Search

Abstract Background Randomised controlled trials (RCTs) are considered the gold standard for generating clinical evidence. The focus on high internal validity in RCTs challenges the external validity and generalisability of findings, potentially hindering their application in routine care. In neurorehabilitation, limited literature addresses conducting RCTs feasibly and efficiently. We investigated barriers and facilitators to conducting RCTs within routine care of neurorehabilitation centres from the perspective of stakeholders in neurorehabilitation in Germany and Austria. Methods We conducted semi-structured interviews with stakeholders in neurorehabilitation from four centres in Germany and Austria, informed by the Theoretical Domains Framework (TDF) and the Capability, Opportunity, Motivation and Behaviour model (COM-B). Employing a hybrid approach, the interview analysis integrated both deductive, theory-driven analysis based on the TDF domains and COM-B model and inductive, reflexive thematic analysis. Results Twelve stakeholders (4 physicians, 4 therapy managers, 4 therapists; 5 females, 7 males; with research experience spanning 0–40 years) were interviewed. Key barriers to conducting RCTs in neurological rehabilitation centres include limited financial, human, and time resources, high clinical workloads, and a lack of interest of some therapists. Ineffective leadership, perceived lack of research expertise, and communication issues were also significant barriers. Social influence factors such as lack of employer support and inadequate training access further contributed to the challenges. Additionally, barriers included insufficient research infrastructure, limited space, internal power struggles, and rigid cost bearer specifications. Key facilitators included physicians’ and therapists’ motivation to advance the field, contribute to knowledge, and to prioritise patient health. Support from supervisors, joint decision-making, and efficient organisation were crucial facilitators. Flexible therapy planning, mutual support, and interdisciplinary collaboration also played important roles. Conclusion Our results suggest that increasing professional development and understanding, along with providing adequate financial, human, time, and spatial resources to support research endeavours, implementing effective communication strategies to enhance interdisciplinary collaboration and coordination among team members may contribute to increased motivation and facilitate RCTs within the setting of neurorehabilitation centres. Trial registration This study was prospectively registered with the German Clinical Trials Register (08.04.2021 DRKSID DRKS00024982).

Abstract Background Experimental studies of wound healing often use survival analysis and time to event outcomes or differences in wound area at a specific time point. However, these methods do not use a potentially large number of observations made over the course of a trial and may be inefficient. A model-based approach can leverage all trial data, but there is little guidance on appropriate models and functional forms to describe wound healing. Methods We derive a general statistical model and review a wide range of plausible mathematical models to describe wound healing. We identify a range of possible derived estimands and their derivation from the models. Using data from a trial of an intervention to promote ulcer healing in patients affected by leprosy that included three measurement methods repeated across the course of the study, we compare the goodness-of-fit of the models using a range of methods and estimate treatment effects and healing rate functions with the best-fitting models. Results Overall, we included 5,581 ulcer measurements of 1,578 unique images from 130 patients. We examined the performance of a range of models. The square root, log square root, and log quadratic models were the best fitting models across all outcome measurement methods. The estimated treatment effects magnitude and sign varied by time post-randomisation, model type, and outcome type, but across all models there was little evidence of effectiveness. The estimated effects were significantly more precise than non-parametric alternatives. For example, estimated differences from the three outcome measurements at 42-days post-randomisation were − 0.01 cm2 (-0.77, 0.74), -0.44 cm2 (-1.64, 0.76), and 0.11 cm2 (-0.87, 1.08) using a non-parametric method versus − 0.03 cm2 (-0.14, 0.06), 0.06 cm2 (-0.05, 0.17), and 0.03 cm2 (-0.07, 0.17) using a square-root model. Conclusions Model-based analyses can dramatically improve the precision of estimates but care must be taken to carefully compare and select the best fitting models. The (log) square-root model is strongly recommended reflecting advice from a century ago.

Abstract Background An adaptive design allows modifying the design based on accumulated data while maintaining trial validity and integrity. The final sample size may be unknown when designing an adaptive trial. It is therefore important to consider what sample size is used in the planning of the study and how that is communicated to add transparency to the understanding of the trial design and facilitate robust planning. In this paper, we reviewed trial protocols and grant applications on the sample size reporting for randomised adaptive trials. Method We searched protocols of randomised trials with comparative objectives on ClinicalTrials.gov (01/01/2010 to 31/12/2022). Contemporary eligible grant applications accessed from UK publicly funded researchers were also included. Suitable records of adaptive designs were reviewed, and key information was extracted and descriptively analysed. Results We identified 439 records, and 265 trials were eligible. Of these, 164 (61.9%) and 101 (38.1%) were sponsored by industry and public sectors, respectively, with 169 (63.8%) of all trials using a group sequential design although trial adaptations used were diverse. The maximum and minimum sample sizes were the most reported or directly inferred (n = 199, 75.1%). The sample size assuming no adaptation would be triggered was usually set as the estimated target sample size in the protocol. However, of the 152 completed trials, 15 (9.9%) and 33 (21.7%) had their sample size increased or reduced triggered by trial adaptations, respectively. The sample size calculation process was generally well reported in most cases (n = 216, 81.5%); however, the justification for the sample size calculation parameters was missing in 116 (43.8%) trials. Less than half gave sufficient information on the study design operating characteristics (n = 119, 44.9%). Conclusion Although the reporting of sample sizes varied, the maximum and minimum sample sizes were usually reported. Most of the trials were planned for estimated enrolment assuming no adaptation would be triggered. This is despite the fact a third of reported trials changed their sample size. The sample size calculation was generally well reported, but the justification of sample size calculation parameters and the reporting of the statistical behaviour of the adaptive design could still be improved.

