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Background Value-adaptive designs for clinical trials are a novel set of emerging methods for delivering greater value for clinical research. There is increasing interest in using them within publicly funded health systems. A value-adaptive design permits ‘in progress’ changes to be made to the trial according to criteria which reflect its overall value to the healthcare system, including the cost-effectiveness of the technologies under investigation, the cost of running the trial and the total health benefit delivered to patients. These trial designs offer the potential to explicitly balance the costs and benefits of adaptive clinical trials with the health economic benefits expected for populations that are affected by any subsequent health technology adoption decisions. They may also improve the expected value of learning from the budget that is spent within a trial. Main body This paper introduces value-adaptive designs for publicly funded clinical trials. It discusses the idea of delivering ‘value for money’ in health technology assessment, what is meant by being ‘value-adaptive’ and the key features that characterise these designs. The methodology behind one kind of value-adaptive design – the value-based sequential model of a two-armed clinical trial proposed by Chick et al. (2017) – is described and illustrated using three retrospective case studies from the United Kingdom. The paper concludes by reviewing a range of perspectives provided by stakeholders, together with our own thoughts, on the practical opportunities and changes required for implementing a value-adaptive approach. Conclusions Value-adaptive clinical trial designs offer the potential to align health research funding allocations with population health economic goals. Many of the systems required to deploy value-adaptive designs within a publicly funded health system already exist and, with increased application, experience, and refinement they have the potential to deliver improved value for money.

Background Adaptive clinical trials increasingly aim to detect heterogeneity of treatment effect (HTE) to guide personalized care. However, most adaptive designs rely on predefined subgroups and are limited in their ability to uncover unknown or complex sources of HTE. Bayesian statistical methods offer a flexible alternative, enabling real-time learning and adaptation within trials. This review evaluates Bayesian methods used to detect hidden HTE in adaptive clinical trials, with attention to their methodological innovations, operating characteristics, and consideration of equity and inclusion in trial design. Methods We conducted a systematic search of MEDLINE, Embase, and other databases to identify original studies that developed Bayesian methods for detecting unknown HTE within adaptive clinical trial designs. Eligible studies were reviewed and synthesized based on design features, statistical methodology, operating characteristics, reproducibility, and whether equity-related factors were explicitly considered. Equity considerations included whether studies incorporated variables related to underrepresented populations—such as age, sex, race/ethnicity, or geography—examined intersectional subgroup effects, or explicitly framed their methods as tools to address health disparities. Results Of 2826 screened records, seven studies met inclusion criteria. Bayesian methods included random partition models, spatial models, logistic regression with dimension reduction, adaptive randomization using machine learning classifiers, and adaptive enrichment or platform designs incorporating model averaging or latent subgroup estimation. In simulation studies, these methods often showed improvements in subgroup detection, efficiency, or power relative to non-Bayesian comparators. None were tested using real-world trial data. Reproducibility was limited overall, with analytic code only available for the three most recent studies. Notably, none explicitly framed their methods as tools to address inequities in treatment outcomes across population subgroups. Conclusions The small number of simulation-based studies illustrates preliminary but promising directions for applying Bayesian methods to detect HTE in adaptive clinical trials. While these approaches demonstrate potential to enhance trial adaptability, scalability, and inclusiveness, current evidence remains limited and largely conceptual. Incorporating an equity lens into future methodological development, alongside greater emphasis on empirical validation and open science practices, will be essential to determine their practical value in advancing equitable clinical research.

Response-adaptive designs allow the randomization probabilities to change during the course of a trial based on cumulated response data so that a greater proportion of patients can be allocated to the better performing treatments. A major concern over the use of response-adaptive designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. In particular, we show that the naïve z-test can have an inflated type I error rate even after applying a Bonferroni correction. Simulation studies have often been used to demonstrate error control but do not provide a guarantee. In this article, we present adaptive testing procedures for normally distributed outcomes that ensure strong familywise error control by iteratively applying the conditional invariance principle. Our approach can be used for fully sequential and block randomized trials and for a large class of adaptive randomization rules found in the literature. We show there is a high price to pay in terms of power to guarantee familywise error control for randomization schemes with extreme allocation probabilities. However, for proposed Bayesian adaptive randomization schemes in the literature, our adaptive tests maintain or increase the power of the trial compared to the z-test. We illustrate our method using a three-armed trial in primary hypercholesterolemia.

