Adaptive Clinical Trial Designs Research Articles

A general strategy for reducing the variability in response-adaptive designs

. Adaptive designs for clinical trials address the ethical requirement of allocating fewer patients to the inferior treatment as the trial progresses. We present here a generic strategy partly motivated by play-the-winner heuristic for reducing the variability of such designs. It is shown through simulations from a real clinical setting that employing this strategy improves the safety and the performance of some standard response adaptive designs. A closely related alternate strategy amenable to a theoretical analysis is also proposed with the same objective of lower variability.

Adaptive designs in clinical trials: a systematic review-part I

BackgroundAdaptive 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.MethodsPublished studies from 2010 to April 2020 were searched in the following databases: MEDLINE (Ovid), Embase (Ovid), and International Pharmaceutical s (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.ResultsOut 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.ConclusionThis 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 registrationhttps://doi.org/10.1186/s13063-018-2934-7.

Confirmatory Adaptive Designs for Clinical Trials With Multiple Time-to-Event Outcomes in Multi-state Markov Models.

The analysis of multiple time-to-event outcomes in a randomized controlled clinical trial can be accomplished with existing methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to conduct interim analyses and adapt the study design if necessary. Due to the expected dependency of the endpoints, the full available information on the involved endpoints may not be used for this purpose. We suggest a solution to this problem by embedding the endpoints in a multistate model. If this model is Markovian, it is possible to take the disease history of the patients into account and allow for data-dependent design adaptations. To this end, we introduce a flexible test procedure for a variety of applications, but are particularly concerned with the simultaneous consideration of progression-free survival (PFS) and overall survival (OS). This setting is of key interest in oncological trials. We conduct simulation studies to determine the properties for small sample sizes and demonstrate an application based on data from the NB2004-HRstudy.

Adaptive Designs for Clinical Trials in Nephrology.

Adaptive Designs for Clinical Trials in Nephrology.

DHA supplementation for early preterm birth prevention: An application of Bayesian finite mixture models to adaptive clinical trial design optimization

BackgroundEarly preterm birth (ePTB) - born before 34 weeks of gestation - poses a significant public health challenge. Two randomized trials indicated an ePTB reduction among pregnant women receiving high-dose docosahexaenoic acid (DHA) supplementation. One of them is Assessment of DHA on Reducing Early Preterm Birth (ADORE). A survey employed in its secondary analysis identified women with low DHA levels, revealing that they derived greater benefits from high-dose DHA supplementation. This survey's inclusion in future trials can provide critical insights for informing clinical practices. ObjectiveTo optimize a Phase III trial design, ADORE Precision, aiming at assessing DHA supplement (200 vs. 1000 mg/day) on reducing ePTB among pregnant women with a low baseline DHA. MethodsWe propose a Bayesian Hybrid Response Adaptive Randomization (RAR) Design utilizing a finite mixture model to characterize gestational age at birth. Subsequently, a dichotomized ePTB outcome is used to inform trial design using RAR. Simulation studies were conducted to compare a Fixed Design, an Adaptive Design with early stopping, an ADORE-like Adaptive RAR Design, and two new Hybrid Designs with different hyperpriors. DiscussionSimulation reveals several advantages of the RAR designs, such as higher allocation to the more promising dose and a trial duration reduction. The proposed Hybrid RAR Designs addresses the statistical power drop observed in Adaptive RAR. The new design model shows robustness to hyperprior choices. We recommend Hybrid RAR Design 1 for ADORE Precision, anticipating that it will yield precise determinations, which is crucial for advancing our understanding in this field.

