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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.

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.

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 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.

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.

  [...]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.

Even today, patients with schizophrenia often have an unfavorable outcome. Negative symptoms and cognitive deficits are common features in many patients and prevent recovery. In recent years, aerobic endurance training has emerged as a therapeutic approach with positive effects on several domains of patients’ health. However, appropriately sized, multicenter randomized controlled trials that would allow better generalization of results are lacking. The exercise study presented here is a multicenter, rater-blind, two-armed, parallel-group randomized clinical trial in patients with clinically stable schizophrenia being conducted at five German tertiary hospitals. The intervention group performs aerobic endurance training on bicycle ergometers three times per week for 40–50 min/session (depending on the intervention week) for a total of 26 weeks, and the control group performs balance and tone training for the same amount of time. Participants are subsequently followed up for 26 weeks. The primary endpoint is all-cause discontinuation; secondary endpoints include psychopathology, cognition, daily functioning, cardiovascular risk factors, and explorative biological measures regarding the underlying mechanisms of exercise. A total of 180 patients will be randomized. With currently 162 randomized participants, our study is the largest trial to date to investigate endurance training in patients with schizophrenia. We hypothesize that aerobic endurance training has beneficial effects on patients’ mental and physical health, leading to lower treatment discontinuation rates and improving disease outcomes. The study results will provide a basis for recommending exercise interventions as an add-on therapy in patients with schizophrenia.The study is registered in the International Clinical Trials Database (ClinicalTrials.gov identifier [NCT number]: NCT03466112) and in the German Clinical Trials Register (DRKS-ID: DRKS00009804).

Common Sense Oncology (CSO) prioritises treatments providing meaningful benefits for people with cancer. Here, we describe CSO principles aimed at improving the design, analysis, and reporting of randomised, controlled, phase 3 clinical trials evaluating cancer treatments. These principles include: (1) control treatment should be the best current standard of care; (2) the preferred primary endpoint is overall survival or a validated surrogate; (3) an absolute measure of benefit should be provided, such as the difference between groups in median overall survival times or the proportion of surviving patients at a prespecified time; (4) health-related quality of life should be at least a secondary endpoint; (5) toxicity should be described objectively without subjective language diminishing its importance; (6) trials should be designed to show or rule out clinically meaningful differences in outcomes, rather than a statistically significant difference alone; (7) censoring should be detailed, and sensitivity analyses done to determine its possible effects; (8) experimental treatments known to improve overall survival at later disease stages should be offered and funded for patients progressing in the control group; and (9) reports of trials should include a lay-language summary. We include checklists to guide compliance with these principles. By encouraging adherence, CSO aims to ensure that clinical trials yield results that are scientifically robust and meaningful to patients.

Purpose In the context of clinical research, there is an increasing need for new study designs that help to incorporate already available data. With the help of historical controls, the existing information can be utilized to support the new study design, but of course, inclusion also carries the risk of bias in the study results. Methods To combine historical and randomized controls we investigate the Fill-it-up-design, which in the first step checks the comparability of the historical and randomized controls performing an equivalence pre-test. If equivalence is confirmed, the historical control data will be included in the new RCT. If equivalence cannot be confirmed, the historical controls will not be considered at all and the randomization of the original study will be extended. We are investigating the performance of this study design in terms of type I error rate and power. Results We demonstrate how many patients need to be recruited in each of the two steps in the Fill-it-up-design and show that the family wise error rate of the design is kept at 5 % . The maximum sample size of the Fill-it-up-design is larger than that of the single-stage design without historical controls and increases as the heterogeneity between the historical controls and the concurrent controls increases. Conclusion The two-stage Fill-it-up-design represents a frequentist method for including historical control data for various study designs. As the maximum sample size of the design is larger, a robust prior belief is essential for its use. The design should therefore be seen as a way out in exceptional situations where a hybrid design is considered necessary.