To investigate the design, conduct, and analysis of adaptive trials through a systematic survey and provide recommendations for future adaptive trials. We systematically searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases up to January 2020. We included trials that were self-described as adaptive trials or applied adaptive designs. We identified three frequently used adaptive designs and summarized their methodological details in terms of design, conduct, and analysis. Lastly, we provided recommendations for future adaptive trials. We included a total of 128 trials in this study. The primary motivations for using adaptive design were to speed up the trials and facilitate decision-making (n = 29, 31.5%). The three most frequently used methods were group sequential design (GSD) (n = 71, 55.5%), adaptive dose-finding design (ADFD) (n = 35, 27.3%), and adaptive randomization design (ARD) (n = 26, 20.3%). The timing and frequency of interim analysis were detailed in three-fourths of the GSD trials (n = 55, 77.5%) and in half of the ADFD trials (n = 19, 54.3%); however, more than half of the ARD trials (n = 15, 57.7%) did not provide this information. Some trials selected a different outcome than the primary outcome for interim analysis (GSD: n = 7, 12.7%; ADFD: n = 8, 27.6%; ARD: n = 7, 50.0%), but the majority of these trials did not provide explicit reasons for this choice (GSD: n = 7, 100.0%; ADFD: n = 7, 87.5%; ARD: n = 5, 71.4%). More than half (n = 76, 59.4%) of trials did not mention the accessibility of supporting documents, and two-thirds (n = 86, 67.2%) did not state the establishment of independent data monitoring committees (IDMCs). Moreover, unplanned adjustments were observed during the conduct of one-sixth adaptive trials (n = 22, 17.2%). Based on our findings, we provide 14 recommendations for improving adaptive trials in the future. Substantial improvements were needed in methods of adaptive trials, particularly in the areas of interim analysis, the establishment of independent data monitoring committees, and unplanned adjustments. In this study, we offer recommendations from both general and specific aspects for researchers to carefully design, conduct, and analyze adaptive trials.

Background Adaptive designs (ADs) are intended to make clinical trials more flexible, offering efficiency and potentially cost-saving benefits. Despite a large number of statistical methods in the literature on different adaptations to trials, the characteristics, advantages and limitations of such designs remain unfamiliar to large parts of the clinical and research community. This systematic review provides an overview of the use of ADs in published clinical trials (Part I). A follow-up (Part II) will compare the application of AD in trials in adult and pediatric studies, to provide real-world examples and recommendations for the child health community. Methods Published studies from 2010 to April 2020 were searched in the following databases: MEDLINE (Ovid), Embase (Ovid), and International Pharmaceutical Abstracts (Ovid). Clinical trial protocols, reports, and a secondary analyses using AD were included. We excluded trial registrations and interventions other than drugs or vaccines to align with regulatory guidance. Data from the published literature on study characteristics, types of adaptations, statistical analysis, stopping boundaries, logistical challenges, operational considerations and ethical considerations were extracted and summarized herein. Results Out of 23,886 retrieved studies, 317 publications of adaptive trials, 267 (84.2%) trial reports, and 50 (15.8%) study protocols), were included. The most frequent disease was oncology (168/317, 53%). Most trials included only adult participants (265, 83.9%),16 trials (5.4%) were limited to only children and 28 (8.9%) were for both children and adults, 8 trials did not report the ages of the included populations. Some studies reported using more than one adaptation (there were 390 reported adaptations in 317 clinical trial reports). Most trials were early in drug development (phase I, II (276/317, 87%). Dose-finding designs were used in the highest proportion of the included trials (121/317, 38.2 %). Adaptive randomization (53/317, 16.7%), with drop-the-losers (or pick-the-winner) designs specifically reported in 29 trials (9.1%) and seamless phase 2-3 design was reported in 27 trials (8.5%). Continual reassessment methods (60/317, 18.9%) and group sequential design (47/317, 14.8%) were also reported. Approximately two-thirds of trials used frequentist statistical methods (203/309, 64%), while Bayesian methods were reported in 24% (75/309) of included trials. Conclusion This review provides a comprehensive report of methodological features in adaptive clinical trials reported between 2010 and 2020. Adaptation details were not uniformly reported, creating limitations in interpretation and generalizability. Nevertheless, implementation of existing reporting guidelines on ADs and the development of novel educational strategies that address the scientific, operational challenges and ethical considerations can help in the clinical trial community to decide on when and how to implement ADs in clinical trials. Study protocol registration https://doi.org/10.1186/s13063-018-2934-7 .