Challenges for Conducting Research during Pandemics. A Narrative Review

Background: Research during pandemics presents unique challenges and opportunities that are crucial for advancing scientific knowledge and improving public health responses. This study's topic is significant due to the unprecedented disruptions caused by pandemics like COVID-19, which have impacted all stages of the research process. This study aims to identify and analyze research challenges during pandemics and propose strategies to overcome these obstacles.Methods: A narrative literature review was conducted, focusing on the challenges of conducting research during pandemics. A comprehensive search strategy was implemented, using databases such as PubMed, Google Scholar, and Web of Science, with keywords including "pandemic," "research challenges," "COVID-19 research," "SARS research," "clinical trials during pandemics," "best practices," and "research strategies." The review covered studies published from 2004 to 2023, including previous pandemics like SARS, H1N1, and COVID-19. Relevant articles were identified through database searches and manual reference list reviews.Discussion: The review revealed multidimensional challenges affecting various stages of the research process. Political, economic, administrative, regulatory, logistical, ethical, and social challenges were identified. Logistical difficulties were prevalent, such as limited access to laboratories and supply chain disruptions. Ethical challenges, including informed consent and data privacy, were magnified during health crises. The mental health impacts on researchers and participants were also significant, with increased anxiety and depression affecting engagement and well-being.Conclusion: The study concludes that while pandemics introduce considerable challenges for conducting research, there are also valuable lessons and best practices that can be applied in future crises. Effective strategies include investing in digital infrastructure, developing flexible ethical frameworks, implementing adaptive clinical trial designs, and supporting mental health for researchers and participants. By learning from the COVID-19 pandemic, the research community can better prepare for and navigate future pandemics, ensuring the continuity and effectiveness of scientific investigations.

A Systematic Review of Adaptive Seamless Clinical Trials for Late-Phase Oncology Development.

Although oncology has seen large scientific and clinical advances over the last decade, it also has one of the lowest success rates for novel agents across therapeutic areas. Adaptive clinical trial design has been a popular option for increasing clinical trial efficiency and the chances of trial success. Seamless clinical trial design are studies in which two or more clinical trial phases are combined into a single study with a pre-specified transition between stages. This integration of phases may enhance efficiency. To understand the precedent for the use of seamless designs, this working group was formed to conduct a comprehensive literature search on seamless clinical trials conducted with confirmatory intent in oncology. Trial design features were extracted into a database and analyzed with descriptive statistics. A literature search identified 68 clinical trials meeting inclusion and exclusion criteria. The most common design feature was a gate on treatment efficacy, where the trial would only proceed to the second stage if sufficient efficacy was observed in the first. The next most common feature was a selection of a dose or treatment regimen. Inferentially and operationally seamless designs were approximately equally represented. Key statistical considerations for seamless phase II/III designs include optimizing design choices by evaluating and comparing operating characteristics across design alternatives as well as showing control of overall Type I error rates. Executing the transition between phases should be evaluated for issues related to accrual, drug production, and procedures to maintain trial integrity. While there are unique statistical, regulatory, and operational considerations for seamless designs they are also uniquely suited to many development settings. These include, for example, addressing dose selection under FDA's Project Optimus and addressing the growing use of biomarkers and personalized medicine approaches in cancer treatment.

A systematic approach to adaptive sequential design for clinical trials: using simulations to select a design with desired operating characteristics

ABSTRACT The failure rates of phase 3 trials are high. Incorrect sample size due to uncertainty of effect size could be a critical contributing factor. Adaptive sequential design (ASD), which may include one or more sample size re-estimations (SSR), has been a popular approach for dealing with such uncertainties. The operating characteristics (OCs) of ASD, including the unconditional power and mean sample size, can be substantially affected by many factors, including the planned sample size, the interim analysis schedule and choice of critical boundaries and rules for interim analysis. We propose a systematic, comprehensive strategy which uses iterative simulations to investigate the operating characteristics of adaptive designs and help achieve adequate unconditional power and cost-effective mean sample size if the effect size is in a pre-identified range.

Adaptive two-stage seamless sequential design for clinical trials

ABSTRACT We propose an adaptive sequential testing procedure for the selection and testing of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in a seamlessly combined phase II/III trial. The selection is to be made at the end of phase 2 stage. Unlike in many of the published literature, the selection rule is not required to be to “select the best”, and does not need to be pre-specified, which provides flexibility and allows the trial investigators to use any efficacy and safety information/criteria, or surrogate or intermediate endpoint to make the selection. Sample size and power calculations are provided. The calculations have been confirmed to be accurate by simulations. Interim analysis can be performed after the selection, sample size can be modified if the observed efficacy deviates from the assumed. Inference after the trial, including p-value, median unbiased point estimate and confidence intervals, are provided. By applying a dominance theorem, the procedure can be applied to normal, binary, Poisson, negative binomial distributed endpoints and time-to-event endpoints, and a mixture of these distributions (in trials involving endpoint selection).

