Clinical Trials最新文献

Background/aimsRare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.S. Food and Drug Administration's Center for Drug Evaluation and Research over the past decade and characterized key regulatory and trial design elements with a focus on the primary efficacy endpoint utilized as the basis of approval.MethodsUsing the Food and Drug Administration's Data Analysis Search Host database, we identified novel new drug applications and biologics license applications with orphan drug designation that were approved between 2013 and 2022 for non-oncologic indications. From Food and Drug Administration review documents and other external databases, we examined characteristics of pivotal trials for the included drugs, such as therapeutic area, trial design, and type of primary efficacy endpoints. Differences in trial design elements associated with primary efficacy endpoint type were assessed such as randomization and blinding. Then, we summarized the primary efficacy endpoint types utilized in pivotal trials by therapeutic area, approval pathway, and whether the disease etiology is well defined.ResultsOne hundred and seven drugs that met our inclusion criteria were approved between 2013 and 2022. Assessment of the 107 drug development programs identified 150 pivotal trials that were subsequently analyzed. The pivotal trials were mostly randomized (80%) and blinded (69.3%). Biomarkers (41.1%) and clinical outcomes (42.1%) were commonly utilized as primary efficacy endpoints. Analysis of the use of clinical trial design elements across trials that utilized biomarkers, clinical outcomes, or composite endpoints did not reveal statistically significant differences. The choice of primary efficacy endpoint varied by the drug's therapeutic area, approval pathway, and whether the indicated disease etiology was well defined. For example, biomarkers were commonly selected as primary efficacy endpoints in hematology drug approvals (70.6%), whereas clinical outcomes were commonly selected in neurology drug approvals (69.6%). Further, if the disease etiology was well defined, biomarkers were more commonly used as primary efficacy endpoints in pivotal trials (44.7%) than if the disease etiology was not well defined (27.3%).DiscussionIn the past 10 years, numerous novel drugs have been approved to treat non-oncologic rare diseases in various therapeutic areas. To demonstrate their efficacy for regulatory approval, biomarkers and clinical outcomes were commonly utilized as primary efficacy endpoints. Biomarkers were not only frequently used as s

Background: Implementation and hybrid effectiveness-implementation trials aspire to speed the translation of science into practice by generating crucial evidence for improving the uptake of effective health interventions. By design, they pose unique recruitment and retention challenges due to their aims, units of analysis, and sampling plans, which typically require many clinical sites (i.e. often 20 or more) and participation by individuals who are related across multiple levels (e.g. linked organizational leaders, clinicians, and patients). In this article, we present a new multilevel, theory-informed, and relationship-centered framework for conceptualizing recruitment and retention in implementation and hybrid effectiveness-implementation trials which integrates and builds on prior work on recruitment and retention strategies in patient-focused trials. We describe the framework's application in the Working to Implement and Sustain Digital Outcome Measures hybrid type III trial, which occurred in part during the COVID-19 pandemic.

Methods: Recruitment for the Working to Implement and Sustain Digital Outcome Measures trial occurred from October 2019 to February 2022. Development of recruitment and retention strategies was guided by a newly developed multilevel framework, which targeted the capability, opportunity, and motivation of organizational leaders, clinicians, patient-facing administrative staff, and patients to engage in research. A structured assessment guide was developed and applied to refine recruitment and retention approaches throughout the trial. We describe the framework and its application amid the onset of the COVID-19 pandemic which required rapid adjustments to address numerous barriers.

Results: The Working to Implement and Sustain Digital Outcome Measures trial enrolled 21 outpatient clinics in three US states, incorporating 252 clinicians and 686 caregivers of youth (95% of patient recruitment target) across two distinct phases. Data completion rates for organizational leaders and clinicians averaged 90% over five waves spanning 18 months, despite the onset of the COVID pandemic. Caregiver completion rates of monthly follow-up assessments ranged from 80%-88% across 6 months. This article presents the multilevel framework, assessment guide, and strategies used to achieve recruitment and retention targets at each level.

