Recruitment and retention of participants in randomised controlled trials: a review of trials funded by the United Kingdom health technology assessment programme

Background Substantial 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. Objectives To 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 selection HTA reports of individually randomised single or multicentre RCTs published from the start of 2004 to the end of April 2016 were reviewed. Data extraction Information was extracted, relating to the trial characteristics, sample size, recruitment and retention by two independent reviewers. Main outcome measures Target 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). Results This 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%). Conclusions There 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

Recruitment and retention of participants in randomised controlled trials: a review of trials funded and published by the United Kingdom Health Technology Assessment Programme

Background Substantial 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. Objectives To 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 selection HTA reports of individually randomised single or multicentre RCTs published from the start of 2004 to the end of April 2016 were reviewed. Data extraction Information was extracted, relating to the trial characteristics, sample size, recruitment and retention by two independent reviewers. Main outcome measures Target 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). Results This 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%). Conclusions There 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

Decay of neutron-rich Mn nuclides and deformation of heavy Fe isotopes

The use of chemically selective laser ionization combined with beta-delayed neutron counting at CERN/ISOLDE has permitted identification and half-life measurements for 623-ms Mn-61 up through 14-ms Mn-69. The measured half-lives are found to be significantly longer near N=40 than the values calculated with a QRPA shell model using ground-state deformations from the FRDM and ETFSI models. Gamma-ray singles and coincidence spectroscopy has been performed for Mn-64 and Mn-66 decays to levels of Fe-64 and Fe-66, revealing a significant drop in the energy of the first 2+ state in these nuclides that suggests an unanticipated increase in collectivity near N=40

Statistical analysis of publicly funded cluster randomised controlled trials : a review of the National Institute for Health Research Journals Library

Background In cluster randomised controlled trials (cRCTs), groups of individuals (rather than individuals) are randomised to minimise the risk of contamination and/or efficiently use limited resources or solve logistic and administrative problems. A major concern in the primary analysis of cRCT is the use of appropriate statistical methods to account for correlation among outcomes from a particular group/cluster. This review aimed to investigate the statistical methods used in practice for analysing the primary outcomes in publicly funded cluster randomised controlled trials, adherence to the CONSORT (Consolidated Standards of Reporting Trials) reporting guidelines for cRCTs and the recruitment abilities of the cluster trials design. Methods We manually searched the United Kingdom’s National Institute for Health Research (NIHR) online Journals Library, from 1 January 1997 to 15 July 2021 chronologically for reports of cRCTs. Information on the statistical methods used in the primary analyses was extracted. One reviewer conducted the search and extraction while the two other independent reviewers supervised and validated 25% of the total trials reviewed. Results A total of 1942 reports, published online in the NIHR Journals Library were screened for eligibility, 118 reports of cRCTs met the initial inclusion criteria, of these 79 reports containing the results of 86 trials with 100 primary outcomes analysed were finally included. Two primary outcomes were analysed at the cluster-level using a generalized linear model. At the individual-level, the generalized linear mixed model was the most used statistical method (80%, 80/100), followed by regression with robust standard errors (7%) then generalized estimating equations (6%). Ninety-five percent (95/100) of the primary outcomes in the trials were analysed with appropriate statistical methods that accounted for clustering while 5% were not. The mean observed intracluster correlation coefficient (ICC) was 0.06 (SD, 0.12; range, − 0.02 to 0.63), and the median value was 0.02 (IQR, 0.001–0.060), although 42% of the observed ICCs for the analysed primary outcomes were not reported. Conclusions In practice, most of the publicly funded cluster trials adjusted for clustering using appropriate statistical method(s), with most of the primary analyses done at the individual level using generalized linear mixed models. However, the inadequate analysis and poor reporting of cluster trials published in the UK is still happening in recent times, despite the availability of the CONSORT reporting guidelines for cluster trials published over a decade ago

Occurrence and Suspected Function of Prematernity Colonies of Eastern Pipistrelles, Perimyotis sub avus, in Indiana