Background: Randomised controlled trials (RCTs) are considered the gold standard for generating clinical evidence. The focus on high internal validity in RCTs challenges the external validity and generalisability of findings, potentially hindering their application in routine care. In neurorehabilitation, limited literature addresses conducting RCTs feasibly and efficiently. We investigated barriers and facilitators to conducting RCTs within routine care of neurorehabilitation centres from the perspective of stakeholders in neurorehabilitation in Germany and Austria. Methods: We conducted semi-structured interviews with stakeholders in neurorehabilitation from four centres in Germany and Austria, informed by the Theoretical Domains Framework (TDF) and the Capability, Opportunity, Motivation and Behaviour model (COM-B). Employing a hybrid approach, the interview analysis integrated both deductive, theory-driven analysis based on the TDF domains and COM-B model and inductive, reflexive thematic analysis. Results: Twelve stakeholders (4 physicians, 4 therapy managers, 4 therapists; 5 females, 7 males; with research experience spanning 0–40 years) were interviewed. Key barriers to conducting RCTs in neurological rehabilitation centres include limited financial, human, and time resources, high clinical workloads, and a lack of interest of some therapists. Ineffective leadership, perceived lack of research expertise, and communication issues were also significant barriers. Social influence factors such as lack of employer support and inadequate training access further contributed to the challenges. Additionally, barriers included insufficient research infrastructure, limited space, internal power struggles, and rigid cost bearer specifications. Key facilitators included physicians' and therapists' motivation to advance the field, contribute to knowledge, and to prioritise patient health. Support from supervisors, joint decision-making, and efficient organisation were crucial facilitators. Flexible therapy planning, mutual support, and interdisciplinary collaboration also played important roles. Conclusion: Our results suggest that increasing professional development and understanding, along with providing adequate financial, human, time, and spatial resources to support research endeavours, implementing effective communication strategies to enhance interdisciplinary collaboration and coordination among team members may contribute to increased motivation and facilitate RCTs within the setting of neurorehabilitation centres. Trial registration: This study was prospectively registered with the German Clinical Trials Register (08.04.2021 DRKSID DRKS00024982). [ABSTRACT FROM AUTHOR]

Background: An adaptive design allows modifying the design based on accumulated data while maintaining trial validity and integrity. The final sample size may be unknown when designing an adaptive trial. It is therefore important to consider what sample size is used in the planning of the study and how that is communicated to add transparency to the understanding of the trial design and facilitate robust planning. In this paper, we reviewed trial protocols and grant applications on the sample size reporting for randomised adaptive trials. Method: We searched protocols of randomised trials with comparative objectives on ClinicalTrials.gov (01/01/2010 to 31/12/2022). Contemporary eligible grant applications accessed from UK publicly funded researchers were also included. Suitable records of adaptive designs were reviewed, and key information was extracted and descriptively analysed. Results: We identified 439 records, and 265 trials were eligible. Of these, 164 (61.9%) and 101 (38.1%) were sponsored by industry and public sectors, respectively, with 169 (63.8%) of all trials using a group sequential design although trial adaptations used were diverse. The maximum and minimum sample sizes were the most reported or directly inferred (n = 199, 75.1%). The sample size assuming no adaptation would be triggered was usually set as the estimated target sample size in the protocol. However, of the 152 completed trials, 15 (9.9%) and 33 (21.7%) had their sample size increased or reduced triggered by trial adaptations, respectively. The sample size calculation process was generally well reported in most cases (n = 216, 81.5%); however, the justification for the sample size calculation parameters was missing in 116 (43.8%) trials. Less than half gave sufficient information on the study design operating characteristics (n = 119, 44.9%). Conclusion: Although the reporting of sample sizes varied, the maximum and minimum sample sizes were usually reported. Most of the trials were planned for estimated enrolment assuming no adaptation would be triggered. This is despite the fact a third of reported trials changed their sample size. The sample size calculation was generally well reported, but the justification of sample size calculation parameters and the reporting of the statistical behaviour of the adaptive design could still be improved. [ABSTRACT FROM AUTHOR]

