A model clinical trials agreement.
Ontology highlight
ABSTRACT:
SUBMITTER: Goodyear MD
PROVIDER: S-EPMC1661743 | biostudies-literature | 2006 Nov
REPOSITORIES: biostudies-literature
Publications
A model clinical trials agreement.
BMJ (Clinical research ed.) 20061117 7578
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Project description:ObjectiveInterobserver agreement in the context of oral epithelial dysplasia (OED) grading has been notoriously unreliable and can impose barriers for developing new molecular markers and diagnostic technologies. This paper aimed to report the details of a 3-stage histopathology review and adjudication process with the goal of achieving a consensus histopathologic diagnosis of each biopsy.Study designTwo adjacent serial histologic sections of oral lesions from 846 patients were independently scored by 2 different pathologists from a pool of 4. In instances where the original 2 pathologists disagreed, a third, independent adjudicating pathologist conducted a review of both sections. If a majority agreement was not achieved, the third stage involved a face-to-face consensus review.ResultsIndividual pathologist pair κ values ranged from 0.251 to 0.706 (fair-good) before the 3-stage review process. During the initial review phase, the 2 pathologists agreed on a diagnosis for 69.9% of the cases. After the adjudication review by a third pathologist, an additional 22.8% of cases were given a consensus diagnosis (agreement of 2 out of 3 pathologists). After the face-to-face review, the remaining 7.3% of cases had a consensus diagnosis.ConclusionsThe use of the defined protocol resulted in a substantial increase (30%) in diagnostic agreement and has the potential to improve the level of agreement for establishing gold standards for studies based on histopathologic diagnosis.
| S-EPMC4564355 | biostudies-literature
Project description:Multiregional clinical trials (MRCTs) provide the benefit of more rapidly introducing drugs to the global market; however, small regional sample sizes can lead to poor estimation quality of region-specific effects when using current statistical methods. With the publication of the International Conference for Harmonisation E17 guideline in 2017, the MRCT design is recognized as a viable strategy that can be accepted by regional regulatory authorities, necessitating new statistical methods that improve the quality of region-specific inference. In this article, we develop a novel methodology for estimating region-specific and global treatment effects for MRCTs using Bayesian model averaging. This approach can be used for trials that compare two treatment groups with respect to a continuous outcome, and it allows for the incorporation of patient characteristics through the inclusion of covariates. We propose an approach that uses posterior model probabilities to quantify evidence in favor of consistency of treatment effects across all regions, and this metric can be used by regulatory authorities for drug approval. We show through simulations that the proposed modeling approach results in lower MSE than a fixed-effects linear regression model and better control of type I error rates than a Bayesian hierarchical model.
| S-EPMC10102881 | biostudies-literature
Project description:BackgroundWith the second largest tuberculosis (TB) burden globally, China is committed to actively engage in international TB clinical trials to contribute to global TB research. However, lack of research capacity among local sites has been identified as a barrier.Main textThe China Tuberculosis Clinical Trials Consortium (CTCTC) was initiated by Beijing Chest Hospital with investment from the US National Institutes of Health and technical support from Family Health International 360 in 2013, as a nationwide collaborative clinical trial network to strengthen selected clinical site research capacity and attract TB clinical trials. The program aims to: 1) recruit leading hospitals that care for TB patients; 2) conduct on-site assessment to identify capacity gaps and needs for improvement; 3) design and deliver capacity building activities; 4) attract and deliver high quality results for TB clinical trials. A total of 24 sites have joined CTCTC, covering 20 provinces in China. Twenty-two sites have been accredited by the National Medical Products Administration (NMPA) to be qualified to conduct TB clinical trials. The onsite assessment, extensive trainings among the CTCTC sites and young investigators have resulted in better understanding and improvement of the site capacity in conducting TB clinical trials. The establishment and growth of the CTCTC network has benefited from the good leadership, effective international cooperation and local commitment. Issues in human resources, regulatory environment and sustainability have been challenging the network from continuing growth. Clinical researchers have full-time clinical responsibilities in China and it is thus important to build a cadre of other human resources to assist. The regulatory environment is becoming friendlier in China to introduce international clinical trials to the CTCTC network.ConclusionsThe CTCTC, with mature management structure and sustainable development model, which are distilled five key lessons for other developing countries or investigators of interest. They are the respectively using assessment-based approach to design tailored training package, understanding the availability of clinical researchers, providing solutions to maintain sustainability, understanding local regulatory environments and working with an international organization with local on-site team, respectively. Although, the experiences and capacity of China's TB hospitals in conducting clinical research vary. Considerable efforts to continue building the capacity are still needed, although the gap is smaller for a few top-tier hospitals.
