Clinical Trials最新文献

Background: The results of rehabilitation clinical trials can be negatively affected by adherence to trial protocols. Adherence is multi-factorial, but studies often look at adherence factors separately. Therefore, a systematic review to appraise and synthesise the evidence is warranted to determine the barriers, facilitators and predictors associated with adherence to inpatient rehabilitation trial protocols, whether and how factors interact with one another, and how adherence to rehabilitation protocols can be optimised.

Methods: A mixed-methods systematic review was conducted and reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Databases searched were PubMed (Ovid), EMBASE (Ovid), MEDLINE (Ovid), CINAHL (Ovid), PsycINFO (Ovid), Cochrane Library, Health Technology Assessment Database, Web of Science and grey literature up to April 2024. A cohesive, integrated methodology was employed, leveraging the Consolidated Framework for Implementation Research (CFIR) 2.0, to transform, synthesise and integrate data from various methodologies to address the review objectives.

Results: Twenty-seven studies met the inclusion criteria (randomised controlled trials, qualitative studies related to randomised controlled trials or mixed-methods). Most of the studies were in stroke (n = 17), but other studies included neurological, respiratory, cardiovascular, post-surgical, osteoarthritis and elderly medical. Multiple factors affecting adherence protocols were identified. Adherence was measured in various ways, and setting pre-specified adherence levels was uncommon.

Conclusion: Adherence to inpatient rehabilitation trial protocols is multi-dimensional and multi-factorial. Consensus of adherence measurement and interpretation of adherence levels is needed to make meaningful comparisons between trials. A standardised approach, including adopting a traffic light system, would enable trialists to implement changes mid-trial or stop the trial to avoid research waste. Adopting approaches from behavioural science in the design and conduct of inpatient rehabilitation trials may overcome some of the behavioural barriers identified and optimise adherence for those delivering and receiving the intervention.

Review registration: Prospective Register of Systematic Reviews, registration number CRD42021270121.

Background/aims: Randomised clinical trials assessing treatment effects on health outcomes (e.g. quality of life) can be affected by data truncation by death, where some patients die before their outcome measure is assessed and their data become undefined after death. The ICH E9(R1) addendum on estimands discusses four strategies for handling such terminal intercurrent events: hypothetical, composite, while-alive, and principal stratum. While the addendum emphasises the importance of aligning statistical methods of analysis (i.e. estimators) with estimands, it does not provide specific guidance and consideration on the choice of estimators in practice. We aim to (1) demonstrate how some statistical methods commonly used in trials can be used to estimate different intercurrent event strategies for handling data truncation by death; and (2) describe how missing outcome data (e.g. due to missed assessments or loss to follow-up) can be handled for each estimator.

Method: We use data from SCORAD, a non-inferiority randomised trial comparing single-fraction versus multifraction radiotherapy on ambulatory status at 8 weeks (primary outcome) among patients with spinal canal compression from metastatic cancer. Here, we estimate the effect of radiotherapy on quality of life (secondary outcome), quantified by the difference in mean global health status between the two groups at 8 weeks. We outline the strategies for handling death and describe a selection of commonly used estimators corresponding to each strategy. The handling of missing data is considered and demonstrated as part of the estimation process.

Results: The hypothetical strategy, targeting a treatment effect assuming patients had not died, can be estimated using linear mixed models (a likelihood approach) or multiple imputation (a method commonly used for handling missing data). The composite and while-alive strategies relate to the 'outcome' attribute of the estimand; the former incorporates death into the definition of the primary outcome, the latter only uses outcome data before death. These can be estimated by re-defining the outcome, for example, assigning a value reflecting poor global health status post-death, or using the last global health status observed before death. The principal stratum strategy, targeting a treatment effect among patients who would not die under either treatment, can be estimated by an analysis of survivors under specific assumptions. Missing data can be handled with linear mixed models or multiple imputation.

Conclusions: Regarding death as an intercurrent event in the process of defining the estimand for the trial will help clarify the choice of suitable estimators. When choosing the estimators, it is important to consider the assumptions required by the estimators as well as their plausibility given the setting of the trial.

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.

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.