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In a cluster-randomized trial (CRT), the number of participants enrolled often varies across clusters. This variation should be considered during both trial design and data analysis to ensure statistical performance goals are achieved. Most methodological literature on the CRT design has assumed equal cluster sizes. This scoping review focuses on methodology for unequal cluster size CRTs. EMBASE, Medline, Google Scholar, MathSciNet and Web of Science databases were searched to identify English-language articles reporting on methodology for unequal cluster size CRTs published until March 2021. We extracted data on the focus of the paper (power calculation, Type I error etc.), the type of CRT, the type and the range of parameter values investigated (number of clusters, mean cluster size, cluster size coefficient of variation, intra-cluster correlation coefficient, etc.), and the main conclusions. Seventy-nine of 5032 identified papers met the inclusion criteria. Papers primarily focused on the parallel-arm CRT (p-CRT, n = 60, 76%) and the stepped-wedge CRT (n = 14, 18%). Roughly 75% of the papers addressed trial design issues (sample size/power calculation) while 25% focused on analysis considerations (Type I error, bias, etc.). The ranges of parameter values explored varied substantially across different studies. Methods for accounting for unequal cluster sizes in the p-CRT have been investigated extensively for Gaussian and binary outcomes. Synthesizing the findings of these works is difficult as the magnitude of impact of the unequal cluster sizes varies substantially across the combinations and ranges of input parameters. Limited investigations have been done for other combinations of a CRT design by outcome type, particularly methodology involving binary outcomes—the most commonly used type of primary outcome in trials. The paucity of methodological papers outside of the p-CRT with Gaussian or binary outcomes highlights the need for further methodological development to fill the gaps.

BackgroundInjury prevention interventions are often implemented at the group level via communities, hospitals, schools, etc, making cluster randomisation a suitable approach to evaluation. The stepped-wedge cluster randomised trial (SW-CRT) design has become increasingly popular for evaluating interventions in real-world settings.MethodIn this commentary, we describe the methodological characteristics of the SW-CRT design and highlight key threats to validity, relevant design and analytical issues, and scenarios in which the SW-CRT design might be a reasonable design choice. We illustrate these key points using a recently completed SW-CRT: the prehospital Canadian C-Spine trial.ResultsSeven potential biases associated with SW-CRTs, including: (1) secular trends, (2) confounding by external factors, (3) identification and recruitment bias, (4) contamination, (5) late and early transitioning, (6) risks of baseline imbalances due to small numbers of clusters and (7) statistical issues are discussed, along with potential mitigation strategies.ConclusionThe SW-CRT design offers a pragmatic approach to evaluating injury prevention interventions that may involve a staggered rollout across services or regions. The design allows an intervention to be rolled out to all participating sites and provides an opportunity to efficiently evaluate effectiveness. It is important, however, for researchers to consider the unique design and analytic issues associated with the SW-CRT design. Mitigating potential threats to validity when using the SW-CRT design helps ensure robust evaluation of injury prevention interventions.

Background The effectiveness of malaria vector control interventions is often evaluated using cluster randomized trials (CRT) with outcomes assessed using repeated cross-sectional surveys. A key requirement for appropriate design and analysis of longitudinal CRTs is accounting for the intra-cluster correlation coefficient (ICC). In addition to exchangeable correlation (constant ICC over time), correlation structures proposed for longitudinal CRT are block exchangeable (allows a different within- and between-period ICC) and exponential decay (allows between-period ICC to decay exponentially). More flexible correlation structures are available in statistical software packages and, although not formally proposed for longitudinal CRTs, may offer some advantages. Our objectives were to empirically explore the impact of these correlation structures on treatment effect inferences, identify gaps in the methodological literature, and make practical recommendations. Methods We obtained data from a parallel-arm CRT conducted in Tanzania to compare four different types of insecticide-treated bed-nets. Malaria prevalence was assessed in cross-sectional surveys of 45 households in each of 84 villages at baseline, 12-, 18- and 24-months post-randomization. We re-analyzed the data using mixed-effects logistic regression according to a prespecified analysis plan but under five different correlation structures as well as a robust variance estimator under exchangeable correlation and compared the estimated correlations and treatment effects. A proof-of-concept simulation was conducted to explore general conclusions. Results The estimated correlation structures varied substantially across different models. The unstructured model was the best-fitting model based on information criteria. Although point estimates and confidence intervals for the treatment effect were similar, allowing for more flexible correlation structures led to different conclusions based on statistical significance. Use of robust variance estimators generally led to wider confidence intervals. Simulation results showed that under-specification can lead to coverage probabilities much lower than nominal levels, but over-specification is more likely to maintain nominal coverage. Conclusion More flexible correlation structures should not be ruled out in longitudinal CRTs. This may be particularly important in malaria trials where outcomes may fluctuate over time. In the absence of robust methods for selecting the best-fitting correlation structure, researchers should examine sensitivity of results to different assumptions about the ICC and consider robust variance estimators.

