Clinical Trials最新文献

The protection from COVID-19 vaccination wanes a few months post-administration of the primary vaccination series or booster doses. New COVID-19 vaccine candidates aiming to help control COVID-19 should show long-term efficacy, allowing a possible annual administration. Until correlates of protection are strongly associated with long-term protection, it has been suggested that any new COVID-19 vaccine candidate must demonstrate at least 75% efficacy (although a 40%-60% efficacy would be sufficient) at 12 months in preventing illness in all age groups within a large randomized controlled efficacy trial. This article discusses four of the many scientific, ethical, and operational challenges that these trials will face in developed countries, focusing on a pivotal trial in adults. These challenges are (1) the comparator and trial population; (2) how to enroll sufficient numbers of adult participants of all age groups considering that countries will recommend COVID-19 booster doses to different populations; (3) whether having access to a comparator booster for the trial is actually feasible; and (4) the changing epidemiology of severe acute respiratory syndrome coronavirus 2 across countries involved in the trial. It is desirable that regulatory agencies publish guidance on the requirements that a trial like the one discussed should comply with to be acceptable from a regulatory standpoint. Ideally, this should happen even before there is a vaccine candidate that could fulfill the requirements mentioned above, as it would allow an open discussion among all stakeholders on its appropriateness and feasibility.

A growing literature has explored the ethical obligations and current practices related to sharing aggregate results with research participants. However, no prior work has examined these issues in the context of pragmatic clinical trials. Several characteristics of pragmatic clinical trials may complicate both the ethics and the logistics of sharing aggregate results. Among these characteristics include that pragmatic clinical trials may affect the rights, welfare, and interests of not only patient-subjects but also clinicians, meaning that results may be owed to a broader range of groups than typically considered in other research contexts. In addition, some pragmatic clinical trials are conducted under a waiver of informed consent, meaning sharing results may alert participants that they were enrolled without their consent. This article explores the ethical dimensions that can inform decision-making about sharing aggregate results from pragmatic clinical trials, and provides recommendations for that sharing. A central insight is that healthcare institutions-as key partners for the conduct of pragmatic clinical trials-must also be key partners in decision-making about sharing aggregate pragmatic clinical trial results. We conclude with insights for future research.

Background: Over the course of a clinical trial, irregularities may arise in the data. Trialists implement human-intensive, expensive central statistical monitoring procedures to identify and correct these irregularities before the results of the trial are analyzed and disseminated. Machine learning algorithms have shown promise for identifying center-level irregularities in multi-center clinical trials with minimal human intervention. We aimed to characterize the form-level data irregularities in several historical clinical trials and evaluate the ability of a machine learning-based outlier detection algorithm to identify them.

Methods: Data irregularities previously identified by humans in historical clinical trials were ascertained by comparing preliminary snapshots of the trial databases to the final, locked databases. We measured the ability of a machine learning based outlier detection algorithm to identify form-level irregularities using concordance (area under the receiver operator characteristic), positive predictive value (precision), and sensitivity (recall).

Results: We examined preliminary snapshots of seven historical clinical trials which randomized a total of 77,001 participants. We extracted a total of 1,267,484 completed entries from 358 case report forms containing irregularities from all snapshots across all trials, containing a total of 24,850 form-wide irregularities (median per-form form-level irregularity rate: 1.81%). Our proposed machine learning algorithm detects form-level irregularities with a median concordance of 0.74 (interquartile range = 0.57-0.89), slightly exceeding the performance of a previously proposed machine learning approach with a median area under the receiver operator characteristic of 0.73 (interquartile range = 0.54-0.88).

Conclusion: Data irregularities in historical clinical trials were ascertained by comparing preliminary snapshots of the trial database to the final database. These irregularities can be categorized according to their scope. Irregularities can be successfully detected by a machine learning algorithm as early or earlier than a human can, without human intervention. Such an approach may complement existing techniques for central statistical monitoring in large multi-center randomized controlled trials and possibly improve the efficiency of costly data verification processes.