Background: Experimental studies of wound healing often use survival analysis and time to event outcomes or differences in wound area at a specific time point. However, these methods do not use a potentially large number of observations made over the course of a trial and may be inefficient. A model-based approach can leverage all trial data, but there is little guidance on appropriate models and functional forms to describe wound healing. Methods: We derive a general statistical model and review a wide range of plausible mathematical models to describe wound healing. We identify a range of possible derived estimands and their derivation from the models. Using data from a trial of an intervention to promote ulcer healing in patients affected by leprosy that included three measurement methods repeated across the course of the study, we compare the goodness-of-fit of the models using a range of methods and estimate treatment effects and healing rate functions with the best-fitting models. Results: Overall, we included 5,581 ulcer measurements of 1,578 unique images from 130 patients. We examined the performance of a range of models. The square root, log square root, and log quadratic models were the best fitting models across all outcome measurement methods. The estimated treatment effects magnitude and sign varied by time post-randomisation, model type, and outcome type, but across all models there was little evidence of effectiveness. The estimated effects were significantly more precise than non-parametric alternatives. For example, estimated differences from the three outcome measurements at 42-days post-randomisation were − 0.01 cm2 (-0.77, 0.74), -0.44 cm2 (-1.64, 0.76), and 0.11 cm2 (-0.87, 1.08) using a non-parametric method versus − 0.03 cm2 (-0.14, 0.06), 0.06 cm2 (-0.05, 0.17), and 0.03 cm2 (-0.07, 0.17) using a square-root model. Conclusions: Model-based analyses can dramatically improve the precision of estimates but care must be taken to carefully compare and select the best fitting models. The (log) square-root model is strongly recommended reflecting advice from a century ago. [ABSTRACT FROM AUTHOR]

Background: Adaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation. Methods: Using two large critical care trials (ADRENAL. ClinicalTrials.gov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR. ClinicalTrials.gov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no treatment effect) and alternative scenario (a positive treatment effect). We used a logistic regression model with 90-day mortality as the outcome and the treatment arm as the covariate. The null hypothesis was tested using a two-sided significance level (α) at 0.05. Results: Across all approaches, increasing the number of interims led to a decreased expected sample size. Under the null scenario, group sequential approaches provided good control of the type-I error rate; however, the type I error rate inflation was an issue for the Bayesian approaches. The Bayesian Predictive Probability and O'Brien-Fleming approaches showed the highest probability of correctly stopping the trials (around 95%). Under the alternative scenario, the Bayesian approaches showed the highest overall probability of correctly stopping the ADRENAL trial for efficacy (around 91%), whereas the Haybittle-Peto approach achieved the greatest power for the NICE-SUGAR trial. Treatment effect estimates became increasingly underestimated as the number of interims increased. Conclusions: This study confirms the right adaptive design can reach the same conclusion as a fixed design with a much-reduced sample size. The efficiency gain associated with an increased number of interims is highly relevant to late-phase critical care trials with large sample sizes and short follow-up times. Systematically exploring adaptive methods at the trial design stage will aid the choice of the most appropriate method. [ABSTRACT FROM AUTHOR]

Abstract Background Incomplete and inconsistent reporting of adverse events (AEs) through multiple sources can distort impressions of the overall safety of the medical interventions examined as well as the benefit-risk relationship. We aimed to assess completed allergic rhinitis (AR) trials registered in ClinicalTrials.gov for completeness and consistency of AEs reporting comparing ClinicalTrials.gov and corresponding publications. Methods We retrospectively examined completed randomised controlled trials on AR registered in ClinicalTrials.gov on or after 9/27/2009 to trials updated with results on or before 12/31/2021 along with any corresponding publications. Complete reporting of AEs in ClinicalTrials.gov were summarised in tables describing AE information, and complete reporting in publications was an explicit statement of serious AE, death or other AE. Difference in completeness, number, or description of AEs between ClinicalTrials.gov and publication was classified as inconsistent reporting of AEs. Results There were 99 registered trials with 45 (45.5%) available publications. All published trials completely reported AEs in ClinicalTrials.gov, and 21 (46.7%) in publications (P

