Clinical Trials最新文献

Background/aims: People with disability have higher rates of cancer, excluding skin cancer, compared with people without disability. Food and Drug Administration draft guidelines from 2024 address use of performance status criteria to determine eligibility for clinical trials, advocating for less restrictive thresholds. We examined the exclusion of people with disability from clinical trials based on performance status and other criteria.

Methods: We reviewed eligibility criteria in approved interventional Phase III and Phase IV oncology clinical trials listed on ClinicalTrails.gov between 1 January 2019 and 31 December 2023. Functional status thresholds were assessed using the Eastern Cooperative Oncology Group Performance Status Scale and Karnofsky Performance Scale in clinical trial eligibility criteria. Qualitative analysis was used to review eligibility criteria relating to functional impairments or disability.

Results: Among 96 oncology clinical trials, approximately 40% had restrictive Eastern Cooperative Oncology Group and Karnofsky Performance Scale thresholds, explicitly including only patients with Eastern Cooperative Oncology Group 0 or 1, or equivalent Karnofsky Performance Scale 70 or greater. Only 20% of studies included patients with Eastern Cooperative Oncology Group 2 and Karnofsky Performance Scale 60. Multiple studies contained miscellaneous eligibility criteria that could potentially exclude people with disability. No studies described making accommodations for people with disability to participate in the clinical trial.

Conclusion: Draft Food and Drug Administration guidelines recommend including patients with Eastern Cooperative Oncology Group scores of 2 and Karnofsky Performance Scale scores of 60 in oncology clinical trials. We found that oncology clinical trials often exclude people with more restrictive performance status scores than the draft Food and Drug Administration guidelines, as well as other criteria that relate to disability. These estimates provide baseline information for assessing how the 2024 Food and Drug Administration guidance, if finalized, might affect the inclusion of people with disability in future trials.

Unlocking the power of personalised medicine in oncology hinges on the integration of clinical trial data with translational data (i.e. biospecimen-derived molecular information). This combined analysis allows researchers to tailor treatments to a patient's unique biological makeup. However, current practices within UK Clinical Trials Units present challenges. While clinical data are held in standardised formats, translational data are complex, diverse, and requires specialised storage. This disparity in format creates significant hurdles for researchers aiming to curate, integrate and analyse these datasets effectively. This article proposes a novel solution: an open-source SQL database schema designed specifically for the needs of academic trial units. Inspired by Cancer Research UK's commitment to open data sharing and exemplified by the Southampton Clinical Trials Unit's CONFIRM trial (with over 150,000 clinical data points), this schema offers a cost-effective and practical 'middle ground' between raw data and expensive Secure Data Environments/Trusted Research Environments. By acting as a central hub for both clinical and translational data, the schema facilitates seamless data sharing and analysis. Researchers gain a holistic view of trials, enabling exploration of connections between clinical observations and the molecular underpinnings of treatment response. Detailed instructions for setting up the database are provided. The open-source nature and straightforward design ensure ease of implementation and affordability, while robust security measures safeguard sensitive data. We further showcase how researchers can leverage popular statistical software like R to directly query the database. This approach fosters collaboration within the academic discovery community, ultimately accelerating progress towards personalised cancer therapies.

Background: Enoximone, a phosphodiesterase III inhibitor, was approved in Germany in 1989 and initially proposed for heart failure and perioperative cardiac conditions. The research of Joachim Boldt and his supervisor Gunter Hempelmann came under scrutiny after investigations revealed systematic scientific misconduct leading to numerous retractions. Therefore, early enoximone studies by Boldt and Hempelmann from 1988 to 1991 were reviewed.

Methods: PubMed-listed publications and dissertations on enoximone from the Justus-Liebig-University of Giessen were analyzed for study design, participant demographics, methods, and outcomes. The data were screened for duplications and inconsistencies.

Results: Of seven randomized controlled trial articles identified, two were retracted. Five of the non-retracted articles reported similarly designed studies and included similar patient cohorts. The analysis revealed overlap in patient demographics and reported outcomes and inconsistencies in cardiac index values between trials, suggesting data duplication and manipulation. Several other articles have been retracted. The analysis results of misconduct and co-authorship of retracted studies during Boldt's late formative years indicate inadequate mentorship. The university's slow response in supporting the retraction of publications involving scientific misconduct indicates systemic oversight problems.

Conclusion: All five publications analyzed remained active and warrant retraction to maintain the integrity of the scientific record. This analysis highlights the need for improved institutional supervision. The current guidelines of the Committee on Publication Ethics for retraction are inadequate for large-scale scientific misconduct. Comprehensive ethics training, regular audits, and transparent reporting are essential to ensure the credibility of published research.

Background: The coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional "frequentist" guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary.

Methods: The mpRCT's efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT's futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients' data with imaginary patients' data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O'Brien-Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline.

Results: A Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O'Brien-Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary.

Conclusions: In a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O'Brien-Fleming boundary. This can be accomplished with either Bayesian or frequentist methods.

Pragmatic clinical trials of standard-of-care interventions compare the relative merits of medical treatments already in use. Traditional research informed consent processes pose significant obstacles to these trials, raising the question of whether they may be conducted with alteration or waiver of informed consent. However, to even be eligible, such a trial in the United States must have no more than minimal research risk. We argue that standard-of-care pragmatic clinical trials can be designed to ensure that they are minimal research risk if the random assignment of an intervention in a pragmatic clinical trial can accommodate individualized, clinically motivated decision-making for each participant. Such a design will ensure that the patient-participants are not exposed to any risks beyond the clinical risks of the interventions, and thus, the trial will have minimal research risk. We explain the logic of this view by comparing three scenarios of standard-of-care pragmatic clinical trials: one with informed consent, one without informed consent, and one recently proposed design called Decision Architecture Randomization Trial. We then conclude by briefly showing that our proposal suggests a natural way to determine when to use an alteration versus a waiver of informed consent.