Clinical Trials最新文献

Introduction: Conducting systematic reviews of clinical trials is time-consuming and resource-intensive. One potential solution is to design databases that are continuously and automatically populated with clinical trial data from harmonised and structured datasets. This scoping review aimed to identify and map publicly available, continuously updated, topic-specific databases of clinical trials.

Methods: We systematically searched PubMed, Embase, the preprint servers medRxiv, arXiv, Open Science Framework, and Google. We characterised each database using seven predefined features (access model, database type, data input sources, retrieval methods, data-extraction methods, trial presentation, and export options) and narratively summarised the results.

Results: We identified 14 continuously updated databases of clinical trials, seven related to COVID-19 (initiated in 2020) and seven non-COVID-19 databases (initiated as early as in 2009). All databases, except one, were publicly funded and accessible without restrictions. Most relied on traditional methods used in static article-based systematic reviews sourcing data from journal publications and trial registries. The COVID-19 databases and some non-COVID-19 databases implemented semi-automated features of data import, which combined automated and manual data curation, whereas the non-COVID-19 databases mainly relied on manual workflows. Most reported information was metadata, such as author names, years of publication, and link to publication or trial registry. Only two databases included trial appraisal information (such as risk of bias assessments). Six databases reported aggregate group-level results, but only one database provided individual participant data on request.

Discussion: Continuously updated topic-specific databases of clinical trials remain limited in number, and existing initiatives mainly employ traditional static systematic review methodologies. A key barrier to developing truly living platforms is the lack of accessible, machine-readable, and standardised clinical trial data.

Background: There is growing recognition of the importance of patient-reported tolerability in complementing traditional clinician-reported safety evaluation of cancer therapies. Recent regulatory guidance listed the evaluation of overall side effect impact as a core patient-reported outcome in oncology clinical trials. A single item ('GP5') that asks about side effect bother is included in the Functional Assessment of Chronic Illness Therapy and has been used to capture overall side effect impact. This paper sought to expand the evidence base for GP5 by examining its association with clinician-reported treatment-emergent adverse events and patient-reported global health.

Methods: We examined six commercial cancer clinical trials that collected GP5. The patient population was drawn from the safety population and the analysis focused on the first on-treatment assessment. Clinician-reported adverse events were classified as symptomatic if such adverse events were considered amenable to patient self-reporting (e.g. nausea). Chi-square tests and Pearson's correlation were used to examine associations. We considered adverse event grade and frequency, both for symptomatic adverse events and any type of adverse events. Global health was measured using the visual analogue scale of the EuroQol-5 Dimensions-3 Levels measure. 'Moderate-severe' bother was characterised as scores of 2-4 on a 0-4 point scale for GP5, and 'severe' bother was characterised as scores of 3-4. Analyses were conducted separately for each trial.

Results: Data from 3,557 patients were included. Across the trials, most (71.7%-94.2%) patients had an adverse event of some kind, but fewer (17.1%-44.4%) had an adverse event of grade 3 or higher. In general, fewer than 50% of patients (20.6%-44.2%) reported moderate-severe bother and 5.8%-17.% reported severe bother. There were consistent, albeit not always statistically significant, associations between GP5 and adverse events, and GP5/global health correlations ranged from -0.17 to -0.41.

Discussion: GP5 is associated with both clinician- and patient-reported symptoms, suggesting its validity and usefulness as part of comprehensive tolerability assessment of cancer trials.

Background: In randomized trials where some standard-treatment arm patients cross to the experimental treatment, it is frequently of interest to estimate the between-arm survival difference as if no patients on the standard-treatment arm had crossed over to the experimental treatment. Rank-preserving structural failure time models, an extension of semiparametric accelerated-failure-time models, are a popular method for accomplishing this because they do not require modeling which patients will crossover.

Methods: In trying to apply the rank-preserving structural failure time model in practice, we noted some unusual behavior of the estimated acceleration parameter (differential treatment effect). Simple examples and limited simulations are provided to examine and understand this behavior.

Results: The simulations show that rank-preserving structural failure time model estimator of the acceleration parameter can take on extreme values, especially when the intent-to-treat analysis favors the standard-treatment arm. Furthermore, the addition of censoring is paradoxically shown to reduce the estimator's variability compared to the uncensored data when the underlying observations are exponentially distributed. Use of a Weibull distribution with short tails for the survival times eliminates this unusual behavior.

Conclusion: The rank-preserving structural failure time model estimators of the acceleration parameter are not based on the joint ranks of the original data, and it is suggested that this makes acceleration-parameter estimator unstable with long-tailed survival distributions.

In May 2023, the US Food and Drug Administration released a guidance document on adjusting for covariates in randomized clinical trials for drugs and biological products. This article provides a summary of motivations for the US Food and Drug Administration guidance document, recommendations in the guidance document, considerations for covariate adjustment in large trials and small trials, and additional topics beyond the scope of the guidance document that may benefit from greater consensus on best practices. A covariate-adjusted prespecified primary analysis can have advantages over an unadjusted analysis and is generally acceptable to the US Food and Drug Administration.

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.