Adaptive designs in dermatology clinical trials: Current status and future perspectives.

Current drug development strategies present many challenges that can impede drug approval by regulatory agencies. Alternative study models, such as adaptive trial designs, have recently sparked interest, as they provide a flexible and more efficient approach in conducting clinical trials. Adaptive trial designs offer several potential benefits over traditional randomized controlled trials, which include decrease in costs, reduced clinical development time and limiting exposure of patients to potentially ineffective treatments allowing completion of studies with fewer patients. This article explores the current use of adaptive trial designs in non-oncologic skin diseases and highlights the most common types of adaptive designs used in the field. We also review the operational challenges and statistical considerations associated with such designs and propose clinical development strategies to successfully implement adaptive designs. The article also proposes instances where adaptive trial designs are particularly beneficial, and other situations where they may not be very useful.

Application of seamless phase Ⅱ/Ⅲ design in vaccine clinical trials

The seamless phase Ⅱ/Ⅲ design integrates independent phase Ⅱ and phase Ⅲ clinical trials into a continuous, phased adaptive clinical trial design. Compared with traditional independent phase Ⅱ and phase Ⅲ clinical trials, the seamless design offers significant advantages in accelerating drug or vaccine development and improving clinical trial efficiency. Currently, the application of this design in anti-tumor drug research is becoming increasingly mature, and it is gradually expanding to clinical trials of vaccines, including the 9-valent human papillomavirus vaccine, sabin strain inactivated polio vaccine, and others. This paper aims to clarify the seamless phase Ⅱ/Ⅲ design concept and offer valuable insights into its implementation. It accomplishes this by presenting a clinical trial example featuring a phase Ⅱ/Ⅲ seamless design for a 9-valent human papillomavirus vaccine. The article delves into the specific considerations and potential challenges related to implementing the seamless design, aiming to provide valuable insights for optimizing vaccine clinical trials within our country.

Adaptive hybrid control design for comparative clinical trials with historical control data

Abstract We propose an adaptive hybrid control causal (AHCC) design to leverage historical control data to reduce the sample size demanded by standard randomised controlled trials (RCT). Under the causal inference framework, we define the causal estimand of the average treatment effect and derive the corresponding estimator based on the trial data and historical control data. The AHCC design takes a multistage or group sequential approach. The number of patients randomised to the concurrent control is adaptively adjusted based on the amount of information borrowed from the historical control data. At each stage, based on the interim data, the contribution of the historical control data, quantified by the effective sample size, is updated and used to determine the randomisation ratio between the treatment and control arms for the next stage, with the goal to resemble a standard RCT upon the completion of the trial. Simulation studies show that the AHCC design has desirable operating characteristics. For example, it saves on sample size when substantial information can be borrowed from the historical control, and it maintains power when little information can be borrowed from the historical control.

Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials.

Different combined outcome-data lags (follow-up durations plus data-collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome-data lags (0-105 days) on the performance of various multi-stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response-adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type-1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome-data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome-data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome-data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response-adaptive randomisation. Increased outcome-data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome-data lags when planning adaptive trials.

The characteristics and regulations of adaptive designs from 2008 to 2020: An overview of European Medicines Agency approvals.

