Clinical Trials最新文献

Background: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life.

Purpose: While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups.

Methods and conclusions: Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

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.

Background: The Opioid Analgesic Reduction Study is a double-blind, prospective, clinical trial investigating analgesic effectiveness in the management of acute post-surgical pain after impacted third molar extraction across five clinical sites. Specifically, Opioid Analgesic Reduction Study examines a commonly prescribed opioid combination (hydrocodone/acetaminophen) against a non-opioid combination (ibuprofen/acetaminophen). The Opioid Analgesic Reduction Study employs a novel, electronic infrastructure, leveraging the functionality of its data management system, Research Electronic Data Capture, to not only serve as its data reservoir but also provide the framework for its quality management program.

Methods: Within the Opioid Analgesic Reduction Study, Research Electronic Data Capture is expanded into a multi-function management tool, serving as the hub for its clinical data management, project management and credentialing, materials management, and quality management. Research Electronic Data Capture effectively captures data, displays/tracks study progress, triggers follow-up, and supports quality management processes.

Results: At 72% study completion, over 12,000 subject data forms have been executed in Research Electronic Data Capture with minimal missing (0.15%) or incomplete or erroneous forms (0.06%). Five hundred, twenty-three queries were initiated to request clarifications and/or address missing data and data discrepancies.

Conclusion: Research Electronic Data Capture is an effective digital health technology that can be maximized to contribute to the success of a clinical trial. The Research Electronic Data Capture infrastructure and enhanced functionality used in Opioid Analgesic Reduction Study provides the framework and the logic that ensures complete, accurate, data while guiding an effective, efficient workflow that can be followed by team members across sites. This enhanced data reliability and comprehensive quality management processes allow for better preparedness and readiness for clinical monitoring and regulatory reporting.

Background: Randomised controlled trials (RCTs) are considered the gold standard design to determine the effectiveness of an intervention, as the only method of decreasing section bias and minimising random error. However, participant recruitment to randomised controlled trials is a major challenge, with many trials failing to recruit the targeted sample size accordingly to the planned protocol. Thus, the aim of this review is to detail the recruitment challenges of preoperative exercise clinical trials.

Methods: A comprehensive search was performed on MEDLINE, Embase, The Cochrane Library, CINAHL, AMED and PsycINFO from inception to July 2021. Randomised controlled trials investigating the effectiveness of preoperative exercise on postoperative complication and/or length of hospital stay in adult cancer patients were included. Main outcomes included recruitment rate, retention rate, number of days needed to screen and recruit one patient and trial recruitment duration. Descriptive statistics were used to summarise outcomes of interest.

Results: A total of 27 trials were identified, including 3656 patients screened (N = 21) and 1414 randomised (median recruitment rate (interquartile range) = 53.6% (25.2%-67.6%), N = 21). The sample size of the included trials ranged from 19 to 270 (median = 48.0; interquartile range = 40.0-85.0) and the duration of trial recruitment ranged from 3 to 50 months (median = 19.0 months; interquartile range = 10.5-34.0). Overall, a median of 3.6 days was needed to screen one patient, whereas 13.7 days were needed to randomise one participant. Over the trials duration, the median dropout rate was 7.9%. Variations in recruitment outcomes were observed across trials of different cancer types but were not statistically significant.

Conclusion: The recruitment of participants to preoperative exercise randomised controlled trials is challenging, but patient retention appears to be less of a problem. Future trials investigating the effectiveness of a preoperative exercise programme following cancer surgery should consider the time taken to recruit patients. Strategies associated with improved recruitment should be investigated in future studies.

Combination therapy is increasingly being explored as a promising approach for improving cancer treatment outcomes. However, identifying effective dose combinations in early oncology drug development is challenging due to limited sample sizes in early-phase clinical trials. This task becomes even more complex when multiple agents are being escalated simultaneously, potentially leading to a loss of monotonic toxicity order with respect to the dose. Traditional single-agent trial designs are insufficient for this multi-dimensional problem, necessitating the development and implementation of dose-finding methods specifically designed for drug combinations. While, in practice, approaches to this problem have focused on preselecting combinations with a known toxicity order and applying single-agent designs, this limits the number of combinations considered and may miss promising dose combinations. In recent years, several novel designs have been proposed for exploring partially ordered drug combination spaces with the goal of identifying a maximum tolerated dose combination, based on safety, or an optimal dose combination, based on toxicity and efficacy. However, their implementation in clinical practice remains limited. In this article, we describe the application of the partial order continual reassessment method and its extensions for combination therapies in early-phase clinical trials. We present completed trials that use safety endpoints to identify maximum tolerated dose combinations and adaptively use both safety and efficacy endpoints to determine optimal treatment strategies. We discuss the effectiveness of the partial-order continual reassessment method and its extensions in identifying optimal treatment strategies and provide our experience with executing these novel adaptive designs in practice. By utilizing innovative dose-finding methods, researchers and clinicians can more effectively navigate the challenges of combination therapy development, ultimately improving patient outcomes in the treatment of cancer.

Background/aims: Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care.

Methods: We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (P_best) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either P_best or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either P_best or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial.

Results: As the number of interventions increases, the SUCRA results in a higher predictive power compared with P_best. Using P_best results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using P_best and the SUCRA, respectively. Thus, we chose P_best as the ranking method for the ALICE trial.

Conclusion: Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than P_best. Future work should consider whether other ranking methods may also be relevant for clinical trial design.