Background Utilizing Bayesian methods in clinical trials has become increasingly popular, as they can incorporate prior information into the design, and allow for smaller sample sizes while providing reliable and robust statistical results. Various Bayesian methods for sample size determination are available, and while these methods are well justified and understood, it is unclear how they are being used in practice. This study aims to understand how sample sizes for Bayesian efficacy randomized clinical trials (RCTs) are determined and inform future designs of Bayesian trials. Methods A systematic literature review was conducted in May 2023 and updated in July 2025. We included completed RCTs which (a) assessed the efficacy of interventions in humans; (b) utilized a Bayesian framework for the primary data analysis; (c) published in English; and (d) enrolled participants between December 2009 – July 2025. Results The literature search produced 74,833 records, of which 27,890 were duplicates, and 46,943 were screened using manual and automated screening. 283 full texts were screened and 164 studies moved to extraction. Our findings demonstrate a slow increase in RCTs using Bayesian methods to analyse primary efficacy data from 2012 onwards, with a sharp increase during the COVID-19 pandemic (42%). The most common method for sample size determination in Bayesian RCTs was a hybrid approach (58%) in which elements of Bayesian and frequentist theory are combined. Bayesian RCTs predominantly took place in North America (34%) and mainly focused on adult study populations (85%). Bayesian trials were used in a variety of disease areas; the most common being COVID-19 (31%). Conclusion Fully Bayesian methods for sample size determination are rarely used in practice, despite significant theoretical development. Our review revealed a lack of standardized reporting across Bayesian RCTs, making it challenging to review the sample size determination. The CONSORT statement indicates that RCTs must report sample size calculations; adhered to by only 84% of included RCTs. Among RCTs that reported sample size determination, relevant information was frequently omitted from reports and discussed in poorly structured supplementary materials. Thus, there is a critical need for greater transparency, standardization and translation of relevant methodology in Bayesian RCTs.

Background Cluster randomized trials (CRTs) are increasingly important for evaluating interventions embedded in health care systems. An essential parameter in sample size calculation to detect both overall and heterogeneous treatment effects for CRTs is the intra-cluster correlation coefficient (ICC) of both outcome and covariates of interest. However, obtaining advance estimates for the ICC can be challenging. When trial outcomes will be obtained from routinely collected data sources, there is an opportunity to obtain reliable ICC estimates in advance of the trial. Using USA national Medicare data, we estimated ICCs for a range of outcomes to inform the design of CRTs for people living with Alzheimer’s and related dementias (ADRD). Method Data from 2018 Medicare Fee-for-Service beneficiaries, specifically, 1,898,812 individuals (≥ 65 years) with diagnosis of ADRD within 3436 hospital service areas (treated as clusters) and 306 hospital referral regions (treated as fixed strata), were used to calculate unadjusted and adjusted ICC estimates for three outcomes: death, any hospitalizations, and any emergency department (ED) visits and three covariates: age, race and sex. We present both overall and stratum-specific ICC estimates. We illustrate their use in sample size calculations for overall treatment effects as well as detecting treatment effect heterogeneity. Results The unadjusted overall ICCs for death, hospitalizations, and ED visits were 0.001, 0.010, and 0.017 respectively. Stratum-specific ICCs varied widely across the 306 HRRs: median 0.001, 0.010 and 0.025 for death, hospitalizations, and ED visits respectively and 0.007, 0.001, and 0.080 for age, sex and race. An interactive R Shiny app is provided that allows users to retrieve estimates overlayed on a map of the USA. Conclusions We presented both adjusted and unadjusted ICCs for outcomes as well as unadjusted ICCs for covariates of potential interest from population-level data in the USA and demonstrated how the estimates may be used in sample size calculations for CRTs in ADRD.

