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Randomized noncomparative trials (RNCTs) promise reduced accrual requirements vs randomized controlled comparative trials because RNCTs do not enroll a control group and instead compare outcomes to historical controls or prespecified estimates. We hypothesized that RNCTs often suffer from two methodological concerns: (1) lack of interpretability due to group-specific inferences in nonrandomly selected samples and (2) misinterpretation due to unlicensed between-group comparisons lacking prespecification. The purpose of this study was to characterize RNCTs and the incidence of these two methodological concerns. We queried PubMed and Web of Science on September 14, 2023, to conduct a meta-epidemiological analysis of published RNCTs in any field of medicine. Trial characteristics and the incidence of methodological concerns were manually recorded. We identified 70 RNCTs published from 2002 to 2023. RNCTs have been increasingly published over time (slope = 0.28, 95% CI 0.17–0.39, P < .001). Sixty trials (60/70, 86%) had a lack of interpretability for the primary endpoint due to group-specific inferences. Unlicensed between-group comparisons were present in 36 trials (36/70, 51%), including in the primary conclusion of 31 trials (31/70, 44%), and were accompanied by significance testing in 20 trials (20/70, 29%). Only five (5/70, 7%) trials were found to have neither of these flaws. Although RNCTs are increasingly published over time, the primary analysis of nearly all published RNCTs in the medical literature was unsupported by their fundamental underlying methodological assumptions. RNCTs promise group-specific inference, which they are unable to deliver, and undermine the primary advantage of randomization, which is comparative inference. The ongoing use of the RNCT design in lieu of a traditional randomized controlled comparative trial should therefore be reconsidered.

This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. A machine learning classifier for retrieving randomized controlled trials (RCTs) was developed (the “Cochrane RCT Classifier”), with the algorithm trained using a data set of title–abstract records from Embase, manually labeled by the Cochrane Crowd. The classifier was then calibrated using a further data set of similar records manually labeled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had abstracts of sufficient length to allow machine classification. The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs), and our bootstrap validation found the classifier had recall of 0.99 (95% confidence interval 0.98–0.99) and precision of 0.08 (95% confidence interval 0.06–0.12) in this data set. The final, calibrated RCT classifier correctly retrieved 43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records were more likely to be missed than those more recently published. The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane Reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production. •Systematic review processes need to become more efficient.•Machine learning is sufficiently mature for real-world use.•A machine learning classifier was built using data from Cochrane Crowd.•It was calibrated to achieve very high recall.•It is now live and in use in Cochrane review production systems.

Background Patient outcomes can depend on the treating centre, or health professional, delivering the intervention. A health professional’s skill in delivery improves with experience, meaning that outcomes may be associated with learning. Considering differences in intervention delivery at trial design will ensure that any appropriate adjustments can be made during analysis. This work aimed to establish practice for the allowance of clustering and learning effects in the design and analysis of randomised multicentre trials. Methods A survey that drew upon quotes from existing guidelines, references to relevant publications and example trial scenarios was delivered. Registered UK Clinical Research Collaboration Registered Clinical Trials Units were invited to participate. Results Forty-four Units participated ( N  = 50). Clustering was managed through design by stratification, more commonly by centre than by treatment provider. Managing learning by design through defining a minimum expertise level for treatment provider was common (89%). One-third reported experience in expertise-based designs. The majority of Units had adjusted for clustering during analysis, although approaches varied. Analysis of learning was rarely performed for the main analysis ( n  = 1), although it was explored by other means. The insight behind the approaches used within and reasons for, or against, alternative approaches were provided. Conclusions Widespread awareness of challenges in designing and analysing multicentre trials is identified. Approaches used, and opinions on these, vary both across and within Units, indicating that approaches are dependent on the type of trial. Agreeing principles to guide trial design and analysis across a range of realistic clinical scenarios should be considered.

