is an incurable disease associated with shorter remissions and survival after each line of standard therapy. Many promising novel, chemotherapy-free therapies are in development, but few are licensed as their role in current treatment pathways is poorly defined. METHODS: The REFRACT trial is an investigator-initiated, UK National Cancer Research Institute, open-label, multi-centre, randomised phase II platform trial aimed at accelerating clinical development of novel therapies by addressing evidence gaps. The first of the three sequential novel therapy arms is epcoritamab plus lenalidomide, to be compared with investigator choice standard therapy (ICT). Patients aged 18 years or older with biopsy proven relapsed or refractory CD20 positive, grade 1-3a follicular lymphoma and assessable disease by PET-CT are eligible. The primary outcome is complete metabolic response by PET-CT at 24 weeks using the Deauville 5-point scale and Lugano 2014 criteria. Secondary outcomes include overall metabolic response, progression-free survival, overall survival, duration of response, and quality of life assessed by EQ-5D-5 L and FACT-Lym. The trial employs an innovative Bayesian design with a target sample size of 284 patients: 95 in the ICT arm and 189 in the novel therapy arms. DISCUSSION: Whilst there are many promising novel drugs in early clinical development for rrFL, understanding the relative efficacy and safety of these agents, and their place in modern treatment pathways, is limited by a lack of randomised trials and dearth of published outcomes for standard regimens to act as historic controls. Therefore, the aim of REFRACT is to provide an efficient platform to evaluate novel agents against standard therapies for rrFL. The adaptive Bayesian power prior methodology design will minimise patient numbers and accelerate trial delivery. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05848765; 08-May-2023. EUDRACT: 2022-000677-75; 10-Feb-2022.

Incorporating participants' welfare into sequential multiple assignment randomized trials.

Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients' time-varying clinical conditions. The sequential, multiple assignment, randomized trial (SMART) is an experimental design that can provide high-quality evidence for constructing optimal DTRs. In a conventional SMART, participants are randomized to available treatments at multiple stages with balanced randomization probabilities. Despite its relative simplicity of implementation and desirable performance in comparing embedded DTRs, the conventional SMART faces inevitable ethical issues, including assigning many participants to the empirically inferior treatment or the treatment they dislike, which might slow down the recruitment procedure and lead to higher attrition rates, ultimately leading to poor internal and external validities of the trial results. In this context, we propose a SMART under the Experiment-as-Market framework (SMART-EXAM), a novel SMART design that holds the potential to improve participants' welfare by incorporating their preferences and predicted treatment effects into the randomization procedure. We describe the steps of conducting a SMART-EXAM and evaluate its performance compared to the conventional SMART. The results indicate that the SMART-EXAM can improve the welfare of the participants enrolled in the trial, while also achieving a desirable ability to construct an optimal DTR when the experimental parameters are suitably specified. We finally illustrate the practical potential of the SMART-EXAM design using data from a SMART for children with attention-deficit/hyperactivity disorder.

Adaptive designs in public health: Vaccine and cluster randomized trials go Bayesian.

Clinical trials in public health-particularly those conducted in low- and middle-income countries-often involve communicable and non-communicable diseases with high disease burden and unmet needs. Trials conducted in these regions often are faced with resource limitations, so improving the efficiencies of these trials is critical. Adaptive trial designs have the potential to save trial time and resources and reduce the number of patients receiving ineffective interventions. In this paper, we provide a detailed account of the implementation of vaccine and cluster randomized trials within the framework of Bayesian adaptive trials, with emphasis on computational efficiency and flexibility with regard to stopping rules and allocation ratios. We offer an educated approach to selecting prior distributions and a data-driven empirical Bayes method for plug-in estimates for nuisance parameters.

Two-stage randomized clinical trials with a right-censored endpoint: Comparison of frequentist and Bayesian adaptive designs.

