Clinical Trials最新文献

Background: Individual non-compliance with an intervention in cluster randomised trials can occur and estimating an intervention effect according to intention-to-treat ignores non-compliance and underestimates efficacy. The effect of the intervention among compliers (the complier average causal effect) provides an unbiased estimate of efficacy but inference can be complex in cluster randomised trials.

Methods: We evaluated the performance of a pragmatic bootstrapping approach accounting for clustering to obtain a 95% confidence interval (CI) for a CACE for cluster randomised trials with monotonicity and one-sided non-compliance. We investigated a variety of scenarios for correlated cluster-level prevalence of a binary outcome and non-compliance (5%, 10%, 20%, 30%, 40%). Cluster randomised trials were simulated with the minimum number of clusters to provide at least 80% and at least 90% power, to detect an ITT odds ratio (OR) of 0.5 with 100 individuals per cluster.

Results: Under all non-compliance scenarios (5%-40%), there was negligible bias for the CACE. In the worst-case of bias, a true OR of 0.18 was estimated as 0.15 for the rarest outcome (5%) and highest non-compliance (40%). There was no under-coverage of bootstrap CIs. CIs were the correct width for an outcome prevalence of 20%-40% but too wide for a less common outcome. Loss of power for a CACE bootstrap analysis versus ITT regression analysis increased as the prevalence of the outcome decreased across all non-compliance scenarios, particularly for an outcome prevalence of less than 20%.

Conclusions: Our bootstrapping approach provides an accessible and computationally simple method to evaluate efficacy in support of ITT analyses in cluster randomised trials.

Background: Valid surrogate endpoints are of great interest for efficient evaluation of novel therapies. With surrogate and true time-to-event endpoints, meta-analytic approaches for surrogacy validation commonly rely on the hazard ratio, ignore that randomized trials possibly contribute to the meta-analysis for different follow-up durations, overlook the importance of the time lag between surrogate and true endpoints in determining surrogate utility, and assume that treatment effects and the strength of surrogacy remain constant over time. In this context, we introduce a novel two-stage meta-analytic model to evaluate trial-level surrogacy.

Methods: Our model employs restricted mean survival time (RMST) differences to quantify treatment effects at the first stage. At the second stage, the model is based on the between-study covariance matrix of RMSTs and differences in RMST to assess surrogacy through coefficients of determination at multiple timepoints. This framework integrates estimates from each component RCT without extrapolation beyond the trial-specific time support, can explicitly model a time lag between endpoints, and remains valid under non-proportional hazards.

Results: Simulation studies indicate that our model yields unbiased and precise estimates of the coefficient of determination. In an individual patient data meta-analysis in gastric cancer, estimates of coefficients of determination from our model reflect the temporal lag between endpoints and reveal dynamic changes in surrogacy strength over time compared to the Clayton survival copula model, a widely used reference method in surrogate endpoint validation for time-to-event outcomes.

Conclusion: Our new meta-analytic model to evaluate trial-level surrogacy using the difference in RMST as the measure of treatment effect does not require the proportional hazard assumption, captures the strength of surrogacy at multiple time points, and can evaluate surrogacy with a time lag between surrogate and true endpoints. The proposed method enhances the rigor and practicality of surrogate endpoint validation in time-to-event settings.

Background: Some scholars have proposed that investigators and health systems should notify patients about their enrollment in pragmatic clinical trials conducted with a waiver of consent. However, others argue that decision-making about notification requires judgment, and reports suggest considerable heterogeneity about whether, when, and how individuals enrolled in pragmatic clinical trials with a waiver of consent are notified about that enrollment. Empirical data can inform this decision-making.

Methods: We conducted semi-structured interviews with knowledgeable stakeholders involved in conducting and/or overseeing pragmatic clinical trials conducted with waivers of consent, including investigators, those charged with the oversight of human subjects research, and operational leadership. Interviews were conducted via video conference from September to December 2024 and were audio-recorded and professionally transcribed. Data were qualitatively analyzed using an integrated approach, including both a priori codes drawn from the interview guide and emergent, inductive codes.

