Clinical Trials最新文献

BackgroundCancer clinical trials are vital for improving treatments. Clinical trial decision-making has been examined from the perspectives of patients and oncologists, but caregiver perspectives on clinical trials and roles in patient enrollment decisions remain understudied.MethodsThis scoping review assessed the state of current research on caregiver roles in cancer trial enrollment decision-making. A review of empirical literature was conducted in January 2024 using PubMed and Embase. Articles were evaluated using a review instrument to determine the aspect of decision-making evaluated, the roles of caregivers in clinical trial enrollment decision-making, and recommendations based on study results.ResultsA total of 23 articles were included in the review. Studies focused on awareness and attitudes about clinical trials (7 articles), hypothetical willingness to participate in a trial (6 articles), and experiences with decision-making (10 articles). Caregiver roles included supporting and deferring to patient autonomy, communicating with clinicians, and taking on burden to facilitate participation in the trial. Researchers recommended including caregivers in clinical trial enrollment discussions and educational outreach, developing interventions to reduce caregiver burden, and future research on caregiver clinical trial decision-making using the framework of relational autonomy.ConclusionEmpirical research on caregiver roles in clinical trial enrollment decision-making is limited. Findings of this review suggest that caregivers experience tension between their perceived role of supporting the patient's autonomy and their own well-being. More research is needed to understand how caregivers navigate these challenges and identify best practices for their inclusion in clinical trial consent.

BackgroundThere is a critical shortage of biostatistics expertise and targeted training programs in clinical trials across Canada.MethodsThe Canadian Network for Statistical Training in Trials (CANSTAT), a pan-Canadian, multi-institutional training platform for biostatisticians in clinical trials, was developed to increase clinical trial biostatistics capacity in Canada.ResultsCANSTAT's training program integrates experiential learning through mentorship and placements at clinical trial sites, online workshops, and capacity-building meetings. The curriculum is designed to equip fellows with essential knowledge of clinical trials, technical skills, and practical experience necessary for their growth into professional trial biostatisticians, with several specific and measurable objectives set to achieve this goal. Educational materials, including CANSTAT competencies, reflective exercises, and individual development plans, are provided to monitor progress and ensure that fellows are meeting their academic and professional goals. Currently, CANSTAT has enrolled 19 fellows.ConclusionCANSTAT has developed a training program that equips fellows with essential skills in clinical trial design, conduct and analysis, and interprofessional communication, preparing them to effectively lead biostatistical efforts in clinical trials. By training a new generation of clinical trial biostatisticians, CANSTAT is strengthening Canada's clinical trial enterprise and improving health outcomes.

BackgroundThe motivations to share anonymised datasets from clinical trials within the scientific community are increasing. Many anonymised datasets are now publicly available for secondary research. However, it is uncertain whether they pose a privacy risk to the involved participants.MethodsWe located a broad sample of publicly available, de-identified/anonymised randomised clinical trial datasets from human participants and contacted their owners to request access, following their local procedures. We classified personal data within these datasets, including unique direct identifiers such as date of birth and other personal data that, on their own, does not identify an individual but may do so when combined with each other, such as sex, age and race (indirect identifiers). Combining indirect identifiers forms strata, and adding more identifiers increases granularity by dividing the data into a larger number of smaller strata. The re-identification risk score equations evaluate membership in these strata in three ways: first, by measuring the proportions of participants in strata above predetermined risk threshold levels (Ra); second, by locating the smallest stratum (Rb); third, by estimating the average membership across all strata in a dataset (Rc). The risk scores range from 0 (lowest risk) to 1 (highest risk); they do not aim to re-identify individuals in the datasets and are used for routinely collected health records. If a dataset contained a direct identifier, it automatically scored 1 in all metrics. Conversely, if a dataset contained no direct or up to one indirect identifier, it automatically scored 0 in all metrics. Finally, we explored which characteristics of the datasets were associated with the risk scores and compared the risk scores and their usability.ResultsSeventy datasets from 14 data sources were analysed. Thirty-one datasets were shared with minimal restrictions (open access), while 39 were shared with varying levels of restrictions before access was granted (controlled access). Datasets had, on average, four identifiers and mean risk scores ranging from 0.47 to 0.91. The most common pieces of information present in the datasets that, when combined, may indirectly identify a participant were sex (80%) and age (72.9%).ConclusionsThis study confirms that clinical trial datasets are rich in personal details and that using re-identification risk scores as a measure of this richness is feasible. These scores could inform the anonymisation process of clinical trials datasets regarding their level of granularity prior to releasing them for secondary research. We propose a strategy for employing these scores in the decision-making process for releasing clinical trials datasets.

