Clinical Trials最新文献

Thousands of healthy volunteers enroll in Phase I clinical trials annually, often motivated by financial gain. Some engage in "over-volunteering"-participating in multiple studies simultaneously or ignoring washout periods. While serious adverse effects are rare, the experimental nature of Phase I trials, which for "first-in-human" studies are designed to trigger adverse events under controlled conditions, makes risks uncertain and unpredictable-a concern that might be compounded by concealed trial participation, which may further increase the likelihood of adverse events. Over-volunteering may also distort trial results through undetected drug interactions. To address this, France, Malaysia, and the UK have implemented national registries to track enrollments and enforce washout periods. Private, for-profit registries, like India's biometric-based system and the US-based Verified Clinical Trials, are used by some clinical research units, mostly private contract research organizations, but their use is not universal and mandatory within countries. Overall, most countries still lack mandatory systems, highlighting the need for broader oversight to protect volunteers and ensure reliable research outcomes. This article discusses the need for and barriers to implementing effective registries and argues that widespread adoption of such registries is critical to protect healthy volunteers from risk of harm while also enhancing trial results' transparency, reliability, and integrity, thereby contributing to developing safer and more effective drugs.

Background: The increasing methodological and regulatory demands of clinical trials have increased the need for clearly defined roles, particularly for Clinical Study Coordinators (CSCs) and Data Managers (DMs). While these professionals play crucial roles in the successful conduction of trials, the lack of established consensus on their profile, role, and responsibilities can lead to overlap and inefficiencies. This review aimed to identify the distinct roles of CSCs and DMs, examine their responsibilities, and explore the roles and responsibilities of CSCs and DMs and, when available, aspects of their collaboration.

Methods: We conducted a systematic review to analyze the distinct roles and responsibilities of CSCs and DMs. A literature search was performed in PubMed, CINAHL, Scopus, and Web of Science for primary studies published between 1 January 2000 and 30 September 2024. Eligible studies focused on defining CSC and DM roles, competencies, and professional responsibilities in clinical trials. Two independent reviewers screened articles, assessed methodological quality, and evaluated the risk of bias. The registration number is PROSPERO CRD42024599819.

Results: Of the 599 records identified, we included 10 studies. CSCs are responsible for the operational side of trials, including patient recruitment, regulatory compliance, and oversight of trial procedures. At the same time, DMs focus on ensuring data accuracy, integrity, and adherence to regulatory standards. The review highlighted the complementary nature of these roles, suggesting that future research could explore how collaboration between the roles may help maintain data quality and meet the demands of modern clinical research. However, the need for standardized role definitions and formal training programs was a significant challenge.

Discussion: This systematic review analyzes the roles of CSCs and DMs, emphasizing their complementary responsibilities in clinical trials. It highlights the need for structured training, standardized workflows, and certification programs to enhance efficiency, ensure regulatory compliance, and improve data quality.

Conclusion: Clarifying the roles of CSCs and DMs and implementing structured training and certification programs are essential to improving trial efficiency. While direct evidence of CSC-DM collaboration was limited, included studies indicate that clear role delineation enhances data quality and regulatory compliance.

BackgroundEvaluation of heterogeneity of treatment effect among participants in large randomized clinical trials may provide insights as to the value of individualizing clinical decisions. The effect modeling approach to predictive heterogeneity of treatment effect analysis offers a promising framework for heterogeneity of treatment effect estimation by simultaneously considering multiple patient characteristics and their interactions with treatment to predict differences in outcomes between randomized treatments. However, its implementation in clinical research remains limited and so we provide a detailed example of its application in a randomized trial that compared raltegravir-based vs darunavir/ritonavir-based therapy as initial antiretroviral treatments for people living with HIV.MethodsThe heterogeneity of treatment effect analysis used a two-step procedure, in which a working proportional hazards model was first selected to construct an index score for ranking the treatment difference for individuals, and then a second calibration step used a non-parametric kernel approach to estimate the true treatment difference for participants with similar index scores. Sensitivity and supplemental analyses were conducted to evaluate the robustness of the results. We further explored the impact of covariates on heterogeneity of treatment effect and the choice between treatments.ResultsThe heterogeneity of treatment effect analysis showed that while there is a clear trend favoring raltegravir-based therapy over darunavir/ritonavir-based therapy for the vast majority of the target population, there were a small subset of patients, characterized by more advanced HIV disease status, for whom the choice between the two treatments might be equivocal.ConclusionsThrough this example, we illustrate how an exploratory heterogeneity of treatment effect analysis might provide further insights into the comparative efficacy of treatments evaluated in a randomized trial. We also highlight some of the issues in implementing and interpreting effect modeling analyses in randomized trials.

BackgroundNutrition and dietary trials are often prone to bias, leading to inaccurate or questionable estimates of intervention efficacy. However, reports on quality management practices of well-controlled dietary trials are scarce. This study aims to introduce the quality management system of the Diet, ExerCIse and CarDiovascular hEalth-Diet Study and report its performance in ensuring study quality.MethodsThe quality management system consisted of a study coordinating center, trial governance, and quality control measures covering study design, conduct, and data analysis and reporting. Metrics for evaluating the performance of the system were collected throughout the whole trial development and conducted from September 2016 to June 2021, covering major activities at the coordinating center, study sites, and central laboratories, with a focus on the protocol amendments, protocol deviations (eligibility, fidelity, confounders management, loss to follow-up and outside-of-window visits, and blindness success), and measurement accuracy.ResultsThree amendments to the study protocol enhanced feasibility. All participants (265) met the eligibility criteria. Among them, only 3% were lost to the primary outcome follow-up measurement. More than 95% of participants completed the study, they consumed more than 96% of the study meals, and more than 94% of participants consumed more than 18 meals per week, with no between-group differences. Online monitoring of nutrient targets for the intervention diet showed that all targets were achieved except for the fiber intake, which was 4.3 g less on average. Only 3% experienced a body weight change greater than 2.0 kg, and 3% had medication changes which were not allowed by the study. James' blinding index at the end of the study was 0.68. The end digits of both systolic and diastolic blood pressure readings were distributed equally. For laboratory measures, 100% of standard samples, 97% of blood-split samples, and 87% of urine-split samples had test results within the acceptable range. Only 1.4% of data items required queries, for which only 30% needed corrections.DiscussionThe Diet, ExerCIse and CarDiovascular hEalth-Diet Study quality management system provides a framework for conducting a high-quality dietary intervention clinical trial.