Background Adaptive designs are increasingly being used in clinical trials within diverse clinical areas. They can offer advantages over traditional non-adaptive approaches, including improved efficiency and patient benefit. The level of improvement observed in practice depends to a large degree on conducting interim analyses (at which adaptations can be made to the trial based on collected data) rapidly and to a high standard. Methods The ROBust INterims for adaptive designs (ROBIN) project aimed to identify best practice for conducting high-quality and rapid interim analyses. This was done through evidence synthesis of published work, qualitative research with trial stakeholders working at public sector clinical trials units, engagement with patients and the public, and a meeting of trial stakeholders to discuss findings and agree recommendations. Results This paper provides recommendations for teams that conduct adaptive trials about how to ensure interim analyses are done rapidly and to a high standard. We break down recommendations by stage of the trial. We also identify a lack of methodology on how best to involve patients in adaptive trials and related decision-making. A limitation of our recommendations is that the research was mostly focused on UK academic settings, although we believe much of the recommendations are relevant in other countries and to industry-sponsored trials. Conclusions When following the recommendations outlined in this paper, the process of planning and executing interim analyses will be smoother; in turn, this will lead to more benefits from using adaptive designs.

To evaluate real-time (day-to-day) adaptation of randomized controlled trials (RCTs) with delayed endpoints – a “forward-looking optimal-experimentation” form of response-adaptive randomization. To identify the implied tradeoffs between lowered mortality, CIs, statistical power, potential arm misidentification, and endpoint rate change during the trial. Using data from RCTs in acute myocardial infarction (30,732 patients in the Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries, GUSTO-1) and coronary heart disease (12,218 patients in the EURopean trial On reduction of cardiac events with Perindopril in stable coronary Artery disease, EUROPA), we resample treatment-arm assignments and expected endpoints to simulate (1) real-time assignment, (2) forward-looking assignments adapted after observing a fixed number of patients (“blocks”), and (3) a variant that balances RCT and real-time assignments. Blinded real-time adaptive randomizations (RTARs) adjust day-to-day arm assignments by optimizing the tradeoff between assigning the (likely) best treatment and learning about endpoint rates for future assignments. Despite delays in endpoints, real-time assignment quickly learns which arm is superior. In the simulations, by the end of the trials, real-time assignment allocated more patients to the superior arm and fewer patients to the inferior arm(s) resulting in less mortality over the course of the trial. Endpoint rates and odds ratios were well within (resampling) CIs of the RCTs, but with tighter CIs on the superior arm and less-tight CIs on the inferior arm(s) and the odds ratios. The variant and patient-block-based adaptation each provides intermediate levels of benefits and costs. When endpoint rates change within a trial, real-time assignment improves estimation of the end-of-trial superior-arm endpoint rates, but exaggerates differences relative to inferior arms. Unlike most response-adaptive randomizations, real-time assignment automatically adjusts to reduce biases when real changes are larger. Real-time assignment improves patient outcomes within the trial and narrows the CI for the superior arm. Benefits are balanced with wider CIs on inferior arms and odds ratios. Forward-looking variants provide intermediate benefits and costs. In no simulations, was an inferior arm identified as statistically superior. Randomized controlled trials (RCT) are the gold standard in clinical trials — typically half of the patients are assigned to a new drug or procedure and the other half to a placebo (or the current best option). Typically, half of the patients might get an inferior drug or treatment. We explore a method, real-time adaptive randomization (RTAR), that uses information observed up to the time of the next assignment to best allocate patients to treatments, balancing known current and unknown future outcomes—treating vs. learning. RTAR is based on a preplanned, but adaptive, assignment rule. Blinding can be maintained, so that neither the trialist nor the patient knows to which treatment the patient was assigned. During the trial, as the RTAR learns the “best” treatment, the RTAR assigns more patients to that best treatment than would a classical RCT. In two large-scale cardiovascular clinical trials, our simulations suggest that the RTAR would have saved lives while identifying the best post-trial treatment at least as well as an RCT. Some statistical measures are improved and others are worse. If endpoint rates in treatments would have changed dramatically during the trial, the RTAR would have adapted better than many other methods. [Display omitted] •Real-time adaptive randomizations (RTARs) save lives and reduce adverse events.•No increase in false positives.•Learn superior treatment with tighter CIs.•Variants fine-tune tradeoffs.•RTARs adapt to some changes in outcome rates.