Abstract Background Recruitment into clinical trials is challenging and there is a lack of evidence on effective recruitment strategies. Personalisation of invitation letters is a potentially pragmatic and feasible way of increasing recruitment rates at a low-cost. However, there is a lack of evidence concerning the effect of personalising of study invitation letters on recruitment rates. Methods We undertook a Study Within A Trial (SWAT) to investigate the effect of personalised versus non-personalised study invitation letters on recruitment rates into the host feasibility trial ENGAGE, a feasibility study of an internet-administered, guided, Low Intensity Cognitive-Behavioural Therapy based self-help intervention for parents of children previously treated for cancer. An intervention group (n = 254) received a personalised study invitation letter and the control group (n = 255) received a non-personalised study invitation letter. The primary outcome was the proportion of participants in the intervention group and the control group enrolled into the ENGAGE host feasibility trial. Secondary outcomes relating to the recruitment and screening process, and retention were examined. Differences in proportions between groups for the primary and secondary outcomes were estimated using logistic regression. Results Of the 509 potential participants, 56 (11.0%) were enrolled into the ENGAGE host feasibility trial: personalised: 30/254 (11.8%) and non-personalised: 26/255 (10.2%). No statistically significant effect on personalisation of enrolment was found (OR 1.18, 95% CI 0.68–2.06). No statistically significant differences were found for any secondary outcome. Conclusions Personalisation of study invitations had no effect on recruitment. However, given the small study sample size in the present SWAT, and lack of similar embedded recruitment RCTs to enable a meta-analysis, additional SWATs to examine the personalisation of study invitation letters are warranted. Trial registration ISRCTN57233429 ; ISRCTN18404129 ; SWAT 112, Northern Ireland Hub for Trials Methodology Research SWAT repository (2018 OCT 1 1231) ( https://www.qub.ac.uk/sites/TheNorthernIrelandNetworkforTrialsMethodologyResearch/FileStore/Filetoupload,939618,en.pdf ).

Background: Incomplete and inconsistent reporting of adverse events (AEs) through multiple sources can distort impressions of the overall safety of the medical interventions examined as well as the benefit-risk relationship. We aimed to assess completed allergic rhinitis (AR) trials registered in ClinicalTrials.gov for completeness and consistency of AEs reporting comparing ClinicalTrials.gov and corresponding publications.Methods: We retrospectively examined completed randomised controlled trials on AR registered in ClinicalTrials.gov on or after 9/27/2009 to trials updated with results on or before 12/31/2021 along with any corresponding publications. Complete reporting of AEs in ClinicalTrials.gov were summarised in tables describing AE information, and complete reporting in publications was an explicit statement of serious AE, death or other AE. Difference in completeness, number, or description of AEs between ClinicalTrials.gov and publication was classified as inconsistent reporting of AEs.Results: There were 99 registered trials with 45 (45.5%) available publications. All published trials completely reported AEs in ClinicalTrials.gov, and 21 (46.7%) in publications (P < .001). In 43 (95.6%) publications, there was at least one inconsistency in the reporting of AEs (P < .001). 8 (17.8%) publications had different number of serious AEs (P = .003), 36 (80.0%) of other AEs (P < .001) while deaths reporting was inconsistent in 8 (57.1%) publications (P = .127).Conclusion: The reporting of AEs from AR trials is complete in ClinicalTrials.gov and incomplete and inconsistent in corresponding publications. There is a need to improve the reporting of AEs from AR trials in corresponding publications, and thus to improve patient safety. [ABSTRACT FROM AUTHOR]

Abstract Background Little guidance exists regarding how best to upskill and support those delivering complex healthcare interventions to ensure robust trial outcomes and implementation fidelity. Mentoring was provided to occupational therapists (OTs) delivering a complex vocational rehabilitation (VR) intervention to stroke survivors. This study aimed to explore mentors’ roles in supporting OTs with intervention delivery and fidelity, and to describe factors affecting the mentoring process and intervention delivery. Methods Quantitative data (duration, mode and total time of mentoring support) was extracted from mentoring records and emails between mentors and OTs, alongside qualitative data on barriers and facilitators to intervention delivery. Semi-structured interviews with mentors (n = 6) and OTs (n = 19) explored experiences and perceptions of intervention training, delivery and the mentoring process. Mean total and monthly time spent mentoring were calculated per trial site. Qualitative data were analysed thematically. Results Forty-one OTs across 16 sites were mentored between March 2018 and April 2020. Most mentoring was provided by phone or Microsoft Teams (range: 88.6–100%), with the remainder via email and SMS (Short Message Service) text messages. Mentors suggested strategies to enhance trial recruitment, improved OTs’ understanding of- and adherence to trial processes, intervention delivery and fidelity, and facilitated independent problem-solving. Barriers to mentoring included OT non-attendance at mentoring sessions and mentors struggling to balance mentoring with clinical roles. Facilitators included support from the trial team and mentors having protected time for mentoring. Conclusions Mentoring supported mentee OTs in various ways, but it remains unclear to what extent the OTS would have been able to deliver the intervention without mentoring support, or how this might have impacted fidelity. Successful implementation of mentoring alongside new complex interventions may increase the likelihood of intervention effectiveness being observed and sustained in real-life contexts. Further research is needed to investigate how mentors could be selected, upskilled, funded and mentoring provided to maximise impact. The clinical- and cost-effectiveness of mentoring as an implementation strategy and its impact on fidelity also requires testing in a future trial. Trial registration ISRCTN, ISRCTN12464275 . Registered on 13th March 2018.

