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/aimsTo assess pre- and postnatal factors associated with participation in a randomized clinical trial of daily docosahexaenoic supplementation in toddlers born preterm. We hypothesized that enrolled families would not differ from those who did not participate.MethodChildren eligible for the Omega Tots trial were born at <35 completed weeks' gestation and were 10-16 months of age at recruitment. Eligibility data abstracted from the medical record were linked with the child's birth certificate. The primary outcome was whether the family enrolled, declined, or was non-responsive to recruitment efforts. Log-binomial regression calculated risk ratios (RR).Results316 families enrolled, 1089 declined, and 1081 were non-responsive. Enrolling, rather than not enrolling, was negatively associated with caregivers being married (RR = 0.76, 95% CI: 0.62, 0.94), identifying as White (RR = 0.76, 95% CI: 0.60, 0.94), and children being born at later gestational ages (RR1-week = 0.96, 95% CI: 0.92, 0.99); positively associated with children weighing <1500 g at birth (RR = 1.26, 95% CI: 1.01, 1.55), attending a neonatology specialty clinic (RR = 1.46, 95% CI: 1.19, 1.80), family participation in WIC (RR = 1.39, 95% CI: 1.13, 1.72), and living in an urban zip code (RR = 1.68, 95% CI: 1.30, 2.17). Varied associations with enrolling rather than declining, enrolling rather than being non-responsive, and declining rather than being non-responsive were identified.ConclusionsMaternal, child, and socioeconomic characteristics were different for families who enrolled, relative to families who did not enroll. Factors associated with enrollment differed between families who were non-responsive to recruitment attempts and those who declined enrollment, with additional differences identified between families who declined participation and those who were non-responsive. Recruitment initiatives tailored to ensuring enrollees reflect the source population may improve generalizability.

Background: Desirability of outcome ranking (DOOR) is a paradigm for the design, monitoring, analysis, interpretation, and reporting of clinical trials based on patient-centric benefit-risk evaluation, developed to address limitations of existing approaches and advance clinical trial science. The first step in implementing DOOR is defining an ordinal DOOR outcome representing a global patient-centric response, a cumulative summary of the benefits and harms for an individual patient. This article aims to develop an analysis methodology for the setting where the DOOR outcome is a progressive time-varying state, and there is interest in event times and times that patients spend in more and less desirable states.

Methods: We develop methods to estimate and make inferences about the temporal treatment effects. If the k-levels of the DOOR outcome are monotone, then k - 1 non-overlapping Kaplan-Meier survival curves can be estimated and plotted. The areas under the curves asymptotically follow a multivariate Gaussian distribution. We apply restricted mean survival time (RMST) concepts to the ordinal Kaplan-Meier curves and provide steps for estimating the covariance structure.

Results: Simulation studies demonstrate that the proposed methods perform well in practical settings. We generate censoring time under a uniform distribution and event times under a multi-state structure. The proposed estimators have small biases, the 95% confidence intervals have correct coverage probabilities, and the proposed tests accurately control the type I error rate under the null hypothesis. We illustrate the methods using data from Adaptive COVID-19 Treatment Trial (ACTT-1), a clinical trial that compared remdesivir vs placebo for the treatment of COVID-19 infection.

Discussion: Ordinal DOOR outcomes, which incorporate benefits and harms and represent an overall patient response, have recently been recommended by the Council for International Organizations of Medical Sciences (CIOMS) as a standard approach to benefit:risk analysis. Such endpoints recognize the cumulative nature of outcomes on patients, account for correlations between efficacy and safety, incorporate multivariate survival outcomes, offer generalizability to inform clinical practice, and recognize finer gradations of patient response and binary outcomes. Robust and interpretable analysis methodologies for ordinal outcomes are needed.

Conclusion: Restricted mean survival time is a useful nonparametric approach for robust treatment effect estimation. We provide a framework for inference using multiple RMSTs to analyze DOOR and other ordinal outcomes using an interpretable time metric.

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.