Results: Twenty-three of 28 experts invited to participate completed interviews. Respondents described rationales both for and against notification. Rationales for notification included both appeals to moral values (respect for persons, respect for autonomy, and transparency), as well as instrumental goals (promoting understanding of and/or support for research, avoiding downstream surprise, and supporting buy-in). Rationales against notification included preserving scientific validity, perceiving notification to lack value, and concerns that notification might be burdensome for patient-subjects or undermine trust and/or clinical or public health goals. Decision-making about notification was context-specific and reflected features related to the study design, the health system setting, the patient population, the clinical condition, and the intervention(s) being evaluated. While some factors were consistently described as weighing against notification, including scientific validity or decisions for which a patient would not be offered a choice outside the research context, other factors resulted in divergent decisions across different pragmatic clinical trials (or even across different sites for the same trial).

Conclusions: While several rationales support notification about enrollment in pragmatic clinical trials conducted with waivers of consent, the relative value and practicability of notification is context-dependent. Some features, such as the need to preserve scientific validity, may appropriately weigh in favor of forgoing notification. However, evidence of divergent decision-making for similar trials suggests the need for a framework to guide future notification decisions. These data can be an important input to inform future framework development.

Backgrounds/aims: To secure market authorization, the Food and Drug Administration requires that drug manufacturers demonstrate product safety and efficacy for an indicated use based on two adequate and well-controlled studies, known as pivotal clinical trials. A single pivotal trial may also be sufficient for product approval, however, if safety and efficacy is clearly and convincingly demonstrated, or if accompanied by confirmatory evidence. We examined all original drug and biologic indication approvals by the Food and Drug Administration between 2015 and 2023 to determine what proportion of those approved on the basis of a single pivotal trial were accompanied by confirmatory evidence, the type and strength of this evidence, and whether confirmatory evidence was cited more frequently after December 2019, when the Food and Drug Administration released draft guidance clarifying issues related to confirmatory evidence.

Methods: Information was extracted from publicly available Food and Drug Administration documents, and we used descriptive statistics to characterize the sample and chi-square tests to compare the frequency with which confirmatory evidence was cited before and after December 2019.

Results: Overall, the Food and Drug Administration approved 441 original drug and biologic indications between 2015 and 2023; 40 of which were excluded. Of the remaining, 181 (41%) were based on 2 or more pivotal trials, 35 (7.9%) on a single pivotal trial with at least one clinical primary efficacy endpoint without orphan designation, and 185 (42%) on a single pivotal trial. Among the final category of approvals, the Food and Drug Administration explicitly referenced confirmatory evidence for 36 (19.5%) single pivotal trial approvals and implicitly referenced confirmatory evidence for 4 (2.2%) others. These 40 approvals referenced 99 unique sources of confirmatory evidence, most commonly pharmacodynamic/mechanistic (n = 49) and other (n = 32). Reference to confirmatory evidence was greater after the Food and Drug Administration issued clarifying guidance in December 2019 (pre: 7% vs post: 34%; p < 0.0001).

Conclusions: Given the rising number of the Food and Drug Administration approvals based on a single pivotal trial, greater clarity on confirmatory evidence standards and communication of its use could be considered.

Background: Cluster randomized controlled trials are increasingly used to evaluate the effectiveness of interventions in clinical and public health research. However, missing data in cluster randomized controlled trials can lead to biased results and reduce statistical power if not handled appropriately. This study aimed to review, describe and summarize how missing primary outcome data are handled in reports of publicly funded cluster randomized controlled trials.

Methods: This study reviewed the handling of missing data in cluster randomized controlled trials published in the UK National Institute for Health and Care Research Journals Library from 1 January 1997 to 31 December 2024. Data extraction focused on trial design, missing data mechanisms, handling methods in primary analyses and sensitivity analyses.

Results: Among the 110 identified cluster randomized controlled trials, 45% (50/110) did not report or take any action on missing data in either primary analysis or sensitivity analysis. In total, 75% (82/110) of the identified cluster randomized controlled trials did not impute missing values in their primary analysis. Advanced methods like multiple imputation were applied in only 15% (16/110) of primary analyses and 28% (31/110) of sensitivity analyses. On the contrary, the review highlighted that missing data handling methods have evolved over time, with an increasing adoption of multiple imputation since 2017. Overall, the reporting of how missing data is handled in cluster randomized controlled trials has improved in recent years, but there are still a large proportion of cluster randomized controlled trials lack of transparency in reporting missing data, where essential information such as the assumed missing mechanism could not be extracted from the reports.