Abstract Background Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. This study aims to investigate methods for the inclusion of RWE in NMA and its impact on the level of uncertainty around the effectiveness estimates, with particular interest in effectiveness of fingolimod. Methods A range of methods for inclusion of RWE in evidence synthesis were investigated by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). A literature search to identify RCTs and RWE evaluating treatments in RRMS was conducted. To assess the impact of inclusion of RWE on the effectiveness estimates, Bayesian hierarchical and adapted power prior models were applied. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior. Results Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. ‘Power prior’ NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs, however, this also increased the level of uncertainty. Conclusion The ‘power prior’ method for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted.

Background: Recruitment into clinical trials is challenging and there is a lack of evidence on effective recruitment strategies. Personalisation of invitation letters is a potentially pragmatic and feasible way of increasing recruitment rates at a low-cost. However, there is a lack of evidence concerning the effect of personalising of study invitation letters on recruitment rates.Methods: We undertook a Study Within A Trial (SWAT) to investigate the effect of personalised versus non-personalised study invitation letters on recruitment rates into the host feasibility trial ENGAGE, a feasibility study of an internet-administered, guided, Low Intensity Cognitive-Behavioural Therapy based self-help intervention for parents of children previously treated for cancer. An intervention group (n = 254) received a personalised study invitation letter and the control group (n = 255) received a non-personalised study invitation letter. The primary outcome was the proportion of participants in the intervention group and the control group enrolled into the ENGAGE host feasibility trial. Secondary outcomes relating to the recruitment and screening process, and retention were examined. Differences in proportions between groups for the primary and secondary outcomes were estimated using logistic regression.Results: Of the 509 potential participants, 56 (11.0%) were enrolled into the ENGAGE host feasibility trial: personalised: 30/254 (11.8%) and non-personalised: 26/255 (10.2%). No statistically significant effect on personalisation of enrolment was found (OR 1.18, 95% CI 0.68-2.06). No statistically significant differences were found for any secondary outcome.Conclusions: Personalisation of study invitations had no effect on recruitment. However, given the small study sample size in the present SWAT, and lack of similar embedded recruitment RCTs to enable a meta-analysis, additional SWATs to examine the personalisation of study invitation letters are warranted.Trial Registration: ISRCTN57233429 ; ISRCTN18404129 ; SWAT 112, Northern Ireland Hub for Trials Methodology Research SWAT repository (2018 OCT 1 1231) ( https://www.qub.ac.uk/sites/TheNorthernIrelandNetworkforTrialsMethodologyResearch/FileStore/Filetoupload,939618,en.pdf ). [ABSTRACT FROM AUTHOR]

Abstract Background Randomised controlled trials (RCTs) are the gold standard for evidence-based practice. However, RCTs can have limitations. For example, translation of findings into practice can be limited by design features, such as inclusion criteria, not accurately reflecting clinical populations. In addition, it is expensive to recruit and follow-up participants in RCTs. Linkage with routinely collected data could offer a cost-effective way to enhance the conduct and generalisability of RCTs. The aim of this study is to investigate how primary care data can support RCTs. Methods Secondary analysis following linkage of two datasets: 1) multicentre CHHiP radiotherapy trial (ISRCTN97182923) and 2) primary care database from the Royal College of General Practitioners Research and Surveillance Centre. Comorbidities and medications recorded in CHHiP at baseline, and radiotherapy-related toxicity recorded in CHHiP over time were compared with primary care records. The association of comorbidities and medications with toxicity was analysed with mixed-effects logistic regression. Results Primary care records were extracted for 106 out of 2811 CHHiP participants recruited from sites in England (median age 70, range 44 to 82). Complementary information included longitudinal body mass index, blood pressure and cholesterol, as well as baseline smoking and alcohol usage but was limited by the considerable missing data. In the linked sample, 9 (8%) participants were recorded in CHHiP as having a history of diabetes and 38 (36%) hypertension, whereas primary care records indicated incidence prior to trial entry of 11 (10%) and 40 (38%) respectively. Concomitant medications were not collected in CHHiP but available in primary care records. This indicated that 44 (41.5%) men took aspirin, 65 (61.3%) statins, 14 (13.2%) metformin and 46 (43.4%) phosphodiesterase-5-inhibitors at some point before or after trial entry. Conclusions We provide a set of recommendations on linkage and supplementation of trials. Data recorded in primary care are a rich resource and linkage could provide near real-time information to supplement trials and an efficient and cost-effective mechanism for long-term follow-up. In addition, standardised primary care data extracts could form part of RCT recruitment and conduct. However, this is at present limited by the variable quality and fragmentation of primary care data.