Conclusion: We conducted a multi-state hybrid type III effectiveness-implementation trial that maintained high recruitment and retention across all relevant levels amid a global pandemic. The newly developed multilevel recruitment and retention framework and assessment guide presented here, which integrates behavioral theory, a relationship-focused lens, and evidence-based strategies for participant recruitment and retention at multiple levels, can be adapted and used by other researchers for implementation, hybrid, and m

Background/aimsRandomized clinical trials often use stratification to ensure balance between arms. Analysis of primary endpoints of these trials typically uses a "stratified analysis," in which analyses are performed separately in each subgroup defined by the stratification factors, and those separate analyses are weighted and combined. In the phase 3 setting, stratified analyses based on a small number of stratification factors can provide a small increase in power. The impact on power and type-1 error of stratification in the setting of smaller sample sizes as in randomized phase 2 trials has not been well characterized.MethodsWe performed computational studies to characterize the power and cross-arm balance of modestly sized clinical trials (less than 170 patients) with varying numbers of stratification factors (0-6), sample sizes, randomization ratios (1:1 vs 2:1), and randomization methods (dynamic balancing vs stratified block).ResultsWe found that the power of unstratified analyses was minimally impacted by the number of stratification factors used in randomization. Analyses stratified by 1-3 factors maintained power over 80%, while power dropped below 80% when four or more stratification factors were used. These trends held regardless of sample size, randomization ratio, and randomization method. For a given randomization ratio and sample size, increasing the number of factors used in randomization had an adverse impact on cross-arm balance. Stratified block randomization performed worse than dynamic balancing with respect to cross-arm balance when three or more stratification factors were used.ConclusionStratified analyses can decrease power in the setting of phase 2 trials when the number of patients in a stratification subgroup is small.

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/aimsWhen conducting a randomised controlled trial in surgery, it is important to consider surgical learning, where surgeons' familiarity with one, or both, of the interventions increases during the trial. If present, learning may compromise trial validity. We demonstrate a statistical investigation into surgical learning within a trial of cleft palate repair.MethodsThe Timing of Primary Surgery compared primary surgery, using the Sommerlad technique, for cleft palate repair delivered at 6 or 12 months of age. Participating surgeons had varying levels of experience with the intervention and in repair across the age groups. Trial design aimed to reduce the surgical learning via pre-trial surgical technique training and balancing the randomisation process by surgeon. We explore residual learning effects by applying visual methods and statistical models to a surgical outcome (fistula formation) and a process indicator (operation time).ResultsNotably, 26 surgeons operated on 521 infants. As the trial progressed, operation time reduced for surgeons with no pre-trial Sommerlad experience (n = 2), before plateauing at 30 operations, whereas it remained stable for those with prior experience. Fistula rates remained stable regardless of technique experience. Pre-trial age for primary surgery experience had no impact on either measures.ConclusionManaging learning effects through design was not fully achieved but balanced between trial arms, and residual effects were minimal. This investigation explores the presence of learning, within a randomised controlled trial that may be valuable for future trials. We recommend such investigations are undertaken to aid trial interpretation and generalisability, and determine success of trial design measures.

Background: The ATHENA COVID-19 study was set up to recruit a cohort of patients with linked health information willing to be recontacted in future to participate in clinical trials and also to investigate the outcomes of people with COVID-19 in Queensland, Australia, using consent. This report describes how patients were recruited, their primary care data extracted, proportions consenting, outcomes of using the recontact method to recruit to a study, and experiences interacting with general practices requested to release the primary care data.

Methods: Patients diagnosed with COVID-19 from 1 January 2020 to 31 December 2020 were systematically approached to gain consent to have their primary healthcare data extracted from their general practice into a Queensland Health database and linked to other datasets for ethically approved research. Patients were also asked to consent to allow future recontact to discuss participation in clinical trials and other research studies. Patients who consented to recontact were later approached to recruit to a long-COVID study. Patients' general practices were contacted to export the patient files. All patient and general practice interactions were recorded. Outcome measures were proportions of patients consenting to data extraction and research, permission to recontact, proportions of general practices agreeing to participate. A thematic analysis was conducted to assess attitudes regarding export of healthcare data, and the proportions consenting to participate in the long-COVID study were also reported.

Results: Of 1212 patients with COVID-19, contact details were available for 1155; 995 (86%) were successfully approached, and 842 (85%) reached a consent decision. Of those who reached a decision, 581 (69%), 615 (73%) and 629 (75%) patients consented to data extraction, recontact, and both, respectively. In all, 382 general practices were contacted, of whom 347 (91%) had an electronic medical record compatible for file export. Of these, 335 (88%) practices agreed to participate, and 12 (3%) declined. In total, 526 patient files were exported. The majority of general practices supported the study and accepted electronic patient consent as legitimate. For the long-COVID study, 376 (90%) of those patients recontacted agreed to have their contact details passed onto the long-COVID study team and 192 (53%) consented to take part in their study.

