Clinical Trials最新文献

Background: Data monitoring committees play a critical role in ensuring the ethical conduct of clinical trials. Data monitoring committee charters set out the role and processes for data monitoring committees in monitoring clinical trials; however, little is known about the information charters contain.

Methods: We conducted a summative content analysis of a convenience sample of data monitoring committee charters based on the criteria set out for charters by the DAMOCLES Study Group in 2005. Thirteen charters from public and commercially sponsored clinical trials were obtained for review.

Results: Although the data monitoring committee charters we analyzed broadly satisfied the criteria set out by the DAMOCLES Study Group, some issues warrant further attention. These included variability in the availability of unmasked data for review, communication across data monitoring committees for related trials, post-trial DMC responsibilities, and a need for more explicit decision-making processes and conflict resolution procedures. Moreover, few of the data monitoring committee charters we were able to analyze included legal protection for members.

Conclusion: Despite limitations due to the difficulties in obtaining data monitoring committee charters, the convenience sample reviewed suggests variability, including in terms of implementation of some best-practice recommendations. There is a need for further exploration of these issues in a larger sample size. Undertaking such research would be assisted by requiring or incentivizing public access to data monitoring committee charters.

Background/AimsThe existing literature indicates that clinical trial knowledge and participation is multifactorial, yet little is known about the association with digital health technology use and digital health engagement. To address this gap, we examined the multivariate association between clinical trial knowledge and participation with past-year health technology use and digital health engagement with medical providers using data from a federal surveillance system in the United States.MethodsA total of 3865 US adult respondents from the Health Information National Trends Survey 5, Cycle 4 provided data in 2020. The two outcomes were clinical trial knowledge (no knowledge, a little knowledge, a lot of knowledge) and participation (never invited, invited did not participate, invited and participated). There were four binary indicators of health technology use for the following purposes in the past year: searching for health or medical information, communicating with a doctor's office, looking up medical test results, and making medical appointments. There were four binary indicators of digital health engagement in the past year: sharing health information on social media, participating in a health forum or support group, watching health-related videos on YouTube, and awareness of ClinicalTrials.gov.ResultsSurvey-weighted multivariate regression models demonstrated that awareness of ClinicalTrials.gov had the greatest associations with clinical trial knowledge (adjusted risk ratio = 7.60, 95% confidence interval: 4.82-12.00) and participation (adjusted risk ratio = 2.60, 95% confidence interval: 1.23-5.54). Using digital technology to look for health information (adjusted risk ratio = 1.35, 95% confidence interval: 1.06-1.71) and communicate with doctor's offices were significantly associated with higher clinical trial knowledge (adjusted risk ratio = 1.64, 95% confidence interval: 1.25-2.14). Involvement in an online forum or support group was significantly associated with an increased likelihood of being invited but not participating in a clinical trial (adjusted risk ratio = 2.32, 95% confidence interval: 1.22-4.39), while using digital technology to make medical appointments was significantly associated with an increased likelihood of clinical trial participation (adjusted risk ratio = 1.79, 95% confidence interval: 1.07-2.99).ConclusionsFindings from this study can inform the design of large-scale digital health campaigns and quality improvement programs focused on increasing clinical trial participation.

Background/aim: Basket designs have been utilized in recent oncology clinical trials due to an increased interest in precision medicine. One current successful basket trial is the American Society for Clinical Oncology Targeted Agent and Profiling Utilization Registry (TAPUR) study, a pragmatic phase II trial where patients are matched based on their tumor genomic profile to treatments that target specific genomic alterations. Despite its success, recruiting patients with rare genomic alterations remains challenging. This study aims to introduce and evaluate a Bayesian approach for integrating data from ongoing independent basket trials that share similar primary aims to improve interim decisions and final analyses and reduce necessary to evaluate treatments.

Methods: We introduce a Bayesian two-stage phase II single-arm trial specifically for rare cancers utilizing a hierarchical Bayesian random effects model that incorporate data from ongoing trials. We compare this approach with the standard Simon two-stage design through extensive numerical simulations and apply it to real-world scenarios.

Results: Simulation results demonstrate that in rare populations our Bayesian approach has attractive operating characteristics. The simulations show that our approach performs well across a broad set of scenarios with fixed and variable numbers of trials.

Conclusion: Our proposed Bayesian two-stage approach effectively integrates data from multiple ongoing basket trials, enhancing the ability to recruit and analyze patients with rare genomic alterations. This approach improves the timing of interim decision-making and final analysis, making it a valuable tool for trials with slow accrual rates.

Background: Death certificates are a legal requirement for a body to be buried or cremated. Many randomised clinical trials utilise disease-specific causes of death as key outcomes informed by these documents. Determination of cause of death is commonly assigned to an adjudication committee, a time- and resource-intensive process which becomes more difficult in trials of older individuals. We sought to assess whether a simple transcription from a death certificate would be adequate to meet trial requirements and whether the certification process itself can be improved to meet public health and research requirements.

Methods: Random audit of 100 death certificates from ASpirin in Reducing Endpoints in the Elderly, a randomised controlled trial conducted in Australian general practice and US community research centres. Participants were Australians (aged 70+ years) and Americans (65+) living in the community and without life-limiting medical conditions at baseline, recruited between 1 March 2010 and 31 December 2014 for the ASpirin in Reducing Endpoints in the Elderly trial. Outcome of interest was misclassification of death rate.

Results: There were 2757 deaths in the 19,114 study population. In a random sample of 100 of these deaths, misclassification of cause of death was identified through the trial adjudication process in 9% of death certificates.

Conclusion: In trials without the resources of ASpirin in Reducing Endpoints in the Elderly, a coding method can be accepted. Based on our experience, we recommend changes to the structure and process of death certification to better serve both clinical research and public health needs, particularly in older, multimorbid populations.

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.