The aim of this study was to identify and characterize all European Medicines Agency (EMA) approvals derived from adaptive designs in clinical trials and to provide an update of the current status of these drugs. Relevant files were identified in the EMA database for annual reports for the period between 2008 and 2020 using a list of suitable keywords related to adaptive designs. We recorded trial characteristics from drug approvals and used Fisher exact test to compare the characteristics. A total of 1,054 EMA approvals were identified, and the percentage of EMA approvals planned with adaptive trial designs increased from 1.85% in the period 2008-2012 to 6.19% in between 2017-2020. A total of 41 approvals were identified among 91 original EMA files that contained adaptive designs. The types of adaptive designs used in clinical trials increased after 2017 where the most common type used was the most common (17/41). Most approvals (32/41) comprised pivotal trials, and most assessments had not been accelerated (38/41). Of 32 confirmatory trials planned with adaptive designs, the proportion of those with additional monitoring (AM) increased significantly (p<0.0001) from 0% in the 2008-2012 period to 90.48% in the 2017-2020 period. The percentage of approved antitumor drugs in approved drugs in ongoing clinical trials was 82.35%, compared to 20.83% in trials that were completed (p=0.0001). The proportion of drug approved but where clinical trials were still ongoing in companies requiring post-authorization safety studies (PASSs) or post-authorization efficacy studies (PAESs) or who were granted conditional marketing authorization (CMA) significantly differed from the group of drugs approved where clinical trials were completed (p=0.0230). A trend showing an increased number of EMA approvals related to adaptive designs was observed for the period from 2008 to 2020. Different types of adaptive trial designs could be encouraged for the designation of clinical trials, especially for antitumor drugs; meanwhile, more stringent monitoring regulations seemed to be conducted for ongoing trials of antitumor drugs with adaptive design.

Beta Upper Confidence Bound Policy for the Design of Clinical Trials

The multi-armed bandit problem is a classic example of the exploration-exploitation trade-off well suited to model sequential resource allocation under uncertainty. One of its typical motivating applications is the adaptive designs in clinical trials which modify the trial's course in accordance with the pre-specified objective by utilizing results accumulating in the trial. Since the response to a procedure in clinical trials is not immediate, the multi-armed bandit policies require adaptation to delays to retain their theoretical guarantees. In this work, we show the importance of such adaptation by evaluating policies using the publicly available datasetThe International Stroke Trial of a randomized trial of aspirin and subcutaneous heparin among 19,435 patients with acute ischaemic stroke. In addition to adapted policies, we analyze the Upper Confidence Bound policy with the beta feedback to mitigate delays when the certainty evidence of successful treatment is available in a relatively short-term period after the procedure.

Adaptive Multiple Comparison Sequential Design (AMCSD) for clinical trials

ABSTRACT We propose an adaptive sequential testing procedure for clinical trials that test the efficacy of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in one trial. At any interim analyses, sample size re-estimation can be conducted, and any option can be dropped for lack of efficacy or unsatisfactory safety profile. Inference after the trial, including p-value, conservative point estimate and confidence intervals, are provided.

Assessment of a Multispecies Probiotic Supplement for Relief of Seasonal Allergic Rhinitis: A Randomized Double-Blind Placebo-Controlled Trial

Background: Early phase clinical research provided initial support for the use of a multispecies probiotic supplement to improve quality of life (QoL) in adults with seasonal allergic rhinitis (AR) and reduce the use of AR symptom relieving medication. This study aimed to confirm these early phase findings in a double-blind randomized placebo-controlled trial. Methods: Individuals, aged 18-65 years, with a minimum 2-year history of AR, moderate-to-severe AR symptoms, and a positive radio-allergosorbent test to Bermuda (Couch) Grass were randomized to receive either a multispecies probiotic supplement (total colony-forming units 4 × 109/day) or placebo twice daily for 8 weeks. A mini-rhinoconjunctivitis quality of life questionnaire (mRQLQ) scale was administered at screening, days 0, 28, and 56. The proportion of participants with a >0.7 improvement in mRQLQ was the primary outcome. Participants also completed a daily symptom and medication diary during the supplementation period. Results: There were 165 participants randomized, with 142 included in the primary outcome analysis. The percentage of participants meeting the threshold for a clinically meaningful reduction in the mRQLQ from days 0 to 56 was not significantly different between groups (61% vs. 62%, p = 0.90). However, 76 participants had a clinically meaningful improvement in QoL (decrease in mRQLQ >0.7) prior to the start of supplementation (screening to day 0). Conclusion: Changes in self-reported QoL and other disease severity metrics between screening and the start of supplementation limited the ability to discern an effect of supplementation and highlight the need for adaptive clinical trial designs in allergy research. Clinical Trial Registration: The trial was registered with the Australia and New Zealand Clinical Trials Registry (ACTRN12619001319167).

