Clinical Trials最新文献

Background/aims: Certain sociodemographic groups are routinely underrepresented in clinical trials, limiting generalisability. Here, we describe the extent to which enriched enrolment approaches yielded a diverse trial population enriched for older age in a randomised controlled trial of a blood-based multi-cancer early detection test (NCT05611632).

Methods: Participants aged 50-77 years were recruited from eight Cancer Alliance regions in England. Most were identified and invited from centralised health service lists; a dynamic invitation algorithm was used to target those in older and more deprived groups. Others were invited by their general practice surgery (GP-based Participant Identification Centres in selected regions); towards the end of recruitment, specifically Asian and Black individuals were invited via this route, as part of a concerted effort to encourage enrolment among these individuals. Some participants self-referred, often following engagement activities involving community organisations. Enrolment took place in 11 mobile clinics at 151 locations that were generally more socioeconomically deprived and ethnically diverse than the England average. We reduced logistical barriers to trial participation by offering language interpretation and translation and disabled access measures. After enrolment, we examined (1) sociodemographic distribution of participants versus England and Cancer Alliance populations, and (2) number needed to invite (NNI; the number of invitations sent to enrol one participant) by age, sex, index of multiple deprivation (IMD) and ethnicity, and GP surgery-level bowel screening participation.

Results: Approximately 1.5 million individuals were invited and 142,924 enrolled (98% via centralised health service lists/invitation algorithm) in 10.5 months. The enrolled population was older and more deprived than the England population aged 50-77 years (73.3% vs 56.8% aged 60-77 years; 42.3% vs 35.3% in IMD groups 1-2). Ethnic diversity was lower in the trial than the England population (1.4% vs 2.8% Black; 3.3% vs 5.3% Asian). NNI was highest in Black (32.8), Asian (28.2) and most-deprived (21.5) groups, and lowest in mixed ethnicity (8.1) and least-deprived (4.6) groups.

Conclusions: Enrolment approaches used in the NHS-Galleri trial enabled recruitment of an older, socioeconomically diverse participant population relatively rapidly. Compared with the England and Cancer Alliance populations, the enrolled population was enriched for those in older age and more deprived groups. Better ethnicity data availability in central health service records could enable better invitation targeting to further enhance ethnically diverse recruitment. Future research should evaluate approaches used to facilitate recruitment from underrepresented groups in clinical trials.

Background/aimsFor 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.MethodsThis 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.ResultsOur 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.ConclusionFor 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.

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.

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.

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: Clinical trials provide an opportunity to identify new treatments and can offer patients access to treatments otherwise unavailable. However, approximately 10% of paediatric clinical trials discontinue before the trial has completed. If this premature termination is because the trial treatment(s) being investigated are identified to be ineffective or unsafe, it results in the abrupt discontinuation of the investigational medicinal product for participants. For some participants, there may not be other treatment options to pursue at the trial-end. Trials prematurely terminating can be a distressing experience for all involved and currently there is little published evidence about the guidance provided to healthcare professionals in the event of premature trial termination. The study protocol is the source of guidance for healthcare professionals delivering clinical research, detailing how to conduct all aspects of the trial. The aim was to quantify the proportion of clinical trial protocols that included premature trial termination and subsequently those that provided instructions related to participant management and care. In addition, to analyse the context in which premature termination was included and the detail of any instructions for participant management and care.

Methods: The ClinicalTrials.gov database was searched by a single reviewer for UK interventional drug trials enrolling children with an available study protocol. Protocols were searched to assess if the risk of premature trial termination was identified, the context for premature termination being included, if information was provided to support the management and care of participants should this situation occur and the detail of those instructions. Data were summarised descriptively.

Results: Of 245 clinical trial protocols, 235 (95.9%) identified the possibility of premature trial termination, the majority within the context of the sponsor asserting their right to terminate the trial (82.7%, 115/235) and providing reasons why the trial could be stopped (65.5%, 91/235). Forty-two percent (98/235) provided guidance for participant management and care, most commonly to contact/inform the participant (45.9%, 45/98). Directions varied in the quantity and level of detail.

Conclusions: This review of UK clinical trial protocol highlights that information surrounding premature termination is lacking, with only 42% providing guidance on the care of trial participants. While this ensures regulatory compliance, it fails to consider the challenge for healthcare professionals in managing participants on-going care or the duty of care owed to participants. Further research is required to understand if additional documents are being used in practice, and if these meet the needs of healthcare professionals in supporting research participants and families during premature trial termination

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.

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.