Conclusion: Despite progress in adopting multiple imputation, inconsistent reporting and reliance on simplistic methods (e.g. complete case analysis) undermine cluster randomized controlled trial credibility. Recommendations include stricter adherence to CONSORT guidelines, routine sensitivity analyses for different missing mechanisms and enhanced training in advanced imputation techniques. This review provides updated insights into how missing data are handled in cluster randomized controlled trials and highlight the urgency for methodological transparency to ensure robust evidence generation in clustered trial designs.

BackgroundEvaluation of heterogeneity of treatment effect among participants in large randomized clinical trials may provide insights as to the value of individualizing clinical decisions. The effect modeling approach to predictive heterogeneity of treatment effect analysis offers a promising framework for heterogeneity of treatment effect estimation by simultaneously considering multiple patient characteristics and their interactions with treatment to predict differences in outcomes between randomized treatments. However, its implementation in clinical research remains limited and so we provide a detailed example of its application in a randomized trial that compared raltegravir-based vs darunavir/ritonavir-based therapy as initial antiretroviral treatments for people living with HIV.MethodsThe heterogeneity of treatment effect analysis used a two-step procedure, in which a working proportional hazards model was first selected to construct an index score for ranking the treatment difference for individuals, and then a second calibration step used a non-parametric kernel approach to estimate the true treatment difference for participants with similar index scores. Sensitivity and supplemental analyses were conducted to evaluate the robustness of the results. We further explored the impact of covariates on heterogeneity of treatment effect and the choice between treatments.ResultsThe heterogeneity of treatment effect analysis showed that while there is a clear trend favoring raltegravir-based therapy over darunavir/ritonavir-based therapy for the vast majority of the target population, there were a small subset of patients, characterized by more advanced HIV disease status, for whom the choice between the two treatments might be equivocal.ConclusionsThrough this example, we illustrate how an exploratory heterogeneity of treatment effect analysis might provide further insights into the comparative efficacy of treatments evaluated in a randomized trial. We also highlight some of the issues in implementing and interpreting effect modeling analyses in randomized trials.

BackgroundNutrition and dietary trials are often prone to bias, leading to inaccurate or questionable estimates of intervention efficacy. However, reports on quality management practices of well-controlled dietary trials are scarce. This study aims to introduce the quality management system of the Diet, ExerCIse and CarDiovascular hEalth-Diet Study and report its performance in ensuring study quality.MethodsThe quality management system consisted of a study coordinating center, trial governance, and quality control measures covering study design, conduct, and data analysis and reporting. Metrics for evaluating the performance of the system were collected throughout the whole trial development and conducted from September 2016 to June 2021, covering major activities at the coordinating center, study sites, and central laboratories, with a focus on the protocol amendments, protocol deviations (eligibility, fidelity, confounders management, loss to follow-up and outside-of-window visits, and blindness success), and measurement accuracy.ResultsThree amendments to the study protocol enhanced feasibility. All participants (265) met the eligibility criteria. Among them, only 3% were lost to the primary outcome follow-up measurement. More than 95% of participants completed the study, they consumed more than 96% of the study meals, and more than 94% of participants consumed more than 18 meals per week, with no between-group differences. Online monitoring of nutrient targets for the intervention diet showed that all targets were achieved except for the fiber intake, which was 4.3 g less on average. Only 3% experienced a body weight change greater than 2.0 kg, and 3% had medication changes which were not allowed by the study. James' blinding index at the end of the study was 0.68. The end digits of both systolic and diastolic blood pressure readings were distributed equally. For laboratory measures, 100% of standard samples, 97% of blood-split samples, and 87% of urine-split samples had test results within the acceptable range. Only 1.4% of data items required queries, for which only 30% needed corrections.DiscussionThe Diet, ExerCIse and CarDiovascular hEalth-Diet Study quality management system provides a framework for conducting a high-quality dietary intervention clinical trial.