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.

Introduction: The sequential parallel comparison design has emerged as a valuable tool in clinical trials with high placebo response rates. To further enhance its efficiency and effectiveness, adaptive strategies, such as sample size adjustment and allocation ratio modification can be employed.

Methods: We compared the performance of Jennison and Turnbull's method and the Promising Zone approach for sample size adjustment in a two-phase sequential parallel comparison design study. We also evaluated the impact of allocation ratio adjustments using Neyman and Optimal allocation strategies. Various scenarios were simulated to assess the effects of different design parameters, including weight in the test statistic, initial randomization ratio, and interim analysis timing.

Results: The Promising Zone approach demonstrated superior or comparable power to Jennison and Turnbull's method at equivalent expected sample sizes while maintaining the intuitive property that more promising interim results lead to smaller required follow-up sample sizes. However, the Promising Zone approach may require a larger maximum possible sample size in some cases. The addition of allocation ratio adjustments offered minimal improvements overall, but showed potential benefits when the variance in the treatment group was larger than that in the placebo group. We also applied our findings to a real-world example from the AVP-923 trial in patients with Alzheimer's disease-related agitation, demonstrating the practical implications of adaptive sequential parallel comparison designs in clinical research.

Discussion: Adaptive strategies can significantly enhance the efficiency of sequential parallel comparison designs. The choice between sample size adjustment methods should consider trade-offs between power, expected sample size, and maximum adjusted sample size. Although allocation ratio adjustments showed limited overall impact, they may be beneficial in specific scenarios. Future research should explore the application of these adaptive strategies to binary and survival outcomes in sequential parallel comparison designs.

Background/aims: Low-intervention clinical trials have been established under the European Union Clinical Trials Regulation (EU 536/2014) which aims to simplify the conduct of clinical trials with authorized medicinal products. There is limited experience with conducting low-intervention trials. Therefore, this study aims to report on experiences and perceived (dis)advantages of low-intervention trials.

Methods: We surveyed representatives of all individual clinical trials registered on the public website of the European Union Clinical Trials Information System between 31 January 2022 and 1 December 2023 that evaluated authorized investigational medicinal products and had at least one investigative site in the European Union. These representatives were approached between June 2023 and January 2024.

Results: We received 70 responses (response rate 21%). Of the respondents, 31 represented a trial registered as low-intervention trial, and 39 represented a trial not registered as a low-intervention trial (hereafter "regular trials"). Simplified clinical trial monitoring and an easier regulatory approval process were perceived as the main advantages of low-intervention trials, with respectively 44% and 34% of the respondents indicating this to be an advantage in low-intervention trials. However, the respondents experienced that stringent and unclear regulatory requirements impeded the conduct of low-intervention trials. Respondents involved with regular trials indicated that 39% of the regular trials met the criteria of a low-intervention trial but were not registered as such, among others due to unfamiliarity with this trial category.

Conclusions: We argue that the simplified procedures for low-intervention trials should be more detailed-for example in regulatory guidance-in the future to further simplify the conduct of clinical trials with authorized investigational medicinal products.

Clinical trials of drugs, procedures, and other therapies play a crucial role in advancing medical science by evaluating the safety, efficacy, and optimal use of medical interventions. The design and implementation of these trials have evolved significantly over time, reflecting advancements in medicine, ethics, and methodology. Early historical examples, such as King Nebuchadnezzar II's and his captives' dietary experiment and Ambroise Paré's treatment of gunshot wounds, laid some foundational principles of trial design. The momentum of clinical trial development increased notably with James Lind's 1747 trial for scurvy and continued to progress during World War I with innovations in blood transfusion techniques. World War II (WWII) marked a pivotal moment with breakthroughs in oncology, including the development of the first modern chemotherapeutic agents derived from mustard gas and the introduction of the randomized controlled trial, credited to British epidemiologist Austin Bradford Hill, which revolutionized trial design. More recent conflicts, such as those in Vietnam, Iraq, and Afghanistan, have driven advancements in trauma care, heroin addiction treatment, and hemorrhage management. In response to historical abuses committed by the Nazis during WWII, the evolution of clinical trials has increasingly emphasized ethical standards, particularly informed consent, starting with the Doctors' Trial and the Nuremberg Code. This article discusses how military needs and wartime innovations have shaped modern clinical research, highlighting the interplay between military imperatives and medical progress. Ultimately, clinical trials play an essential role in advancing medical science and improving patient outcomes.