Use of circulating tumor DNA (ctDNA) for early assessment of treatment response in patients with advanced colorectal cancer (aCRC): A real-world (RW) analysis.

246 Background: Data suggests that changes in ctDNA quantity correlate with response to therapy in patients with advanced solid malignancies. However, there is little consistency on how to calculate and interpret such changes. Here, we apply a clinically-validated molecular response algorithm to a RW cohort of patients with aCRC to further evaluate its ability to assess treatment outcomes. Methods: We queried the Guardant INFORM database, which comprises aggregated commercial payer health claims and de-identified records from patients with comprehensive ctDNA testing via Guardant360 (G360) from September 2018-March 2022. Patients with aCRC who received G360 within 15 weeks prior to new treatment initiation (any line of therapy) and a second test 3-15 weeks after treatment initiation were retrospectively evaluated using the G360 Response algorithm. Cox proportional hazards (CPH) were used for RW time to next treatment (TTNT), time to treatment discontinuation (TTD), and overall survival (rwOS) analyses. Patients categorized as molecular responders had a &gt;50% decrease in mean variant allele fraction (VAF) ratio from pre-treatment to on-treatment. Gender, age, line of therapy (LOT) and comorbidities were included as covariates. Median TTNT, TTD, and rwOS were calculated by Kaplan Meier. Results: Of 185 aCRC patients with eligible MR results, 65% received chemotherapy +/- VEGF, 21% received regimens containing anti-EGFR monoclonal antibodies, and 14% received other therapies. 43% of aCRC patients were classified as molecular responders, 42% were non-responders, and 15% were not evaluable by the algorithm due to no/low ctDNA at one or both timepoints. 16% of patients cleared their ctDNA on treatment (i.e., ctDNA became undetectable). Molecular responders had significantly longer TTNT (median 10.1 months vs 6.1 months; HR p &lt; 0.005), TTD (median 5.2 Months vs 3.9 months, HR p=0.041), and rwOS (not reached vs 17.8 months, HR p=0.017). Conclusions: Patients with aCRC classified as molecular responders, as calculated by this algorithm, had prolonged time on treatment and overall survival. Compared to tumor markers, ctDNA has a short half-life, which can allow for early response assessment, as shown by our study. These findings are relevant for clinical care, with future potential to allow for adaptive clinical trial design. [Table: see text]

A Modification of Location Commensurate Power Prior in Clinical Trials

A statistical approach to incorporate historical data efficiently while maintaining statistical integrity is needed when the use of historical data along with current data is justified. Bayesian statistics enables the utilization of historical data in its priors for inference. In clinical trial analysis, if historical data support current data, incorporating priors can help improve statistical power as well as reduce sample sizes. However, if historical and current data are not compatible, incorporating Bayesian priors can result in estimation bias, hence inflated Type I error. A more recent Bayesian approach, location commensurate power prior (LCPP) helps resolve inflated Type I error issue and gain power in certain scenarios. Our simulation results show that in the presence of large conflicts (a conflict is defined as an average of historical data minus current control data), all Bayesian approaches do not perform well especially in the presence of negative large conflicts, all Bayesian approaches have much lower power compared to frequentist. To address this issue, we propose a modification of location commensurate power prior (mLCPP) that leverages frequentist, traditional Bayesian and LCPP. The proposed approach results in better performance across all conflict profiles. We also apply mLCPP to an adaptive clinical trial design and show that if large conflicts present, mLCPP signals sample size re-estimation in a control arm to avoid under-powered studies and if moderate to no conflicts, it signals partial to full use of historical information, thus improving power while allowing an acceptable inflation of Type I error and offering opportunity for sample size reduction.

Adaptive design clinical trials: current status by disease and trial phase in various perspectives.

An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages.