Clinical Trials最新文献

Introduction: The sequential parallel comparison design has emerged as a valuable tool in clinical trials with high placebo response rates. To further enhance its efficiency and effectiveness, adaptive strategies, such as sample size adjustment and allocation ratio modification can be employed.

Methods: We compared the performance of Jennison and Turnbull's method and the Promising Zone approach for sample size adjustment in a two-phase sequential parallel comparison design study. We also evaluated the impact of allocation ratio adjustments using Neyman and Optimal allocation strategies. Various scenarios were simulated to assess the effects of different design parameters, including weight in the test statistic, initial randomization ratio, and interim analysis timing.

Results: The Promising Zone approach demonstrated superior or comparable power to Jennison and Turnbull's method at equivalent expected sample sizes while maintaining the intuitive property that more promising interim results lead to smaller required follow-up sample sizes. However, the Promising Zone approach may require a larger maximum possible sample size in some cases. The addition of allocation ratio adjustments offered minimal improvements overall, but showed potential benefits when the variance in the treatment group was larger than that in the placebo group. We also applied our findings to a real-world example from the AVP-923 trial in patients with Alzheimer's disease-related agitation, demonstrating the practical implications of adaptive sequential parallel comparison designs in clinical research.

Discussion: Adaptive strategies can significantly enhance the efficiency of sequential parallel comparison designs. The choice between sample size adjustment methods should consider trade-offs between power, expected sample size, and maximum adjusted sample size. Although allocation ratio adjustments showed limited overall impact, they may be beneficial in specific scenarios. Future research should explore the application of these adaptive strategies to binary and survival outcomes in sequential parallel comparison designs.

Metaheuristics are commonly used in computer science and engineering to solve optimization problems, but their potential applications in clinical trial design have remained largely unexplored. This article provides a brief overview of metaheuristics and reviews their limited use in clinical trial settings. We focus on nature-inspired metaheuristics and apply one of its exemplary algorithms, the particle swarm optimization (PSO) algorithm, to find phase I/II designs that jointly consider toxicity and efficacy. As a specific application, we demonstrate the utility of PSO in designing optimal dose-finding studies to estimate the optimal biological dose (OBD) for a continuation-ratio model with four parameters under multiple constraints. Our design improves existing designs by protecting patients from receiving doses higher than the unknown maximum tolerated dose and ensuring that the OBD is estimated with high accuracy. In addition, we show the effectiveness of metaheuristics in addressing more computationally challenging design problems by extending Simon's phase II designs to more than two stages and finding more flexible Bayesian optimal phase II designs with enhanced power.

The adoption of innovative, model-based, and computationally intensive clinical trial designs is challenged by barriers including clinician engagement, regulatory acceptance, dissemination beyond major research institutions, and patient accrual. This session explored strategies to overcome these barriers. Key approaches discussed included the development of user-friendly software and interactive platforms to enhance transparency, open sharing of algorithms, and recognition of software contributions in academic publishing. Building collaborations with stakeholders predisposed to innovation, fostering interdisciplinary communication, and producing complementary methodological and clinical publications were emphasized as essential steps. Practical considerations for trials with small sample sizes included the use of adaptive designs, individualized trials, and alternative optimization strategies when traditional theoretical assumptions are infeasible. A major theme of the discussion was the importance of model assumptions in innovative designs. Questions were raised about the sensitivity of results to these assumptions and the robustness of methods, particularly under limited sample sizes. Addressing this requires extensive simulation studies across varied scenarios to assess operating characteristics. The focus should be on achieving clinically meaningful goals-such as identifying effective dose regions-rather than perfect model specification. Speakers emphasized the need to acknowledge and, when feasible, test assumptions post hoc, integrating such verification as secondary objectives in trial design. An iterative scientific process was encouraged, recognizing that trials not only serve immediate clinical goals but also advance broader scientific understanding. Assumptions provide a principled foundation for methodology, but thoughtful scrutiny of their realism was urged, given the risk of relying on overly strong or untestable premises. The potential of metaheuristic algorithms was highlighted for efficiently identifying optimal designs across different model assumptions, supporting robustness evaluations. Practical implementation should adapt optimal designs to stakeholder needs while preserving acceptable statistical efficiency. In sum, advancing the adoption of innovative designs requires improved communication, infrastructure, and methodological transparency, alongside careful evaluation of model assumptions and robustness.

Background/AimsLow-intervention clinical trials have been established under the European Union Clinical Trials Regulation (EU 536/2014) which aims to simplify the conduct of clinical trials with authorized medicinal products. There is limited experience with conducting low-intervention trials. Therefore, this study aims to report on experiences and perceived (dis)advantages of low-intervention trials.MethodsWe surveyed representatives of all individual clinical trials registered on the public website of the European Union Clinical Trials Information System between 31 January 2022 and 1 December 2023 that evaluated authorized investigational medicinal products and had at least one investigative site in the European Union. These representatives were approached between June 2023 and January 2024.ResultsWe received 70 responses (response rate 21%). Of the respondents, 31 represented a trial registered as low-intervention trial, and 39 represented a trial not registered as a low-intervention trial (hereafter "regular trials"). Simplified clinical trial monitoring and an easier regulatory approval process were perceived as the main advantages of low-intervention trials, with respectively 44% and 34% of the respondents indicating this to be an advantage in low-intervention trials. However, the respondents experienced that stringent and unclear regulatory requirements impeded the conduct of low-intervention trials. Respondents involved with regular trials indicated that 39% of the regular trials met the criteria of a low-intervention trial but were not registered as such, among others due to unfamiliarity with this trial category.ConclusionsWe argue that the simplified procedures for low-intervention trials should be more detailed-for example in regulatory guidance-in the future to further simplify the conduct of clinical trials with authorized investigational medicinal products.

Phase I dose-escalation studies for a single-agent and combination of anti-cancer agents have explored various model-based designs to guide identification of a maximum tolerated dose and recommended phase II dose. This work describes a parallel approach to dose escalation to expedite identification of maximum tolerated doses both for an anti-cancer agent as monotherapy and in combination with another agent. We develop a three-parameter Bayesian logistic regression model that allows for more efficient use of information between monotherapy and combination parts of the study. The model allows the monotherapy and combination data to drive dose escalation of the combination of treatments, reflecting the known dose-toxicity relationship between the monotherapy and combination setting. Through a thorough simulation study in which the proposed model is compared to two comparative approaches, the three-parameter Bayesian logistic regression model is shown to accurately select doses in the target toxicity interval, performing similar to comparative approaches in terms of proportion of target dose/combination selection, while more than halving the proportion of doses selected that were greater than the target toxicity, thereby improving safety concerns.

Unlocking the power of personalised medicine in oncology hinges on the integration of clinical trial data with translational data (i.e. biospecimen-derived molecular information). This combined analysis allows researchers to tailor treatments to a patient's unique biological makeup. However, current practices within UK Clinical Trials Units present challenges. While clinical data are held in standardised formats, translational data are complex, diverse, and requires specialised storage. This disparity in format creates significant hurdles for researchers aiming to curate, integrate and analyse these datasets effectively. This article proposes a novel solution: an open-source SQL database schema designed specifically for the needs of academic trial units. Inspired by Cancer Research UK's commitment to open data sharing and exemplified by the Southampton Clinical Trials Unit's CONFIRM trial (with over 150,000 clinical data points), this schema offers a cost-effective and practical 'middle ground' between raw data and expensive Secure Data Environments/Trusted Research Environments. By acting as a central hub for both clinical and translational data, the schema facilitates seamless data sharing and analysis. Researchers gain a holistic view of trials, enabling exploration of connections between clinical observations and the molecular underpinnings of treatment response. Detailed instructions for setting up the database are provided. The open-source nature and straightforward design ensure ease of implementation and affordability, while robust security measures safeguard sensitive data. We further showcase how researchers can leverage popular statistical software like R to directly query the database. This approach fosters collaboration within the academic discovery community, ultimately accelerating progress towards personalised cancer therapies.

Background: N-of-1 trials compare two or more treatment options for a single participant. These trials have been used to study options for chronic conditions such as arthritis and attention deficit hyperactivity disorder. In addition, they have been suggested as a means to study interventions in rare populations that may not be tractable to include in standard clinical trials, such as treatment options for HIV-positive patients in need of organ transplant. Sequential monitoring of accruing data has been well-studied in traditional clinical trials, but these methods have not yet been implemented in N-of-1 trials. However, the option to validly stop an N-of-1 trial early could deliver faster decisions that could directly improve the patient's health.

Methods: In this work, we propose and evaluate a framework to (1) facilitate sequential monitoring in individual N-of-1 trials with a continuous outcome and (2) combine results across a series of already-completed sequentially monitored N-of-1 trials. By employing the block structure common to N-of-1 trials, we suggest that existing approaches to sequential monitoring may be employed when data from one N-of-1 trial are analyzed with a linear mixed-effects model. To combine results across a series of already-completed sequentially monitored N-of-1 trials, we propose combining the naive estimates from constituent trials in a random-effects model with inverse-variance weighting. We evaluate these proposals via simulation.

Results: We find that type 1 error can be substantially inflated for N-of-1 trials with a small number of planned blocks but can reach the nominal rate for trials with more planned blocks or those with larger numbers of periods per block or by using a $t$-value correction. For those settings with acceptable type 1 error, sequential monitoring results in similar power and on average earlier stopping compared with trials with no sequential monitoring. And, as expected, we find that including a larger number of constituent trials in a series reduces the mean-squared error of the combined point estimator.

Conclusion: Under suitable design considerations, our proposed framework for sequential monitoring can support clinicians in providing important decisions earlier, on average, for patients engaged in N-of-1 trials.

Background: Clinical trials handle a huge amount of data which can be used during the trial to improve the ongoing study conduct. It is suggested by regulators to implement the remote approach to evaluate clinical trials by analysing collected data. Central statistical monitoring helps to achieve that by employing quantitative methods, the results of which are a basis for decision-making on quality issues.

Methods: This article presents a scoping review which is based on a systematic and iterative approach to identify and synthesise literature on central statistical monitoring methodology. In particular, we investigated the decision-making processes (with emphasis on quality issues) of central statistical monitoring methodology and its place in the clinical trial workflow. We reviewed papers published over the last 10 years in two databases (Scopus and Web of Science) with a focus on data mining algorithms of central statistical monitoring and its benefit to the quality of trials.

Results: As a result, 24 scientific papers were selected for this review, and they consider central statistical monitoring at two levels. First, the perspective of the central statistical monitoring process and its location in the study conduct in terms of quality issues. Second, central statistical monitoring methods categorised into practices applied in the industry, and innovative methods in development. The established methods are discussed through the prism of categories of their usage. In turn, the innovations refer to either research on new methods or extensions to existing ones.

Discussion: Our review suggests directions for further research into central statistical monitoring methodology - including increased application of multivariate analysis and using advanced distance metrics - and guidance on how central statistical monitoring operates in response to regulators' requirements.