Adaptive randomized clinical trials are of major interest when dealing with a time-to-event outcome in a prolonged observation window. No consensus exists either to define stopping boundaries or to combine p $$ p $$ values or test statistics in the terminal analysis in the case of a frequentist design and sample size adaptation. In a one-sided setting, we compared three frequentist approaches using stopping boundaries relying on α $$ \alpha $$ -spending functions and a Bayesian monitoring setting with boundaries based on the posterior distribution of the log-hazard ratio. All designs comprised a single interim analysis with an efficacy stopping rule and the possibility of sample size adaptation at this interim step. Three frequentist approaches were defined based on the terminal analysis: combination of stagewise statistics (Wassmer) or of p $$ p $$ values (Desseaux), or on patientwise splitting (Jörgens), and we compared the results with those of the Bayesian monitoring approach (Freedman). These different approaches were evaluated in a simulation study and then illustrated on a real dataset from a randomized clinical trial conducted in elderly patients with chronic lymphocytic leukemia. All approaches controlled for the type I error rate, except for the Bayesian monitoring approach, and yielded satisfactory power. It appears that the frequentist approaches are the best in underpowered trials. The power of all the approaches was affected by the violation of the proportional hazards (PH) assumption. For adaptive designs with a survival endpoint and a one-sided alternative hypothesis, the Wassmer and Jörgens approaches after sample size adaptation should be preferred, unless violation of PH is suspected.

A hybrid approach to sample size re-estimation in cluster randomized trials with continuous outcomes.

This study presents a hybrid (Bayesian-frequentist) approach to sample size re-estimation (SSRE) for cluster randomised trials with continuous outcome data, allowing for uncertainty in the intra-cluster correlation (ICC). In the hybrid framework, pre-trial knowledge about the ICC is captured by placing a Truncated Normal prior on it, which is then updated at an interim analysis using the study data, and used in expected power control. On average, both the hybrid and frequentist approaches mitigate against the implications of misspecifying the ICC at the trial's design stage. In addition, both frameworks lead to SSRE designs with approximate control of the type I error-rate at the desired level. It is clearly demonstrated how the hybrid approach is able to reduce the high variability in the re-estimated sample size observed within the frequentist framework, based on the informativeness of the prior. However, misspecification of a highly informative prior can cause significant power loss. In conclusion, a hybrid approach could offer advantages to cluster randomised trials using SSRE. Specifically, when there is available data or expert opinion to help guide the choice of prior for the ICC, the hybrid approach can reduce the variance of the re-estimated required sample size compared to a frequentist approach. As SSRE is unlikely to be employed when there is substantial amounts of such data available (ie, when a constructed prior is highly informative), the greatest utility of a hybrid approach to SSRE likely lies when there is low-quality evidence available to guide the choice of prior.

Kidney transplant candidates' perspectives on the implementation of a Canadian Willingness to Cross program: A strategy to increase access to kidney transplantation for highly sensitized patients.

BACKGROUND: Kidney transplantation is the optimal treatment for end-stage renal disease. However, highly sensitized patients (HSPs) have reduced access to transplantation, leading to increased morbidity and mortality on the waiting list. The Canadian Willingness to Cross (WTC) program proposes allowing transplantation across preformed donor specific antibodies (DSA) determined to be at a low risk of rejection under the adaptive design framework. This study collected patients' perspectives on the development of this program. METHODS: Forty-one individual interviews were conducted with kidney transplant candidates from three Canadian transplant centers in 2022. The interviews were digitally recorded and transcribed for subsequent analyses. RESULTS: Despite limited familiarity with the adaptive design, participants demonstrated trust in the researchers. They perceived the WTC program as a pathway for HSPs to access transplantation while mitigating transplant-related risks. HSPs saw the WTC program as a source of hope and an opportunity to leave dialysis, despite acknowledging inherent uncertainties. Some non-HSPs expressed concerns about fairness, anticipating increased waiting times and potential compromise in kidney graft longevity due to higher rejection risks. Participants recommended essential strategies for implementing the WTC program, including organizing informational meetings and highlighting the necessity for psychosocial support. CONCLUSION: The WTC program emerges as a promising strategy to enhance HSPs' access to kidney transplantation. While HSPs perceived this program as a source of hope, non-HSPs voiced concerns about distributive justice issues. These results will help develop a WTC program that is ethically sound for transplant candidates.