During summer, some pregnant female Eastern Pipistrelles form colonies in buildings but most typically roost in clusters of live or dead leaves in trees. We provide evidence that some that ultimately roost in leaf clusters form temporary colonies in or on buildings in early spring, prior to moving to the leaf clusters where they give birth. We call these prematernity colonies, and define them as those formed for a short time following hibernation and before the bats move to their maternity roosts. Prematernity colonies form from late April to early May and individuals relocate to leaf clusters from late May to early June. The bats showed strong fidelity to prematernity roosts, returning annually. Time of occupancy during any one year averaged 26 days. Nine bats were radio-tracked during the transition from building to tree roosts and were found to use several different trees. Bats showed interannual fidelity to building roosts. Buildings may help colonies re-form after individuals migrate from their hibernacula. Also, they could provide a warmer or more stable microclimate for pregnant females

How is missing data handled in cluster randomized controlled trials? A review of trials published in the NIHR Journals Library 1997–2024

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

Analysing cluster randomised controlled trials using GLMM, GEE1, GEE2, and QIF: results from four case studies

Background Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials. Methods Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages. Results The intracluster correlation coefficient (ICC) estimates for the case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters. Conclusion This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches

The relationships between regional Quaternary uplift, deformation across active normal faults and historical seismicity in the upper plate of subduction zones: The Capo D’Orlando Fault, NE Sicily

In order to investigate deformation within the upper plate of the Calabrian subduction zone we have mapped and modelled a sequence of Late Quaternary palaeoshorelines tectonically-deformed by the Capo D’Orlando normal fault, NE Sicily, which forms part of the actively deforming Calabrian Arc. In addition to the 1908 Messina Strait earthquake (Mw 7.1), this region has experienced damaging earthquakes, possibly on the Capo D’Orlando Fault, however, it is not considered by some to be a potential seismogenic source. Uplifted Quaternary palaeoshorelines are preserved on the hangingwall of the Capo D’Orlando Fault, indicating that hangingwall subsidence is counteracted by regional uplift, likely because of deformation associated with subduction/collision. We attempt to constrain the relationship between regional uplift, crustal extensional processes and historical seismicity, and we quantify both the normal and regional deformation signals. We report uplift variations along the strike of the fault and use a synchronous correlation technique to assign ages to palaeoshorelines, facilitating calculation of uplift rates and the fault throw-rate. Uplift rates in the hangingwall increase from 0.4 mm/yr in the centre of the fault to 0.89 mm/yr beyond its SW fault tip, suggesting 0.5 mm/yr of fault related subsidence, which implies a throw-rate of 0.63 ± 0.02 mm/yr, and significant seismic hazard. Overall, we emphasise that upper plate extension and related vertical motions complicate the process of deriving information on the subduction/collision process, such as coupling and slip distribution on the subduction interface, parameters that are commonly inferred for other subduction zones without considering upper plate deformation

Comparison of statistical methods for the analysis of patient-reported outcomes in randomised controlled trials: a simulation study

Patient-reported outcomes (PROs) that aim to measure patients’ subjective attitudes towards their health or health-related conditions in various fields have been increasingly used in randomised controlled trials (RCTs). PRO data is likely to be bounded, discrete, and skewed. Although various statistical methods are available for the analysis of PROs in RCT settings, there is no consensus on what statistical methods are the most appropriate for use. This study aims to use simulation methods to compare the performance (in terms of bias, empirical standard error, coverage of the confidence interval, Type I error, and power) of three different statistical methods, multiple linear regression (MLR), Tobit regression (Tobit), and median regression (Median), to estimate a range of predefined treatment effects for a PRO in a two-arm balanced RCT. We assumed there was an underlying latent continuous outcome that the PRO was measuring, but the actual scores observed were equally spaced and discrete. This study found that MLR was associated with little bias of the estimated treatment effect, small standard errors, and appropriate coverage of the confidence interval under most scenarios. Tobit performed worse than MLR for analysing PROs with a small number of levels, but it had better performance when analysing PROs with more discrete values. Median showed extremely large bias and errors, associated with low power and coverage for most scenarios especially when the number of possible discrete values was small. We recommend MLR as a simple and universal statistical method for the analysis of PROs in RCT settings