Abstract Background Bayesian adaptive methods are increasingly being used to design clinical trials and offer several advantages over traditional approaches. Decisions at analysis points are usually based on the posterior distribution of the treatment effect. However, there is some confusion as to whether control of type I error is required for Bayesian designs as this is a frequentist concept. Methods We discuss the arguments for and against adjusting for multiplicities in Bayesian trials with interim analyses. With two case studies we illustrate the effect of including interim analyses on type I/II error rates in Bayesian clinical trials where no adjustments for multiplicities are made. We propose several approaches to control type I error, and also alternative methods for decision-making in Bayesian clinical trials. Results In both case studies we demonstrated that the type I error was inflated in the Bayesian adaptive designs through incorporation of interim analyses that allowed early stopping for efficacy and without adjustments to account for multiplicity. Incorporation of early stopping for efficacy also increased the power in some instances. An increase in the number of interim analyses that only allowed early stopping for futility decreased the type I error, but also decreased power. An increase in the number of interim analyses that allowed for either early stopping for efficacy or futility generally increased type I error and decreased power. Conclusions Currently, regulators require demonstration of control of type I error for both frequentist and Bayesian adaptive designs, particularly for late-phase trials. To demonstrate control of type I error in Bayesian adaptive designs, adjustments to the stopping boundaries are usually required for designs that allow for early stopping for efficacy as the number of analyses increase. If the designs only allow for early stopping for futility then adjustments to the stopping boundaries are not needed to control type I error. If one instead uses a strict Bayesian approach, which is currently more accepted in the design and analysis of exploratory trials, then type I errors could be ignored and the designs could instead focus on the posterior probabilities of treatment effects of clinically-relevant values.

Abstract Background While randomised controlled trials (RCTs) provide high-quality evidence to guide practice, much routine care is not based upon available RCTs. This disconnect between evidence and practice is not sufficiently well understood. This case study explores this relationship using a novel approach. Better understanding may improve trial design, conduct, reporting and implementation, helping patients benefit from the best available evidence. Methods We employed a case-study approach, comprising mixed methods to examine the case of interest: the primary outcome paper of a surgical RCT (the TIME trial). Letters and editorials citing the TIME trial’s primary report underwent qualitative thematic analysis, and the RCT was critically appraised using validated tools. These analyses were compared to provide insight into how the TIME trial findings were interpreted and appraised by the clinical community. Results 23 letters and editorials were studied. Most authorship included at least one academic (20/23) and one surgeon (21/23). Authors identified wide-ranging issues including confounding variables or outcome selection. Clear descriptions of bias or generalisability were lacking. Structured appraisal identified risks of bias. Non-RCT evidence was less critically appraised. Authors reached varying conclusions about the trial without consistent justification. Authors discussed aspects of internal and external validity covered by appraisal tools but did not use these methodological terms in their articles. Conclusions This novel method for examining interpretation of an RCT in the clinical community showed that published responses identified limited issues with trial design. Responses did not provide coherent rationales for accepting (or not) trial results. Findings may suggest that authors lacked skills in appraisal of RCT design and conduct. Multiple case studies with cross-case analysis of other trials are needed.

Abstract Background It is important to estimate the treatment effect of interest accurately and precisely within the analysis of randomised controlled trials. One way to increase precision in the estimate and thus improve the power for randomised trials with continuous outcomes is through adjustment for pre-specified prognostic baseline covariates. Typically covariate adjustment is conducted using regression analysis, however recently, Inverse Probability of Treatment Weighting (IPTW) using the propensity score has been proposed as an alternative method. For a continuous outcome it has been shown that the IPTW estimator has the same large sample statistical properties as that obtained via analysis of covariance. However the performance of IPTW has not been explored for smaller population trials (

Background: There is an unmet need for review methods to support priority-setting, policy-making and strategic planning when a wide variety of interventions from differing disciplines may have the potential to impact a health outcome of interest. This article describes a Modular Literature Review, a novel systematic search and review method that employs systematic search strategies together with a hierarchy-based appraisal and synthesis of the resulting evidence.Methods: We designed the Modular Review to examine the effects of 43 interventions on a health problem of global significance. Using the PICOS (Population, Intervention, Comparison, Outcome, Study design) framework, we developed a single four-module search template in which population, comparison and outcome modules were the same for each search and the intervention module was different for each of the 43 interventions. A series of literature searches were performed in five databases, followed by screening, extraction and analysis of data. "ES documents", source documents for effect size (ES) estimates, were systematically identified based on a hierarchy of evidence. The evidence was categorised according to the likely effect on the outcome and presented in a standardised format with quantitative effect estimates, meta-analyses and narrative reporting. We compared the Modular Review to other review methods in health research for its strengths and limitations.Results: The Modular Review method was used to review the impact of 46 antenatal interventions on four specified birth outcomes within 12 months. A total of 61,279 records were found; 35,244 were screened by title-abstract. Six thousand two hundred seventy-two full articles were reviewed against the inclusion criteria resulting in 365 eligible articles.Conclusions: The Modular Review preserves principles that have traditionally been important to systematic reviews but can address multiple research questions simultaneously. The result is an accessible, reliable answer to the question of "what works?". Thus, it is a well-suited literature review method to support prioritisation, decisions and planning to implement an agenda for health improvement. [ABSTRACT FROM AUTHOR]