Conclusion: This report describes how primary care data were successfully extracted using consent, and that the majority of patients approached gave permission for their healthcare information to be used for research and be recontacted. The consent-to-recontact concept demonstrated its effectiveness to recruit to new research studies. The majority of general practices were willing to export identifiable patient healthcare data for linkage provided consent had been obtained.

BackgroundAmid growing emphasis from pharmaceutical companies, advocacy groups, and regulatory bodies for sharing of individual participant data, recent audits reveal limited sharing, particularly for high-revenue medicines. Therefore, this study aimed to assess the individual participant data-sharing eligibility of clinical trials supporting the Food and Drug Administration approval of the top 30 highest-revenue medicines for 2021.MethodsA cross-sectional analysis was conducted on 316 clinical trials supporting approval of the top 30 revenue-generating medicines of 2021. The study assessed whether these trials were eligible for individual participant data sharing, defined as being publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. Information was gathered from various sources including ClinicalTrials.gov, the European Union Clinical Trials Register, and PubMed. Key factors such as the trial phase, completion dates, and the nature of the data-sharing process were also examined.ResultsOf the 316 trials, 201 (64%) were confirmed eligible for sharing, meaning they were either publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. A total of 102 (32%) were confirmed ineligible, and for 13 (4%), the sponsor indicated that a full research proposal would be required to determine eligibility. The analysis also revealed a higher rate of individual participant data sharing among companies that utilized independent platforms, such as Vivli, for managing their individual participant data-sharing process. Trials not marked as completed had significantly lower eligibility for individual participant data sharing.ConclusionThis study highlights that a substantial portion of trials for top revenue-generating medicines are eligible for individual participant data sharing. However, challenges persist, particularly for trials that are marked as ongoing and for trials where the sharing processes are managed internally by pharmaceutical companies. Data-sharing rates could be improved by adopting open-access individual participant data-sharing models or using independent platforms. Standardizing policies to facilitate immediate individual participant data availability for approved medicines is necessary.

BackgroundIn randomized controlled trials (RCTs), unplanned design modifications due to unexpected circumstances are seldom reported. Naively lumping data from pre- and post-design changes to estimate the size of the treatment effect, as planned in the original study, can introduce systematic bias and limit interpretability of the trial findings. There has been limited discussion on how to estimate the treatment effect when an RCT undergoes major design changes during the trial. Using our recently completed RCT, which underwent multiple design changes, as an example, we examined the statistical implications of design changes on the treatment effect estimates.MethodsOur example RCT aimed to test an advance care planning intervention targeting dementia patients and their surrogate decision-makers compared to usual care. The original trial underwent two major mid-trial design changes resulting in three smaller studies. The changes included altering the number of study arms and adding new recruitment sites, thus perturbing the initial statistical assumptions. We used a simulation study to mimic these design modifications in our RCT, generate independent patient-level data and evaluate naïve lumping of data, a two-stage fixed-effect and random-effect meta-analysis model to obtain an average effect size estimate from all studies. Standardized mean-difference and odds-ratio estimates at post-intervention were used as effect sizes for continuous and binary outcomes, respectively. The performance of the estimates from different methods were compared by studying their statistical properties (e.g. bias, mean squared error, and coverage probability of 95% confidence intervals).ResultsWhen between-design heterogeneity is negligible, the fixed- and random-effect meta-analysis models yielded accurate and precise effect-size estimates for both continuous and binary data. As between-design heterogeneity increased, the estimates from random meta-analysis methods indicated less bias and higher coverage probability compared to the naïve and fixed-effect methods, however the mean squared error was higher indicating greater uncertainty arising from a small number of studies. The between-study heterogeneity parameter was not precisely estimable due to fewer studies. With increasing sample sizes within each study, the effect-size estimates showed improved precision and statistical power.ConclusionsWhen a trial undergoes unplanned major design changes, the statistical approach to estimate the treatment effect needs to be determined carefully. Naïve lumping of data across designs is not appropriate even when the overall goal of the trial remains unchanged. Understanding the implications of the different aspects of design changes and accounting for them in the analysis of the data are essential for internal validity and reporting of the trial findings. Importantly, investigators must disclose the design changes clearly in their study reports.