| S-EPMC7229594 | biostudies-literature
Project description:ImportanceMega-trials can provide large-scale evidence on important questions.ObjectiveTo explore how the results of mega-trials compare with the meta-analysis results of trials with smaller sample sizes.Data sourcesClinicalTrials.gov was searched for mega-trials until January 2023. PubMed was searched until June 2023 for meta-analyses incorporating the results of the eligible mega-trials.Study selectionMega-trials were eligible if they were noncluster nonvaccine randomized clinical trials, had a sample size over 10 000, and had a peer-reviewed meta-analysis publication presenting results for the primary outcome of the mega-trials and/or all-cause mortality.Data extraction and synthesisFor each selected meta-analysis, we extracted results of smaller trials and mega-trials included in the summary effect estimate and combined them separately using random effects. These estimates were used to calculate the ratio of odds ratios (ROR) between mega-trials and smaller trials in each meta-analysis. Next, the RORs were combined using random effects. Risk of bias was extracted for each trial included in our analyses (or when not available, assessed only for mega-trials). Data analysis was conducted from January to June 2024.Main outcomes and measuresThe main outcomes were the summary ROR for the primary outcome and all-cause mortality between mega-trials and smaller trials. Sensitivity analyses were performed with respect to the year of publication, masking, weight, type of intervention, and specialty.ResultsOf 120 mega-trials identified, 41 showed a significant result for the primary outcome and 22 showed a significant result for all-cause mortality. In 35 comparisons of primary outcomes (including 85 point estimates from 69 unique mega-trials and 272 point estimates from smaller trials) and 26 comparisons of all-cause mortality (including 70 point estimates from 65 unique mega-trials and 267 point estimates from smaller trials), no difference existed between the outcomes of the mega-trials and smaller trials for primary outcome (ROR, 1.00; 95% CI, 0.97-1.04) nor for all-cause mortality (ROR, 1.00; 95% CI, 0.97-1.04). For the primary outcomes, smaller trials published before the mega-trials had more favorable results than the mega-trials (ROR, 1.05; 95% CI, 1.01-1.10) and subsequent smaller trials published after the mega-trials (ROR, 1.10; 95% CI, 1.04-1.18).Conclusions and relevanceIn this meta-research analysis, meta-analyses of smaller studies showed overall comparable results with mega-trials, but smaller trials published before the mega-trials gave more favorable results than mega-trials. These findings suggest that mega-trials need to be performed more often given the relative low number of mega-trials found, their low significant rates, and the fact that smaller trials published prior to mega-trial report more beneficial results than mega-trials and subsequent smaller trials.
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Project description:IntroductionManaging clinical trials is a complex process requiring careful integration of human, technology, compliance, and operations for success. We collaborated with experts to develop a multi-axial Clinical Trials Management Ecosystem (CTME) maturity model (MM) to help institutions identify best practices for CTME capabilities.MethodsA working group of research informaticists was established. An online session on maturity models was hosted, followed by a review of the candidate domain axes and finalization of the axes. Next, maturity level attributes were defined for min/max levels (level 1 and level 5) for each axis of the CTME MM, followed by the intermediate levels. A REDCap survey comprising the model's statements was then created, and a subset of working group members tested the model by completing it at their respective institutions. The finalized survey was distributed to all working group members.ResultsWe developed a CTME MM comprising five maturity levels across 11 axes: study management, regulatory and audit management, financial management, investigational product management, subject identification and recruitment, subject management, data, reporting analytics & dashboard, system integration and interfaces, staff training & personnel management, and organizational maturity and culture. Informaticists at 22 Clinical and Translational Science Award hubs and one other organization self-assessed their institutional CTME maturity. Respondents reported relatively high maturity for study management and investigational product management. The reporting analytics & dashboard axis was the least mature.ConclusionThe CTME MM provides a framework to research organizations to evaluate their current clinical trials management maturity across 11 axes and identify areas for future growth.
| S-EPMC11883580 | biostudies-literature
Project description:BackgroundPatient participation in clinical trials is vital for knowledge advancement and outcomes improvement. Few adult cancer patients participate in trials. Although patient.decision-making about trial participation has been frequently examined, the participation rate for patients actually offered a trial is unknown.MethodsA systematic review and meta-analysis using 3 major search engines was undertaken. We identified studies from January 1, 2000, to January 1, 2020, that examined clinical trial participation in the United States. Studies must have specified the numbers of patients offered a trial and the number enrolled. A random effects model of proportions was used. All statistical tests were 2-sided.ResultsWe identified 35 studies (30 about treatment trials and 5 about cancer control trials) among which 9759 patients were offered trial participation. Overall, 55.0% (95% confidence interval [CI] = 49.4% to 60.5%) of patients agreed to enroll. Participation rates did not differ between treatment (55.0%, 95% CI = 48.9% to 60.9%) and cancer control trials (55.3%, 95% CI = 38.9% to 71.1%; P = .98). Black patients participated at similar rates (58.4%, 95% CI = 46.8% to 69.7%) compared with White patients (55.1%, 95% CI = 44.3% to 65.6%; P = .88). The main reasons for nonparticipation were treatment choice or lack of interest.ConclusionsMore than half of all cancer patients offered a clinical trial do participate. These findings upend several conventional beliefs about cancer clinical trial participation, including that Black patients are less likely to agree to participate and that patient decision-making is the primary barrier to participation. Policies and interventions to improve clinical trial participation should focus more on modifiable systemic structural and clinical barriers, such as improving access to available trials and broadening eligibility criteria.
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Project description:HeartShare - Extant Datasets - Harmonized Clinical Trials Collection
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Project description:This SuperSeries is composed of the SubSeries listed below.
2019-03-03 | GSE113380 | GEO
Project description:Animal "avatars" and co-clinical trials are being developed for possible use in personalized medicine in oncology. In a co-clinical trial, the cancer cells of the patient's tumor are xenotransplanted into the animal avatar for drug efficacy studies, and the data collected in the animal trial are used to plan the best drug treatment in the patient trial. Zebrafish have recently been proposed for implementing avatar models, however the lack of a general criterion for the chemotherapy dose conversion from humans to fish is a limitation in terms of conducting co-clinical trials. Here, we validate a simple, reliant and cost-effective avatar model based on the use of zebrafish embryos. By crossing data from safety and efficacy studies, we found a basic formula for estimating the equivalent dose for use in co-clinical trials which we validated in a clinical study enrolling 24 adult patients with solid cancers (XenoZ, NCT03668418).
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