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This paper proposes a two-stage phase I-II clinical trial design to optimize doseschedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decisionmaking is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design’s performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.

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 .

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.

We propose a novel response-adaptive randomization procedure for multi-armed trials with continuous outcomes that are assumed to be normally distributed. Our proposed rule is non-myopic, and oriented toward a patient benefit objective, yet maintains computational feasibility. We derive our response-adaptive algorithm based on the Gittins index for the multi-armed bandit problem, as a modification of the method first introduced in Villar et al. (Biometrics, 71, pp. 969-978). The resulting procedure can be implemented under the assumption of both known or unknown variance. We illustrate the proposed procedure by simulations in the context of phase II cancer trials. Our results show that, in a multi-armed setting, there are efficiency and patient benefit gains of using a response-adaptive allocation procedure with a continuous endpoint instead of a binary one. These gains persist even if an anticipated low rate of missing data due to deaths, dropouts, or complete responses is imputed online through a procedure first introduced in this paper. Additionally, we discuss how there are response-adaptive designs that outperform the traditional equal randomized design both in terms of efficiency and patient benefit measures in the multi-armed trial context.

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.

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.

Background The demand for adaptive trial designs is growing because of their flexibility and the potential for efficiency gains over traditional fixed designs. Adaptive trials allow planned modifications to the design based on accumulating data. Simulation is imperative in designing adaptive trials because analytical power formulae cannot account for data-driven adaptations. Despite their popularity, the uptake of adaptive trials has been slowed by the lack of expertise and availability of training resources. Methods In this tutorial, we demonstrate how to simulate data from a simple adaptive trial with a single interim analysis, summarise the simulations, and use these results to balance the type I error and power to inform the study design and to determine the expected sample size. The simulation code, based on a real trial in hyponatraemia in children, is provided in both R and Stata programming languages. The code is written in modules to improve comprehensibility and enable simple changes to generate a range of adaptive designs. Discussion When using simulation to design an adaptive trial, the simulations must be tailored to the unique design requirements of the trial at hand. This tutorial provides a foundational framework designed to make the simulation process more accessible to both statisticians and clinicians.

Background Many recent Stroke trials fail to show a beneficial effect of the intervention late in the development. Currently a large number of new treatment options are being developed. Multi-arm multi-stage (MAMS) designs offer one potential strategy to avoid lengthy studies of treatments without beneficial effects while at the same time allowing evaluation of several novel treatments. In this paper we provide a review of what MAMS designs are and argue that they are of particular value for Stroke trials. We illustrate this benefit through a case study based on previous published trials of endovascular treatment for acute ischemic stroke. We show in this case study that MAMS trials provide additional power for the same sample size compared to alternative trial designs. This level of additional power depends on the recruitment length of the trial, with most efficiency gained when recruitment is relatively slow. We conclude with a discussion of additional considerations required when starting a MAMS trial. Conclusion MAMS trial designs are potentially very useful for stroke trials due to their improved statistical power compared to the traditional approach.

Escalating costs of cardiovascular trials are limiting medical innovations, prompting the development of more efficient and flexible study designs. The Bayesian paradigm offers a framework conducive to adaptive trial methodologies and is well suited for the study of small populations. Bayesian adaptive trials provide a statistical structure for combining prior information with accumulating data to compute probabilities of unknown quantities of interest. Adaptive design features are useful in modifying randomization schemes, adjusting sample sizes and providing continuous surveillance to guide decisions on dropping study arms or premature trial interruption. Advantages include greater efficiency, minimization of risks, inclusion of knowledge as it is generated, cost savings and more intuitive interpretability. Extensive high-level computations are facilitated by an expanding armamentarium of available tools.

Background Phase-3 clinical trials provide the highest level of evidence on drug safety and effectiveness needed for market approval by implementing large randomized controlled trials (RCTs). However, 30–40% of these trials fail mainly because such studies have inadequate sample sizes, stemming from the inability to obtain accurate initial estimates of average treatment effect parameters. Methods To remove this obstacle from the drug development cycle, we present a new algorithm called Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE) that powers a parallel-group trial, a standard RCT design, by leveraging a state-of-the-art hypothesis testing strategy and a novel trend-adaptive design (TAD). Specifically, TAD-SIE uses synthetic intervention (SI) to estimate individual treatment effects and thereby simulate a cross-over design, which makes it easier for a trial to reach target power within trial constraints (e.g., sample size limits). To estimate sample sizes, TAD-SIE implements a new TAD tailored to SI given that using it violates assumptions under standard TADs. In addition, our TAD overcomes the ineffectiveness of standard TADs by allowing sample sizes to be increased across iterations without any condition while controlling significance level with futility stopping. Our TAD also introduces a hyperparameter that enables trial designers to trade off between accuracy and efficiency (sample size and number of iterations) of the solution. Results On a real-world Phase-3 clinical RCT (i.e., a two-arm parallel-group superiority trial with an equal number of subjects per arm), TAD-SIE obtains operating points ranging between 63% to 84% power and 3% to 6% significance level in contrast to baseline algorithms that get at best 49% power and 6% significance level. Conclusion TAD-SIE is a superior TAD that can be used to reach typical target operating points but only for trials with rapidly measurable primary outcomes due to its sequential nature. The framework is useful to practitioners interested in leveraging the SI algorithm for their study design.