Background/aims: Randomized 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.

Methods: We 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).

Results: We 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.

Conclusion: Stratified analyses can decrease power in the setting of phase 2 trials when the number of patients in a stratification subgroup is small.

Background/aims: When 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.

Methods: The 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).

Results: Notably, 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.

Conclusion: Managing 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: In randomized clinical trials, multiple-testing procedures, composite endpoints, and prioritized outcome approaches are increasingly used to analyze multiple binary outcomes. Previous studies have shown that correlations between outcomes influence their sample size requirements. Although sample size is an important factor affecting the choice of statistical methods, the power and required sample sizes of methods for analyzing multiple binary outcomes have yet to be compared under the influence of outcome correlations.

Methods: We conducted simulations to evaluate the power of co-primary and multiple primary endpoints, composite endpoints, and prioritized outcome approaches based on generalized pairwise comparisons with varying correlations, marginal proportions, treatment effects, and number of outcomes. We then conducted a case study on sample size using a clinical trial of a migraine treatment as an example.

Results: The correlations significantly affected the statistical power and sample size of composite endpoints. The power and sample size of co-primary endpoints remained relatively stable across different correlations, though their power declined substantially when treatment effects were opposite on some components or more than two components were present. While the correlations influenced the power and sample size of all methods assessed, their direction and degree of influence varied between methods. Notably, the method with the greatest power and smallest sample size also differed depending on the correlations. When the correlations were the same between arms, prioritized outcome approaches usually had higher power and smaller sample sizes than other methods.

Conclusions: Anticipated correlations and their uncertainty should be considered when selecting statistical methods. Overall, co-primary endpoints remain a reliable option for evaluating the superiority of all components, although they are unsuitable for assessing the balance between treatment effects pointing in different directions. Generalized pairwise comparisons offer a useful alternative to deal with multiple prioritized outcomes, often providing the smallest sample sizes when the correlation structures are shared between the arms.

Background: N-of-1 trials compare two or more treatment options for a single participant. These trials have been used to study options for chronic conditions such as arthritis and attention deficit hyperactivity disorder. In addition, they have been suggested as a means to study interventions in rare populations that may not be tractable to include in standard clinical trials, such as treatment options for HIV-positive patients in need of organ transplant. Sequential monitoring of accruing data has been well-studied in traditional clinical trials, but these methods have not yet been implemented in N-of-1 trials. However, the option to validly stop an N-of-1 trial early could deliver faster decisions that could directly improve the patient's health.

Methods: In this work, we propose and evaluate a framework to (1) facilitate sequential monitoring in individual N-of-1 trials with a continuous outcome and (2) combine results across a series of already-completed sequentially monitored N-of-1 trials. By employing the block structure common to N-of-1 trials, we suggest that existing approaches to sequential monitoring may be employed when data from one N-of-1 trial are analyzed with a linear mixed-effects model. To combine results across a series of already-completed sequentially monitored N-of-1 trials, we propose combining the naive estimates from constituent trials in a random-effects model with inverse-variance weighting. We evaluate these proposals via simulation.

Results: We find that type 1 error can be substantially inflated for N-of-1 trials with a small number of planned blocks but can reach the nominal rate for trials with more planned blocks or those with larger numbers of periods per block or by using a $t$-value correction. For those settings with acceptable type 1 error, sequential monitoring results in similar power and on average earlier stopping compared with trials with no sequential monitoring. And, as expected, we find that including a larger number of constituent trials in a series reduces the mean-squared error of the combined point estimator.

Conclusion: Under suitable design considerations, our proposed framework for sequential monitoring can support clinicians in providing important decisions earlier, on average, for patients engaged in N-of-1 trials.