Bayesian sequential monitoring strategies for trials of digestive cancer therapeutics.

BACKGROUND: New therapeutics in oncology have presented challenges to existing paradigms and trial designs in all phases of drug development. As a motivating example, we considered an ongoing phase II trial planned to evaluate the combination of a MET inhibitor and an anti-PD-L1 immunotherapy to treat advanced oesogastric carcinoma. The objective of the paper was to exemplify the planning of an adaptive phase II trial with novel anti-cancer agents, including prolonged observation windows and joint sequential evaluation of efficacy and toxicity. METHODS: We considered various candidate designs and computed decision rules assuming correlations between efficacy and toxicity. Simulations were conducted to evaluate the operating characteristics of all designs. RESULTS: Design approaches allowing continuous accrual, such as the time-to-event Bayesian Optimal Phase II design (TOP), showed good operating characteristics while ensuring a reduced trial duration. All designs were sensitive to the specification of the correlation between efficacy and toxicity during planning, but TOP can take that correlation into account more easily. CONCLUSIONS: While specifying design working hypotheses requires caution, Bayesian approaches such as the TOP design had desirable operating characteristics and allowed incorporating concomittant information, such as toxicity data from concomitant observations in another relevant patient population (e.g., defined by mutational status).

Adaptive designs were primarily used but inadequately reported in early phase drug trials.

BACKGROUND: Faced with the high cost and limited efficiency of classical randomized controlled trials, researchers are increasingly applying adaptive designs to speed up the development of new drugs. However, the application of adaptive design to drug randomized controlled trials (RCTs) and whether the reporting is adequate are unclear. Thus, this study aimed to summarize the epidemiological characteristics of the relevant trials and assess their reporting quality by the Adaptive designs CONSORT Extension (ACE) checklist. METHODS: We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to January 2020. We included drug RCTs that explicitly claimed to be adaptive trials or used any type of adaptative design. We extracted the epidemiological characteristics of included studies to summarize their adaptive design application. We assessed the reporting quality of the trials by Adaptive designs CONSORT Extension (ACE) checklist. Univariable and multivariable linear regression models were used to the association of four prespecified factors with the quality of reporting. RESULTS: Our survey included 108 adaptive trials. We found that adaptive design has been increasingly applied over the years, and was commonly used in phase II trials (n = 45, 41.7%). The primary reasons for using adaptive design were to speed the trial and facilitate decision-making (n = 24, 22.2%), maximize the benefit of participants (n = 21, 19.4%), and reduce the total sample size (n = 15, 13.9%). Group sequential design (n = 63, 58.3%) was the most frequently applied method, followed by adaptive randomization design (n = 26, 24.1%), and adaptive dose-finding design (n = 24, 22.2%). The proportion of adherence to the ACE checklist of 26 topics ranged from 7.4 to 99.1%, with eight topics being adequately reported (i.e., level of adherence ≥ 80%), and eight others being poorly reported (i.e., level of adherence ≤ 30%). In addition, among the seven items specific for adaptive trials, three were poorly reported: accessibility to statistical analysis plan (n = 8, 7.4%), measures for confidentiality (n = 14, 13.0%), and assessments of similarity between interim stages (n = 25, 23.1%). The mean score of the ACE checklist was 13.9 (standard deviation [SD], 3.5) out of 26. According to our multivariable regression analysis, later published trials (estimated ß = 0.14, p < 0.01) and the multicenter trials (estimated ß = 2.22, p < 0.01) were associated with better reporting. CONCLUSION: Adaptive design has shown an increasing use over the years, and was primarily applied to early phase drug trials. However, the reporting quality of adaptive trials is suboptimal, and substantial efforts are needed to improve the reporting.

Adaptive design trials in eating disorder research: A scoping review.