Background In a cross-sectional stepped-wedge trial with unequal cluster sizes, attained power in the trial depends on the realized allocation of the clusters. This attained power may differ from the expected power calculated using standard formulae by averaging the attained powers over all allocations the randomization algorithm can generate. We investigated the effect of design factors and allocation characteristics on attained power and developed models to predict attained power based on allocation characteristics. Method Based on data simulated and analyzed using linear mixed-effects models, we evaluated the distribution of attained powers under different scenarios with varying intraclass correlation coefficient (ICC) of the responses, coefficient of variation (CV) of the cluster sizes, number of cluster-size groups, distributions of group sizes, and number of clusters. We explored the relationship between attained power and two allocation characteristics: the individual-level correlation between treatment status and time period, and the absolute treatment group imbalance. When computational time was excessive due to a scenario having a large number of possible allocations, we developed regression models to predict attained power using the treatment-vs-time period correlation and absolute treatment group imbalance as predictors. Results The risk of attained power falling more than 5% below the expected or nominal power decreased as the ICC or number of clusters increased and as the CV decreased. Attained power was strongly affected by the treatment-vs-time period correlation. The absolute treatment group imbalance had much less impact on attained power. The attained power for any allocation was predicted accurately using a logistic regression model with the treatment-vs-time period correlation and the absolute treatment group imbalance as predictors. Conclusion In a stepped-wedge trial with unequal cluster sizes, the risk that randomization yields an allocation with inadequate attained power depends on the ICC, the CV of the cluster sizes, and number of clusters. To reduce the computational burden of simulating attained power for allocations, the attained power can be predicted via regression modeling. Trial designers can reduce the risk of low attained power by restricting the randomization algorithm to avoid allocations with large treatment-vs-time period correlations.

Background Adaptive clinical trials increasingly aim to detect heterogeneity of treatment effect (HTE) to guide personalized care. However, most adaptive designs rely on predefined subgroups and are limited in their ability to uncover unknown or complex sources of HTE. Bayesian statistical methods offer a flexible alternative, enabling real-time learning and adaptation within trials. This review evaluates Bayesian methods used to detect hidden HTE in adaptive clinical trials, with attention to their methodological innovations, operating characteristics, and consideration of equity and inclusion in trial design. Methods We conducted a systematic search of MEDLINE, Embase, and other databases to identify original studies that developed Bayesian methods for detecting unknown HTE within adaptive clinical trial designs. Eligible studies were reviewed and synthesized based on design features, statistical methodology, operating characteristics, reproducibility, and whether equity-related factors were explicitly considered. Equity considerations included whether studies incorporated variables related to underrepresented populations—such as age, sex, race/ethnicity, or geography—examined intersectional subgroup effects, or explicitly framed their methods as tools to address health disparities. Results Of 2826 screened records, seven studies met inclusion criteria. Bayesian methods included random partition models, spatial models, logistic regression with dimension reduction, adaptive randomization using machine learning classifiers, and adaptive enrichment or platform designs incorporating model averaging or latent subgroup estimation. In simulation studies, these methods often showed improvements in subgroup detection, efficiency, or power relative to non-Bayesian comparators. None were tested using real-world trial data. Reproducibility was limited overall, with analytic code only available for the three most recent studies. Notably, none explicitly framed their methods as tools to address inequities in treatment outcomes across population subgroups. Conclusions The small number of simulation-based studies illustrates preliminary but promising directions for applying Bayesian methods to detect HTE in adaptive clinical trials. While these approaches demonstrate potential to enhance trial adaptability, scalability, and inclusiveness, current evidence remains limited and largely conceptual. Incorporating an equity lens into future methodological development, alongside greater emphasis on empirical validation and open science practices, will be essential to determine their practical value in advancing equitable clinical research.

ObjectivesTo describe the extent to which pragmatic trials underachieved or overachieved their target sample sizes, examine explanations and identify characteristics associated with under-recruitment and over-recruitment.Study design and settingSecondary analysis of an existing database of primary trial reports published during 2014–2019, registered in ClinicalTrials.gov, self-labelled as pragmatic and with target and achieved sample sizes available.ResultsOf 372 eligible trials, the prevalence of under-recruitment (achieving <90% of target sample size) was 71 (19.1%) and of over-recruitment (>110% of target) was 87 (23.4%). Under-recruiting trials commonly acknowledged that they did not achieve their targets (51, 71.8%), with the majority providing an explanation, but only 11 (12.6%) over-recruiting trials acknowledged recruitment excess. The prevalence of under-recruitment in individually randomised versus cluster randomised trials was 41 (17.0%) and 30 (22.9%), respectively; prevalence of over-recruitment was 39 (16.2%) vs 48 (36.7%), respectively. Overall, 101 025 participants were recruited to trials that did not achieve at least 90% of their target sample size. When considering trials with over-recruitment, the total number of participants recruited in excess of the target was a median (Q1–Q3) 319 (75–1478) per trial for an overall total of 555 309 more participants than targeted. In multinomial logistic regression, cluster randomisation and lower journal impact factor were significantly associated with both under-recruitment and over-recruitment, while using exclusively routinely collected data and educational/behavioural interventions were significantly associated with over-recruitment; we were unable to detect significant associations with obtaining consent, publication year, country of recruitment or public engagement.ConclusionsA clear explanation for under-recruitment or over-recruitment in pragmatic trials should be provided to encourage transparency in research, and to inform recruitment to future trials with comparable designs. The issues and ethical implications of over-recruitment should be more widely recognised by trialists, particularly when designing cluster randomised trials.