Background/AimsTo respect the rights and wellbeing of research participants, these should receive information at all stages of the trial, and procedures should be put in place to ensure a valid consent that promotes an informed, autonomous and voluntary decision-making. This review focuses on the extent and type of evidence available in relation to best practices in the information provision and consent processes for vaccine trials conducted in Sub-Saharan Africa.MethodsAncillary studies or evaluations assessing the information and/or consent processes used in vaccine trials implemented in Sub-Saharan Africa were eligible. The databases PubMed, CINAHL, Scopus, Web of Science, African Index Medicus Google Scholar and ProQuest dissertations and thesis citation index and Open Access Theses and Dissertations were searched, without time limits. Following a deductive approach, relevant data were extracted using an extraction tool and categorised into themes.ResultsThe review included 46 sources reporting results from 37 studies implemented in 13 Sub-Saharan African countries. The studies covered: community engagement (n = 8); informants (n = 7); messages (n = 7); communication tools (n = 3); community groups (n = 4); consent process (n = 11); comprehension (n = 19) and dissemination of results (n = 4). They mostly represented the views of participants or parents of trial participants; researchers and trial site personnel; and community members and representatives, including those with formal informational roles. The studies showed gaps in information and consent processes leading to a lack of understanding and confusion or suspicion. The involvement of community members in information giving was essential. These were able to communicate in culturally-appropriate ways and also increase trust in the trial.ConclusionsThe studies highlight complexities involved in the information and consent processes for vaccine trials implemented in Sub-Saharan Africa. These processes would benefit from a stronger consideration to the context where research takes place, including culture, language, non-biomedical conceptions and power imbalances. The views from ethics review boards were mostly absent.

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.

Introduction: Depression can be an intrinsic part of neurodegeneration, or a reaction to the onset of motor or non-motor disability. Depression can also adversely influence an individual's perception of their disease, independently of its severity or impact on function. Participant-reported outcome measures are recognised as an important adjunct to objective clinical trial data. Although severe depression is a frequent contraindication for trial participation, mild-to-moderate depressive symptoms could potentially influence the outcome of such questionnaires. We aimed to explore this within two interventional trials for Parkinson's disease (Exenatide PD3; NCT04232969) and multiple system atrophy (Exenatide MSA; NCT04431713).

Methods: Prior to investigational drug exposure, participants completed either the Patient Health Questionnaire or Beck Depression Inventory-II, which allowed us to dichotomise them into two groups (normal or mild-to-moderately elevated burden of depressive symptoms). Participants with Parkinson's self-completed the Movement Disorder Society Sponsored Revision of the Unified Parkinson's disease Rating Scale Parts 1b and 2, Parkinson's Disease Questionnaire-39, Non-motor Symptoms Scale and EQ-5D-5L, while participants with multiple system atrophy self-completed a Quality of Life scale and had assistance with completing the Unified Multiple System Atrophy Rating Scale Part 1. The Movement Disorder Society Sponsored Revision of the Unified Parkinson's Disease Rating Scale Part 3 and Unified Multiple System Atrophy Rating Scale Part 2 provided objective, clinician-rated measures of motor severity.

Results: A mild-to-moderately elevated burden of depressive symptoms was identified in 32.5% (63/194) and 42.0% (21/50) of Parkinson's and multiple system atrophy participants, respectively. Despite the normal and elevated groups being comparable in terms of disease duration and objective motor severity, those with a mild-to-moderately elevated burden of depressive symptoms self-reported worse disease impact. However, measures which involved clinician and/or carer input (Unified Multiple System Atrophy Rating Scale Part 1) were not influenced by co-existing depressive symptoms.

Conclusions: Mild-to-moderate depressive symptoms, below the threshold for diagnosing a depressive disorder, are associated with negative self-reporting, and such findings should be carefully considered when planning the design and analysis of trials in neurodegenerative diseases.

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.

Background: The Sequential Multiple-Assignment Randomised Trial (SMART) design is considered the gold standard for developing adaptive interventions, which tailor treatments to individual patient characteristics and responses. While SMART offers a rigorous framework aligned with real-world clinical decision-making, it is often complex, time-consuming, and costly. As interest in SMART design grows, there is increasing recognition for the need to improve its implementation through more explicit guidance and best practices. Efficiency gains may also be possible by incorporating external data to inform their design, conduct, and analysis. This review aimed to identify all published trials using the SMART design, summarise their design, conduct, and reporting practices and evaluate the use of external data in their implementation.

Methods: We searched PubMed, Medline, PsycINFO, Scopus, and Web of Science databases for all SMART up to June 30, 2024. External data were defined as non-simulated individual patient data collected outside the main SMART to supplement or inform the main trial.

Results: We included 80 SMART, of which 35 (44%) were completed and 45 (56%) were ongoing. Most trials reported two phases of randomisation (93%), with the primary aim focusing on evaluating main effects (81%) of interventions at the first stage of randomisation. There was inadequate reporting of several key aspects, including sample size estimation, statistical analysis software, allocation concealment, data missingness, multiple testing, sensitivity analysis, and the use of SMART in the title. Seventeen (21%) SMART (4-completed trials and 13-trial protocols) referred to the use of external data from electronic health records (n = 12) and registries (n = 5). External data was used for recruitment (n = 11), outcome measures (n = 6), and to provide baseline covariate information (n = 1).