OBJECTIVE: This scoping review sought to map the breadth of literature on the use of adaptive design trials in eating disorder research. METHOD: A systematic literature search was conducted in Medline, Scopus, PsycInfo, Emcare, Econlit, CINAHL and ProQuest Dissertations and Theses. Articles were included if they reported on an intervention targeting any type of eating disorder (including anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified feeding or eating disorders), and employed the use of an adaptive design trial to evaluate the intervention. Two independent reviewers screened citations for inclusion, and data abstraction was performed by one reviewer and verified by a second. RESULTS: We identified five adaptive design trials targeting anorexia nervosa, bulimia nervosa and binge-eating disorder conducted in the USA and Australia. All employed adaptive treatment arm switching based on early response to treatment and identified a priori stopping rules. None of the studies included value of information analysis to guide adaptive design decisions and none included lived experience perspectives. DISCUSSION: The limited use of adaptive designs in eating disorder trials represents a missed opportunity to improve enrolment targets, attrition rates, treatment outcomes and trial efficiency. We outline the range of adaptive methodologies, how they could be applied to eating disorder research, and the specific operational and statistical considerations relevant to adaptive design trials. PUBLIC SIGNIFICANCE: Adaptive design trials are increasingly employed as flexible, efficient alternatives to fixed trial designs, but they are not often used in eating disorder research. This first scoping review identified five adaptive design trials targeting anorexia nervosa, bulimia nervosa and binge-eating disorder that employed treatment arm switching adaptive methodology. We make recommendations on the use of adaptive design trials for future eating disorder trials.

OBJETIVO: Esta revisión exploratoria buscó mapear el alcance de la literatura sobre el uso de ensayos de diseño adaptativo en la investigación de trastornos de conducta alimentaria. MÉTODO: Se realizó una búsqueda sistemática de literatura en Medline, Scopus, PsycInfo, Econlit y CINAHL. Se incluyeron artículos que informaban sobre una intervención dirigida a cualquier tipo de trastorno de conducta alimentaria (incluyendo anorexia nerviosa, bulimia nerviosa, trastorno por atracón y otros trastornos de la conducta alimentaria o de la ingestión de alimentos especificados) y empleaban el uso de un ensayo de diseño adaptativo para evaluar la intervención. Dos revisores independientes examinaron las citas para su inclusión, y la abstracción de datos fue realizada por un revisor y verificada por otro. RESULTADOS: Identificamos cinco ensayos de diseño adaptativo dirigidos a la anorexia nerviosa, bulimia nerviosa y trastorno por atracón realizados en Estados Unidos y Australia. Todos emplearon el cambio adaptativo de brazo de tratamiento basado en la respuesta temprana al tratamiento e identificaron reglas de detención a priori. Ninguno de los estudios incluyó análisis del Valor de la Información para guiar las decisiones de diseño adaptativo y ninguno incluyó perspectivas de experiencia vivida. DISCUSIÓN: El uso limitado de diseños adaptativos en ensayos de trastornos de conducta alimentaria representa una oportunidad perdida para mejorar los objetivos de reclutamiento, tasas de deserción, resultados del tratamiento y eficiencia del ensayo. Esbozamos la gama de metodologías adaptativas, cómo podrían aplicarse a la investigación de trastornos de conducta alimentaria, y las consideraciones operativas y estadísticas específicas relevantes para los ensayos de diseño adaptativo. PÚBLICA SIGNIFICANCIA: Los ensayos de diseño adaptativo se emplean cada vez más como alternativas flexibles y eficientes a los diseños de ensayos fijos, pero no se utilizan con frecuencia en la investigación de trastornos de conducta alimentaria. Esta primera revisión exploratoria identificó cinco ensayos de diseño adaptativo dirigidos a la anorexia nerviosa, bulimia nerviosa y trastorno por atracón que emplearon la metodología adaptativa de cambio de brazo de tratamiento. Hacemos recomendaciones sobre el uso de ensayos de diseño adaptativo para futuros ensayos de trastornos de conducta alimentaria.