Background Insertion of a central venous access device (CVAD) allows clinicians to easily access the circulation of a patient to administer life-saving interventions. Due to their invasive nature, CVADs are prone to complications such as bacterial biofilm production and colonization, catheter-related bloodstream infection, occlusion, and catheter-related venous thrombosis. A CVAD is among the most common interventions for patients in the intensive care unit (ICU), exposing this vulnerable population to the risk of nosocomial infection and catheter occlusion. The current standard of care involves the use of normal saline as a catheter locking solution for central venous catheters (CVCs) and peripherally inserted central catheter (PICC) lines, and a citrate lock for hemodialysis catheters. Saline offers little prophylactic measures against catheter complications. Four percent of tetrasodium ethylenediaminetetraacetic acid (EDTA) fluid (marketed as KiteLock Sterile Locking Solution™) is non-antibiotic, possesses antimicrobial, anti-biofilm, and anti-coagulant properties, and is approved by Health Canada as a catheter locking solution. As such, it may be a superior CVAD locking solution than the present standard of care lock in the ICU patient population. Methods Our team proposes to fill this knowledge gap by performing a multi-center, cluster-randomized, crossover trial evaluating the impact of 4% tetrasodium EDTA on a primary composite outcome of the incidence rate of central line-associated bloodstream infection (CLABSI), catheter occlusion leading to removal, and use of alteplase to resolve catheter occlusion compared to the standard of care. The study will be performed at five critical care units. Discussion If successful, the results of this study can serve as evidence for a shift of standard of care practices to include EDTA locking fluid in routine CVAD locking procedures. Completion of this study has the potential to improve CVAD standard of care to become safer for patients, as well as provides an opportunity to decrease strain on healthcare budgets related to treating preventable CVAD complications. Success and subsequent implementation of this intervention in the ICU may also be extrapolated to other patient populations with heavy CVAD use including hemodialysis, oncology, parenteral nutrition, and pediatric patient populations. On a global scale, eradicating biofilm produced by antibiotic-resistant bacteria may serve to lessen the threat of “superbugs” and contribute to international initiatives supporting the termination of antibiotic overuse. Trial registration ClinicalTrials.gov NCT04548713, registered on September 9th, 2020. 

Adults ≥ 65 are at risk of cervical spine (C-spine) injury, even after low-level falls. The objectives of this systematic review were to determine the prevalence of C-spine injury in this population and explore the association of unreliable clinical exam with C-spine injury. We conducted this systematic review according to PRISMA guidelines. We searched MEDLINE, PubMed, EMBASE, Scopus, Web of Science, and the Cochrane Database of Systematic reviews to include studies reporting on C-spine injury in adults ≥ 65 years after low-level falls. Two reviewers independently screened articles, abstracted data, and assessed bias. Discrepancies were resolved by a third reviewer. A meta-analysis was performed to estimate overall prevalence and the pooled odds ratio for the association between C-spine injury and an unreliable clinical exam. The search identified 2044citations, 138 full texts were screened, and 21 studies were included in the systematic review. C-spine injury prevalence in adults ≥ 65 years after low-level falls was 3.8% (95% CI: 2.8–5.3). The odds of c-spine injury in those with altered level of consciousness (aLOC) v/s not aLOC was 1.21 (0.90–1.63) and in those with GCS < 15 v/s GCS 15 was 1.62 (0.37–6.98). Studies were at a low-risk of bias, although some had low recruitment and significant loss to follow-up. Adults ≥ 65 years are at risk of cervical spine injury after low-level falls. More research is needed to determine whether there is an association between cervical spine injury and GCS < 15 or altered level of consciousness.