Cluster randomized trials (CRTs) are frequently used to evaluate interventions in low- and middle-income countries (LMICs). Robust execution and transparent reporting of randomization procedures are essential for successful implementation and accurate interpretation of CRTs. Our objectives were to review the quality of reporting and implementation of randomization procedures in a sample of parallel-arm CRTs conducted in LMICs. We selected a random sample of 300 primary reports of parallel-arm CRTs from a database of 800 CRTs conducted in LMICs between 2017 and 2022. Data were extracted by two reviewers per trial and summarized using descriptive statistics. Among 300 trials, 192 (64%) reported the method of sequence generation, 213 (71%) reported the type of randomization procedure used, 146 (49%) reported who generated the sequence, 136 (45%) reported whether randomization was implemented by an independent person, and 75 (25%) reported a method of allocation concealment. Among those reporting the methods used, suboptimal randomization procedures were common: 28% did not use a computer, 21% did not use restricted randomization, 58% did not use a statistician to generate the sequence, in 53% the person was not independent from the trial, and 80% did not use central randomization. Public randomization ceremonies were used in 10% of trials as an alternative method of allocation concealment and to reassure participants of fair allocation procedures. The conduct and reporting of randomization procedures of CRTs in LMICs is suboptimal. Dissemination of guidance to promote robust implementation of randomization in LMICs is required, and future research on the implementation of public randomization ceremonies is warranted. Cluster randomized trials (CRTs) are trials where entire groups, rather than individuals, are randomly assigned to different treatments (eg, intervention or usual care). This randomization process can be challenging in CRTs; clear reporting and proper execution are important to ensure fairness and accurate results. In this study, we reviewed how well randomization procedures were reported and carried out in 300 CRTs, selected from a larger database of 800 CRTs, conducted in low- and middle-income countries (LMICs), and published between 2017 and 2022. We found that reporting on key aspects of randomization was often incomplete: 64% reported how they created the random allocation sequence, 71% reported the type of randomization method used, 49% reported who generated the sequence, 45% reported whether a person independent from the trial handled the randomization, and 25% reported how they kept group assignments hidden until the intervention was ready to begin. Even when trials did reported these methods, many did not follow best practices: 28% did not use a computer, 21% did not apply techniques to ensure balanced treatment arms, 58% did not involve a statistician to generate the sequence, 53% had someone involved in the trial handle randomization (as opposed to an independent person), and 80% did not use central randomization to assign groups, where a third party reveals treatment assignment to groups. Interestingly, 10% of trials used public randomization ceremonies (events where group assignments are revealed in a public setting) to keep group assignments hidden until revealment and to reassure participants that the process was fair. Overall, we found that randomization procedures in CRTs were often not well reported or carried out optimally. It is important for researchers to follow established guidelines to ensure randomization is done properly in CRTs in LMICs. More research is also needed to understand how public randomization ceremonies are used in practice. [Display omitted] •Robust randomization methods are essential for cluster randomized trials (CRTs).•Improved adherence to reporting and best practices for randomization in CRTs is needed.•Public randomization ceremonies may help with implementation challenges.•Further research on the conduct of public randomization ceremonies is warranted.

Registry-based randomized controlled trials are defined as pragmatic trials that use registries as a platform for case records, data collection, randomization, and follow-up. Recently, the application of registry-based randomized controlled trials has attracted increasing attention in health research to address comparative effectiveness research questions in real-world settings, mainly due to their low cost, enhanced generalizability of findings, rapid consecutive enrollment, and the potential completeness of follow-up for the reference population, when compared with conventional randomized effectiveness trials. However several challenges of registry-based randomized controlled trials have to be taken into consideration, including registry data quality, ethical issues, and methodological challenges. In this article, we summarize the advantages, challenges, and areas for future research related to registry-based randomized controlled trials.

BackgroundSubstantial amounts of public funds are invested in health research worldwide. Publicly funded randomised controlled trials (RCTs) often recruit participants at a slower than anticipated rate. Many trials fail to reach their planned sample size within the envisaged trial timescale and trial funding envelope.ObjectivesTo review the consent, recruitment and retention rates for single and multicentre randomised control trials funded and published by the UK's National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme.Data sources and study selectionHTA reports of individually randomised single or multicentre RCTs published from the start of 2004 to the end of April 2016 were reviewed.Data extractionInformation was extracted, relating to the trial characteristics, sample size, recruitment and retention by two independent reviewers.Main outcome measuresTarget sample size and whether it was achieved; recruitment rates (number of participants recruited per centre per month) and retention rates (randomised participants retained and assessed with valid primary outcome data).ResultsThis review identified 151 individually RCTs from 787 NIHR HTA reports. The final recruitment target sample size was achieved in 56% (85/151) of the RCTs and more than 80% of the final target sample size was achieved for 79% of the RCTs (119/151). The median recruitment rate (participants per centre per month) was found to be 0.92 (IQR 0.43–2.79) and the median retention rate (proportion of participants with valid primary outcome data at follow-up) was estimated at 89% (IQR 79–97%).ConclusionsThere is considerable variation in the consent, recruitment and retention rates in publicly funded RCTs. Investigators should bear this in mind at the planning stage of their study and not be overly optimistic about their recruitment projections.