Background: Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. This study aims to investigate methods for the inclusion of RWE in NMA and its impact on the level of uncertainty around the effectiveness estimates, with particular interest in effectiveness of fingolimod.Methods: A range of methods for inclusion of RWE in evidence synthesis were investigated by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). A literature search to identify RCTs and RWE evaluating treatments in RRMS was conducted. To assess the impact of inclusion of RWE on the effectiveness estimates, Bayesian hierarchical and adapted power prior models were applied. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior.Results: Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. 'Power prior' NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs, however, this also increased the level of uncertainty.Conclusion: The 'power prior' method for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted. [ABSTRACT FROM AUTHOR]

Background: Little guidance exists regarding how best to upskill and support those delivering complex healthcare interventions to ensure robust trial outcomes and implementation fidelity. Mentoring was provided to occupational therapists (OTs) delivering a complex vocational rehabilitation (VR) intervention to stroke survivors. This study aimed to explore mentors' roles in supporting OTs with intervention delivery and fidelity, and to describe factors affecting the mentoring process and intervention delivery.Methods: Quantitative data (duration, mode and total time of mentoring support) was extracted from mentoring records and emails between mentors and OTs, alongside qualitative data on barriers and facilitators to intervention delivery. Semi-structured interviews with mentors (n = 6) and OTs (n = 19) explored experiences and perceptions of intervention training, delivery and the mentoring process. Mean total and monthly time spent mentoring were calculated per trial site. Qualitative data were analysed thematically.Results: Forty-one OTs across 16 sites were mentored between March 2018 and April 2020. Most mentoring was provided by phone or Microsoft Teams (range: 88.6-100%), with the remainder via email and SMS (Short Message Service) text messages. Mentors suggested strategies to enhance trial recruitment, improved OTs' understanding of- and adherence to trial processes, intervention delivery and fidelity, and facilitated independent problem-solving. Barriers to mentoring included OT non-attendance at mentoring sessions and mentors struggling to balance mentoring with clinical roles. Facilitators included support from the trial team and mentors having protected time for mentoring.Conclusions: Mentoring supported mentee OTs in various ways, but it remains unclear to what extent the OTS would have been able to deliver the intervention without mentoring support, or how this might have impacted fidelity. Successful implementation of mentoring alongside new complex interventions may increase the likelihood of intervention effectiveness being observed and sustained in real-life contexts. Further research is needed to investigate how mentors could be selected, upskilled, funded and mentoring provided to maximise impact. The clinical- and cost-effectiveness of mentoring as an implementation strategy and its impact on fidelity also requires testing in a future trial.Trial Registration: ISRCTN, ISRCTN12464275 . Registered on 13th March 2018. [ABSTRACT FROM AUTHOR]

Abstract Background Falls are common among older people, and General Practitioners (GPs) could play an important role in implementing strategies to manage fall risk. Despite this, fall prevention is not a routine activity in general practice settings. The iSOLVE cluster randomised controlled trial aimed to evaluate implementation of a fall prevention decision tool in general practice. This paper sought to describe the strategies used and reflect on the enablers and barriers relevant to successful recruitment of general practices, GPs and their patients. Methods Recruitment was conducted within the geographical area of a Primary Health Network in Northern Sydney, Australia. General practices and GPs were engaged via online surveys, mailed invitations to participate, educational workshops, practitioner networks and promotional practice visits. Patients 65 years or older were recruited via mailed invitations, incorporating the practice letterhead and the name(s) of participating GP(s). Observations of recruitment strategies, results and enabling factors were recorded in field notes as descriptive and narrative data, and analysed using mixed-methods. Results It took 19 months to complete recruitment of 27 general practices, 75 GPs and 560 patients. The multiple strategies used to engage general practices and GPs were collectively useful in reaching the targeted sample size. Practice visits were valuable in engaging GPs and staff, establishing interest in fall prevention and commitment to the trial. A mix of small, medium and large practices were recruited. While some were recruited as a whole-practice, other practices had few or half of the number of GPs recruited. The importance of preventing falls in older patients, simplicity of research design, provision of resources and logistic facilitation of patient recruitment appealed to GPs. Recruitment of older patients was successfully achieved by mailed invitations which was a strategy that was familiar to practice staff and patients. Patient response rates were above the expected 10% for most practices. Many practices (n = 17) achieved the targeted number of 20 or more patients. Conclusions Recruitment in general practice settings can be successfully achieved through multiple recruitment strategies, effective communication and rapport building, ensuring research topic and design suit general practice needs, and using familiar communication strategies to engage patients. Trial registration The trial was prospectively registered on 29 April 2015 with the Australian New Zealand Clinical Trial Registry www.anzctr.org.au (trial ID: ACTRN12615000401550).