Monitoring the conduct of phase III randomized controlled trials is driven by ethical reasons to protect the study integrity and the safety of trial participants. We propose a group sequential, pragmatic approach for monitoring the accumulating efficacy information in randomized controlled trials. The "Population Health Research Institute boundary" is simple to implement and sensible, as it considers the reduction in uncertainty with increasing information as the study progresses. It is also pragmatic, since it takes into consideration the typical monitoring behavior of monitoring committees of large multicenter trials and is relatively easily implemented. It not only controls the overall Lan-DeMets type I error probability (alpha) spent, but performs better than other group sequential boundaries for the total nominal study alpha. We illustrate the use of our monitoring approach in the early termination of two past completed trials.

Background/aims: The Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE^®) was developed to capture symptomatic adverse events from the patient perspective. We aim to describe statistical properties of PRO-CTCAE items and summary scores and to provide evidence for recommendations regarding PRO-CTCAE administration and reporting.

Methods: Using data from the PRO-CTCAE validation study (NCT02158637), prevalence, means, and standard deviations of PRO-CTCAE items, composite scores, and mean and maximum scores across attributes (frequency, severity, and/or interference) of symptomatic adverse events were calculated. For each adverse event, correlations and agreement between attributes, correlations between attributes and composite scores, and correlations between composite, mean, and maximum scores were estimated.

Results: PRO-CTCAE items were completed by 899 patients with various cancer types. Most patients reported experiencing one or more adverse events, with the most prevalent being fatigue (87.7%), sad/unhappy feelings (66.0%), anxiety (63.6%), pain (63.2%), insomnia (61.8%), and dry mouth (60.0%). Attributes were moderately to strongly correlated within an adverse event (r = 0.53 to 0.77, all p < 0.001) but not fully concordant (κ_weighted = 0.26 to 0.60, all p < 0.001), with interference demonstrating lowest mean scores and prevalence among attributes of the same adverse event. Attributes were moderately to strongly correlated with composite scores (r = 0.67 to 0.97, all p < 0.001). Composite scores were moderately to strongly correlated with mean and maximum scores for the same adverse event (r = 0.69 to 0.94, all p < 0.001). Correlations between composite scores of different adverse events varied widely (r = 0.04 to 0.68) but were moderate to strong for conceptually related adverse events.

Conclusions: Results provide evidence for PRO-CTCAE administration and reporting recommendations that the full complement of attributes be administered for each adverse event, and that attributes as well as summary scores be reported.

Background: Amid 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.

Methods: A 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.

Results: Of 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.

Conclusion: This 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.

Background/aims: For cancers with low incidence, low event rates, and a time-to-event endpoint, a randomized non-inferiority trial designed based on the logrank test can require a large sample size with significantly prolonged enrollment duration, making such a non-inferiority trial not feasible. This article evaluates a design based on a non-inferiority test of proportions, compares its required sample size to the non-inferiority logrank test, assesses whether there are scenarios for which a non-inferiority test of proportions can be more efficient, and provides guidelines in usage of a non-inferiority test of proportions.

Methods: This article describes the sample size calculation for a randomized non-inferiority trial based on a non-inferiority logrank test or a non-inferiority test of proportions. The sample size required by the two design methods are compared for a wide range of scenarios, varying the underlying Weibull survival functions, the non-inferiority margin, and loss to follow-up rate.

Results: Our results showed that there are scenarios for which the non-inferiority test of proportions can have significantly reduced sample size. Specifically, the non-inferiority test of proportions can be considered for cancers with more than 80% long-term survival rate. We provide guidance in choice of this design approach based on parameters of the Weibull survival functions, the non-inferiority margin, and loss to follow-up rate.

Conclusion: For cancers with low incidence and low event rates, a non-inferiority trial based on the logrank test is not feasible due to its large required sample size and prolonged enrollment duration. The use of a non-inferiority test of proportions can make a randomized non-inferiority Phase III trial feasible.