Conclusion: SMART designs are increasingly used to develop adaptive interventions across diverse clinical contexts, yet key methodological features and basic components remain inconsistently reported. This limits transparency, reproducibility, and potential for translation into routine care. Although external data are widely used in standard randomised controlled trials, their use in the SMART is still limited, likely due to methodological and infrastructural challenges and the absence of tailored reporting standards. To improve the efficiency and generalisability of SMART designs, expert-led extensions of CONSORT and SPIRIT guidelines are needed, including specific recommendations for reporting external data use. Future research should explore optimal external data sources for informing SMART components and promote interdisciplinary collaboration and training to support high-quality implementation.

Background: Adverse event monitoring is essential to monitor safety for oncology patients on early-phase clinical trials. Previous research considers that electronic patient-reported adverse events reporting is feasible and complementary to traditional clinician-led recording. An electronic patient-reported adverse event system was developed to explore the feasibility of this in early trials patients.

Methods: A prospective single-arm feasibility study was undertaken at two recruiting hospitals. Participants were adult oncology patients who had recently (<1 month) started receiving a novel anticancer treatment on an academic early-phase trial and had access to the Internet. For a 12-week period, weekly reminders were sent to participants to complete an electronic patient-reported adverse event questionnaire remotely covering symptoms identified as relevant to the recruiting trials. The primary outcome was compliance (proportion of completed questionnaires/expected completions). Secondary outcomes included recruitment rates, attrition, electronic patient-reported outcome versus clinician-recorded adverse events, number of notifications, issues recorded, and patient acceptability.

Results: Twenty-three participants consented (76.7% consent rate), 18 remained on study at 12 weeks (4 were withdrawn due to toxicity and 1 patient choice). Compliance with weekly electronic patient-reported adverse event was high, with a cumulative of 85.1% across the 12 weeks. Comparison with clinician-recorded adverse events showed electronic patient-reported adverse event resulted in wider coverage of adverse events: three times as many symptoms reported on electronic patient-reported adverse event (n = 174 last assessment) than recorded in the medical charts (n = 50 last record). End-of-study feedback indicated most patients reflected positively on their time on the study.

Conclusions: Remote electronic patient-reported adverse event reporting by patients in early-phase trials is feasible and acceptable. The study highlights some logistical challenges that require consideration in future electronic patient-reported outcome work to ensure adverse events are fully captured and recorded.

Trial registration: ClinicalTrials.gov ID: NCT03461939 (first registered: 05/03/2018).

Background: The averted events ratio (AER) is a recently developed estimand for non-inferiority active-control prevention trials with a time-to-event outcome. In contrast to the traditional rate ratio or rate difference, the AER is based on the number of events averted by each of the two treatments rather than the observed events. The AER requires an assumption about either the background event rate (the counterfactual placebo incidence) or the counterfactual effectiveness of the control treatment. We develop and present sample size formulae for trials in which the AER is defined as the primary estimand, and draw comparisons with the conventional 95-95 method based on the rate ratio.

Methods: We express sample size in terms of the expected number of events and required person-years follow-up in the control and experimental arms. Sample size formulae were based on Wald confidence intervals on a logarithmic scale, assuming the active and control treatments to be equally effective. Using the AER, sample size depends on whether the analysis will be based on the counterfactual placebo incidence or the counterfactual treatment effectiveness. For both approaches, and the 95-95 method, sample size is a function of the background event rate, the effectiveness of the control treatment, the preservation-of-effect size (non-inferiority margin), the confidence limit for inferring non-inferiority, and the desired statistical power to demonstrate non-inferiority.

Results: The smallest sample size is obtained using the AER based on the counterfactual placebo incidence. The advantage is greater the higher the value of the control treatment effectiveness. For example, compared with the 95-95 method, it allows between a 2.6-fold and 4.0-fold reduction in sample size for 50% treatment effectiveness (depending of the non-inferiority margin), and between a 7.7-fold and 11.9-fold reduction for 80% treatment effectiveness. The AER based on the control treatment effectiveness is less efficient but still requires smaller sample sizes than the 95-95 method: between a 1.5-fold and 2.9-fold reduction for 50% treatment effectiveness, and between a 2.3-fold and 6.4-fold reduction for 80% treatment effectiveness. Sample size is highly sensitive to the non-inferiority margin: increasing the preservation-of-effect size from 50% to 60% implies a 1.84-fold increase in the sample size; from 60% to 70%, an increase of 2.15-fold; and from 70% to 80%, an increase of 2.55-fold.

Conclusion: As well as having important advantages of interpretation, using the AER as the primary estimand in active-control non-inferiority trials permits smaller and more cost-effective studies. Ideally, the AER should be derived via the counterfactual placebo incidence when this is practicable.