A systematic review and meta-analysis were utilised to explore the methodological quality, program fidelity, and efficacy of social skills group programs (SSGPs) aiming to support autistic adolescents in navigating their everyday social worlds. The study evaluated the methodological quality and theoretical fidelity of studies, with a random effect meta-analysis conducted to summarise the overall efficacy of SSGP and its effect on social communication and interaction, behavioural/emotional challenges, adaptive functioning, and autism characteristics. Although findings from the 18 identified studies indicated an adjusted medium overall effect with these programs successfully supporting autistic adolescents’ socialisation needs (g = 0. 60, p  < 0.001), most studies demonstrated medium to low program fidelity despite their good methodological quality. Given the significant heterogeneity of SSGPs and variations in the design and measurement frameworks of efficacy studies, understanding the generalisability of the findings of this research is unclear.

Background In individually randomised trials we might expect interventions delivered in groups or by care providers to result in clustering of outcomes for participants treated in the same group or by the same care provider. In partially nested randomised controlled trials (pnRCTs) this clustering only occurs in one trial arm, commonly the intervention arm. It is important to measure and account for between-cluster variability in trial design and analysis. We compare analysis approaches for pnRCTs with continuous outcomes, investigating the impact on statistical inference of cluster sizes, coding of the non-clustered arm, intracluster correlation coefficient (ICCs), and differential variance between intervention and control arm, and provide recommendations for analysis. Methods We performed a simulation study assessing the performance of six analysis approaches for a two-arm pnRCT with a continuous outcome. These include: linear regression model; fully clustered mixed-effects model with singleton clusters in control arm; fully clustered mixed-effects model with one large cluster in control arm; fully clustered mixed-effects model with pseudo clusters in control arm; partially nested homoscedastic mixed effects model, and partially nested heteroscedastic mixed effects model. We varied the cluster size, number of clusters, ICC, and individual variance between the two trial arms. Results All models provided unbiased intervention effect estimates. In the partially nested mixed-effects models, methods for classifying the non-clustered control arm had negligible impact. Failure to account for even small ICCs resulted in inflated Type I error rates and over-coverage of confidence intervals. Fully clustered mixed effects models provided poor control of the Type I error rates and biased ICC estimates. The heteroscedastic partially nested mixed-effects model maintained relatively good control of Type I error rates, unbiased ICC estimation, and did not noticeably reduce power even with homoscedastic individual variances across arms. Conclusions In general, we recommend the use of a heteroscedastic partially nested mixed-effects model, which models the clustering in only one arm, for continuous outcomes similar to those generated under the scenarios of our simulations study. However, with few clusters (3–6), small cluster sizes (5–10), and small ICC (≤0.05) this model underestimates Type I error rates and there is no optimal model.

  [...]we organised a CONSORT Group meeting to update the 2001 statement [6],[7],[8]. Transparent reporting reveals deficiencies in research if they exist. [...]investigators who conduct inadequate trials, but who must transparently report, should not be able to pass through the publication process without revelation of their trial's inadequacies.

Randomised controlled trials often use surrogate endpoints to substitute for a target outcome (an outcome of direct interest and relevance to trial participants, clinicians, and other stakeholders—eg, all cause mortality) to improve efficiency (through shortened duration of follow-up, reduced sample size, and lower research costs), and for ethical or practical reasons. However, their use has a fundamental limitation in terms of uncertainty of the intervention effect on the target outcome and limited information on potential intervention harms. There have been increasing calls for improved reporting of trial protocols that use surrogate endpoints. This report presents the SPIRIT-Surrogate, an extension of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklist, a consensus driven reporting guideline designed for trial protocols using surrogate endpoints as the primary outcome(s). The SPIRIT-Surrogate extension includes nine items modified from the SPIRIT 2013 checklist. The guideline provides examples and explanations for each item. We recommend that all stakeholders (including trial investigators and sponsors, research ethics reviewers, funders, journal editors, and peer reviewers) use this extension in reporting trial protocols that use surrogate endpoints. Its use will allow for improved design of such trials, improved transparency, and interpretation of findings when trials are completed, and ultimately reduced research waste.