Adjuvant chemotherapy has reduced the risk of tumor recurrence and improved survival in patients with resected colorectal cancer. Potential utility of circulating tumor DNA (ctDNA) prior to and post surgery has been reported across various solid tumors. We initiated a new type of adaptive platform trials to evaluate the clinical benefits of ctDNA analysis and refine precision adjuvant therapy for resectable colorectal cancer, named CIRCULATE‐Japan including three clinical trials. The GALAXY study is a prospectively conducted large‐scale registry designed to monitor ctDNA for patients with clinical stage II to IV or recurrent colorectal cancer who can undergo complete surgical resection. The VEGA trial is a randomized phase III study designed to test whether postoperative surgery alone is noninferior to the standard therapy with capecitabine plus oxaliplatin for 3 months in patients with high‐risk stage II or low‐risk stage III colon cancer if ctDNA status is negative at week 4 after curative surgery in the GALAXY study. The ALTAIR trial is a double‐blind, phase III study designed to establish the superiority of trifluridine/tipiracil as compared with placebo in patients with resected colorectal cancer who show circulating tumor–positive status in the GALAXY study. Therefore, CIRCULATE‐Japan encompasses both “de‐escalation” and “escalation” trials for ctDNA‐negative and ‐positive patients, respectively, and helps to answer whether measuring ctDNA postoperatively has prognostic and/or predictive value. Our ctDNA‐guided adaptive platform trials will accelerate clinical development toward further precision oncology in the field of adjuvant therapy. Analysis of ctDNA status could be utilized as a predictor of risk stratification for recurrence and to monitor the effectiveness of adjuvant chemotherapy. ctDNA is a promising, noninvasive tumor biomarker that can aid in tumor monitoring throughout disease management. CIRCULATE‐Japan encompasses both “de‐escalation” and “escalation” trials for circulating tumor DNA–negative and –positive patients, respectively, and helps to answer whether measuring circulating tumor DNA postoperatively has prognostic and/or predictive value. Our circulating tumor DNA–guided adaptive platform trials will accelerate clinical development toward further precision oncology in the field of adjuvant therapy.

Background An adaptive design allows modifying the design based on accumulated data while maintaining trial validity and integrity. The final sample size may be unknown when designing an adaptive trial. It is therefore important to consider what sample size is used in the planning of the study and how that is communicated to add transparency to the understanding of the trial design and facilitate robust planning. In this paper, we reviewed trial protocols and grant applications on the sample size reporting for randomised adaptive trials. Method We searched protocols of randomised trials with comparative objectives on ClinicalTrials.gov (01/01/2010 to 31/12/2022). Contemporary eligible grant applications accessed from UK publicly funded researchers were also included. Suitable records of adaptive designs were reviewed, and key information was extracted and descriptively analysed. Results We identified 439 records, and 265 trials were eligible. Of these, 164 (61.9%) and 101 (38.1%) were sponsored by industry and public sectors, respectively, with 169 (63.8%) of all trials using a group sequential design although trial adaptations used were diverse. The maximum and minimum sample sizes were the most reported or directly inferred ( n  = 199, 75.1%). The sample size assuming no adaptation would be triggered was usually set as the estimated target sample size in the protocol. However, of the 152 completed trials, 15 (9.9%) and 33 (21.7%) had their sample size increased or reduced triggered by trial adaptations, respectively. The sample size calculation process was generally well reported in most cases ( n  = 216, 81.5%); however, the justification for the sample size calculation parameters was missing in 116 (43.8%) trials. Less than half gave sufficient information on the study design operating characteristics ( n  = 119, 44.9%). Conclusion Although the reporting of sample sizes varied, the maximum and minimum sample sizes were usually reported. Most of the trials were planned for estimated enrolment assuming no adaptation would be triggered. This is despite the fact a third of reported trials changed their sample size. The sample size calculation was generally well reported, but the justification of sample size calculation parameters and the reporting of the statistical behaviour of the adaptive design could still be improved.

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.