Clinical Trials最新文献

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: Concerns about low accrual have long been a standard part of the discourse on cancer clinical trials, reaching even as far as the news media. Indeed, so many trials are closed before completing accrual that a cottage industry has recently developed creating statistical models to predict trial failure. We previously proposed four methodologic fixes for the current crisis in clinical trials: (1) dramatically reducing the number of eligibility criteria, (2) using data routinely collected in clinical practice for trial endpoints; then lowering barriers to accrual by (3) cluster randomization or (4) staged consent.

Methods: We report our practical experience of applying these fixes to randomized trials at Memorial Sloan Kettering Cancer Center.

Results: We have completed seven single-center randomized trials, with several more underway and accruing rapidly, with a total accrual approaching 10,000. Many of the trials have compared surgical interventions, an area where trials have traditionally been hard to complete. Only one of these trials was externally funded. While low costs were possible due to the existing research infrastructure at our institution, such infrastructure is common at major cancer centers.

Conclusions: Further research on innovative clinical trial designs is warranted, particularly in higher-stakes settings, and in trials of medical and radiotherapy interventions.

Background: Randomized controlled trials with pragmatic intent aim to generate evidence that directly informs clinical decisions. Some have argued that the ethical protection of informed consent can be in tension with the goals of pragmatism. But the impact of other ethical protections on trial pragmatism has yet to be explored.

Purpose: In this article, we analyze the relationship between additional ethical protections for vulnerable participants and the degree of pragmatism within the PRagmatic Explanatory Continuum Indicator Summary-2 (PRECIS-2) domains of trial design.

Methods: We analyze three example trials with pragmatic intent that include vulnerable participants.

Conclusion: The relationship between ethical protections and trial pragmatism is complex. In some cases, additional ethical protections for vulnerable participants can promote the pragmatism of some of the PRECIS-2 domains of trial design. When designing trials with pragmatic intent, researchers ought to look for opportunities wherein ethical protections enhance the degree of pragmatism.

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.

Background/aims: This study aimed to determine the prevalence of ordinal, binary, and numerical composite endpoints among coronavirus disease 2019 trials and the potential bias attributable to their use.

Methods: We systematically reviewed the Cochrane COVID-19 Study Register to assess the prevalence, characteristics, and bias associated with using composite endpoints in coronavirus disease 2019 randomized clinical trials. We compared the effect measure (relative risk) of composite outcomes and that of its most critical component (i.e. death) by estimating the Bias Attributable to Composite Outcomes index [ln(relative risk for the composite outcome)/ln(relative risk for death)].

Results: Composite endpoints accounted for 152 out of 417 primary endpoints in coronavirus disease 2019 randomized trials, being more frequent among studies published in high-impact journals. Ordinal endpoints were the most common (54% of all composites), followed by binary or time-to-event (34%), numerical (11%), and hierarchical (1%). Composites predominated among trials enrolling patients with severe disease when compared to trials with a mild or moderate case mix (odds ratio = 1.72). Adaptations of the seven-point World Health Organization scale occurred in 40% of the ordinal primary endpoints, which frequently underwent dichotomization for the statistical analyses. Mortality accounted for a median of 24% (interquartile range: 6%-48%) of all events when included in the composite. The median point estimate of the Bias Attributable to Composite Outcomes index was 0.3 (interquartile range: -0.1 to 0.7), being significantly lower than 1 in 5 of 24 comparisons.

Discussion: Composite endpoints were used in a significant proportion of coronavirus disease 2019 trials, especially those involving severely ill patients. This is likely due to the higher anticipated rates of competing events, such as death, in such studies. Ordinal composites were common but often not fully appreciated, reducing the potential gains in information and statistical efficiency. For studies with binary composites, death was the most frequent component, and, unexpectedly, composite outcome estimates were often closer to the null when compared to those for mortality death. Numerical composites were less common, and only two trials used hierarchical endpoints. These newer approaches may offer advantages over traditional binary and ordinal composites; however, their potential benefits warrant further scrutiny.

Conclusion: Composite endpoints accounted for more than a third of coronavirus disease 2019 trials' primary endpoints; their use was more common among studies that included patients with severe disease and their point effect estimates tended to underestimate those for mortality.

Composite time-to-event endpoints are commonly used in cardiovascular outcome trials. For example, the IMPROVE-IT trial comparing ezetimibe+simvastatin to placebo+simvastatin in 18,144 patients with acute coronary syndrome used a primary composite endpoint with five component outcomes: (1) cardiovascular death, (2) non-fatal stroke, (3) non-fatal myocardial infarction, (4) coronary revascularization ≥30 days after randomization, and (5) unstable angina requiring hospitalization. In such settings, the traditional analysis compares treatments using the observed time to the occurrence of the first (i.e. earliest) component outcome for each patient. This approach ignores information for subsequent outcome(s), possibly leading to reduced power to demonstrate the benefit of the test versus the control treatment. We use real data examples and simulations to contrast the traditional approach with several alternative approaches that use data for all the intra-patient component outcomes, not just the first.

In randomized clinical trials, analyses of time-to-event data without risk stratification, or with stratification based on pre-selected factors revealed at the end of the trial to be at most weakly associated with risk, are quite common. We caution that such analyses are likely delivering hazard ratio estimates that unwittingly dilute the evidence of benefit for the test relative to the control treatment. To make our case, first, we use a hypothetical scenario to contrast risk-unstratified and risk-stratified hazard ratios. Thereafter, we draw attention to the previously published 5-step stratified testing and amalgamation routine (5-STAR) approach in which a pre-specified treatment-blinded algorithm is applied to survival times from the trial to partition patients into well-separated risk strata using baseline covariates determined to be jointly strongly prognostic for event risk. After treatment unblinding, a treatment comparison is done within each risk stratum and stratum-level results are averaged for overall inference. For illustration, we use 5-STAR to reanalyze data for the primary and key secondary time-to-event endpoints from three published cardiovascular outcomes trials. The results show that the 5-STAR estimate is typically smaller (i.e. more in favor of the test treatment) than the originally reported (traditional) estimate. This is not surprising because 5-STAR mitigates the presumed dilution bias in the traditional hazard ratio estimate caused by no or inadequate risk stratification, as evidenced by two detailed examples. Pre-selection of stratification factors at the trial design stage to achieve adequate risk stratification for the analysis will often be challenging. In such settings, an objective risk stratification approach such as 5-STAR, which is partly aligned with guidance from the US Food and Drug Administration on covariate-adjustment in clinical trials, is worthy of consideration.