A Bayesian adaptive design approach for stepped-wedge cluster randomized trials.

BACKGROUND: The Bayesian group sequential design has been applied widely in clinical studies, especially in Phase II and III studies. It allows early termination based on accumulating interim data. However, to date, there lacks development in its application to stepped-wedge cluster randomized trials, which are gaining popularity in pragmatic trials conducted by clinical and health care delivery researchers. METHODS: We propose a Bayesian adaptive design approach for stepped-wedge cluster randomized trials, which makes adaptive decisions based on the predictive probability of declaring the intervention effective at the end of study given interim data. The Bayesian models and the algorithms for posterior inference and trial conduct are presented. RESULTS: We present how to determine design parameters through extensive simulations to achieve desired operational characteristics. We further evaluate how various design factors, such as the number of steps, cluster size, random variability in cluster size, and correlation structures, impact trial properties, including power, type I error, and the probability of early stopping. An application example is presented. CONCLUSION: This study presents the incorporation of Bayesian adaptive strategies into stepped-wedge cluster randomized trials design. The proposed approach provides the flexibility to stop the trial early if substantial evidence of efficacy or futility is observed, improving the flexibility and efficiency of stepped-wedge cluster randomized trials.

Statistical and practical considerations in planning and conduct of dose-optimization trials.

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.

Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies.

Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.

The score-goldilocks design for phase 3 clinical trials.

In this paper, we propose a new Bayesian adaptive design, score-goldilocks design, which has the same algorithmic idea as goldilocks design. The score-goldilocks design leads to a uniform formula for calculating the probability of trial success for different endpoint trials by using the normal approximation. The simulation results show that the score-goldilocks design is not only very similar to the goldilocks design in terms of operating characteristics such as type 1 error, power, average sample size, probability of stop for futility, and probability of early stop for success, but also greatly saves the calculation time and improves the operation efficiency.

Flexible seamless 2-in-1 design with sample size adaptation.

The 2-in-1 design is becoming popular in oncology drug development, with the flexibility in using different endpoints at different decision time. Based on the observed interim data, sponsors can choose to seamlessly advance a small phase 2 trial to a full-scale confirmatory phase 3 trial with a pre-determined maximum sample size or remain in a phase 2 trial. While this approach may increase efficiency in drug development, it is rigid and requires a pre-specified fixed sample size. In this paper, we propose a flexible 2-in-1 design with sample size adaptation, while retaining the advantage of allowing an intermediate endpoint for interim decision-making. The proposed design reflects the needs of the recent FDA's Project FrontRunner initiative, which encourages the use of an earlier surrogate endpoint to potentially support accelerated approval with conversion to standard approval with long-term endpoints from the same randomized study. Additionally, we identify the interim decision cut-off to allow a conventional test procedure at the final analysis. Extensive simulation studies showed that the proposed design requires much a smaller sample size and shorter timeline than the simple 2-in-1 design, while achieving similar power. We present a case study in multiple myeloma to demonstrate the benefits of the proposed design.

Adaptive sequential design for phase II single-arm oncology trials: an expansion of Simon's design.

Single-arm phase II trials are very common in oncology. A fixed sample trial may lack sufficient power if the true efficacy is less than the assumed one. Adaptive designs have been proposed in the literature. We propose a Simon's design based, adaptive sequential design. Simon's design is the most used fixed sample design for single-arm phase II oncology trials. A prominent feature of Simon's design is that it minimizes the sample size when there is no clinically meaningful efficacy. We identify Simon's trial as a special group sequential design. Established methods for sample size re-estimation (SSR) can be readily applied to Simon's design. Simulations show that simply adding SSR to Simon's design may still not provide desirable power. We propose some expansions to Simon's design. The expanded design with SSR can provide even more power.

An adaptive seamless 2-in-1 design with biomarker-driven subgroup enrichment.

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.

DOD-Combo: Bayesian dose finding design in combination trials with meta-analytic-predictive prior.

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.