Abstract Background Most clinical trials with time-to-event primary outcomes are designed assuming constant event rates and proportional hazards over time. Non-constant event rates and non-proportional hazards are seen increasingly frequently in trials. The objectives of this review were firstly to identify whether non-constant event rates and time-dependent treatment effects were allowed for in sample size calculations of trials, and secondly to assess the methods used for the analysis and reporting of time-to-event outcomes including how researchers accounted for non-proportional treatment effects. Methods We reviewed all original reports published between January and June 2017 in four high impact medical journals for trials for which the primary outcome involved time-to-event analysis. We recorded the methods used to analyse and present the main outcomes of the trial and assessed the reporting of assumptions underlying these methods. The sample size calculation was reviewed to see if the effect of either non-constant hazard rates or anticipated non-proportionality of the treatment effect was allowed for during the trial design. Results From 446 original reports we identified 66 trials with a time-to-event primary outcome encompassing trial start dates from July 1995 to November 2014. The majority of these trials (73%) had sample size calculations that used standard formulae with a minority of trials (11%) using simulation for anticipated changing event rates and/or non-proportional hazards. Well-established analytical methods, Kaplan-Meier curves (98%), the log rank test (88%) and the Cox proportional hazards model (97%), were used almost exclusively for the main outcome. Parametric regression models were considered in 11% of the reports. Of the trials reporting inference from the Cox model, only 11% reported any results of testing the assumption of proportional hazards. Conclusions Our review confirmed that when designing trials with time-to-event primary outcomes, methodologies assuming constant event rates and proportional hazards were predominantly used despite potential efficiencies in sample size needed or power achieved using alternative methods. The Cox proportional hazards model was used almost exclusively to present inferential results, yet testing and reporting of the pivotal assumption underpinning this estimation method was lacking.

Abstract 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.

Abstract Background We use the example of the Gojjam Lymphoedema Best Practice Trial (GoLBeT), a pragmatic trial in a remote rural setting in northern Ethiopia, to extract lessons relevant to other investigators balancing the demands of practicality and community acceptability with internal and external validity in clinical trials. Methods We explain in detail the preparation for the trial, its setting in northern Ethiopia, the identification and selection of patients (inclusion and exclusion criterion, identifying and screening of patients at home, enrollment of patients at the health centres and health posts), and randomisation. Results We describe the challenges met, together with strategies employed to overcome them. Conclusions Examples given in the previous section are contextualised and general principles extracted where possible. We conclude that it is possible to conduct a trial that balances approaches that support internal validity (e.g. careful design of proformas, accurate case identification, control over data quality and high retention rates) with those that favour generalisability (e.g. ‘real world’ setting and low rates of exclusion). Strategies, such as Rapid Ethical Assessment, that increase researchers’ understanding of the study setting and inclusion of hard-to-reach participants are likely to have resource and time implications, but are vital in achieving an appropriate balance. Trial registration ISRCTN67805210, registered 24/01/2013.

Background: Randomised controlled trials (RCTs) are the gold standard for evidence-based practice. However, RCTs can have limitations. For example, translation of findings into practice can be limited by design features, such as inclusion criteria, not accurately reflecting clinical populations. In addition, it is expensive to recruit and follow-up participants in RCTs. Linkage with routinely collected data could offer a cost-effective way to enhance the conduct and generalisability of RCTs. The aim of this study is to investigate how primary care data can support RCTs.Methods: Secondary analysis following linkage of two datasets: 1) multicentre CHHiP radiotherapy trial (ISRCTN97182923) and 2) primary care database from the Royal College of General Practitioners Research and Surveillance Centre. Comorbidities and medications recorded in CHHiP at baseline, and radiotherapy-related toxicity recorded in CHHiP over time were compared with primary care records. The association of comorbidities and medications with toxicity was analysed with mixed-effects logistic regression.Results: Primary care records were extracted for 106 out of 2811 CHHiP participants recruited from sites in England (median age 70, range 44 to 82). Complementary information included longitudinal body mass index, blood pressure and cholesterol, as well as baseline smoking and alcohol usage but was limited by the considerable missing data. In the linked sample, 9 (8%) participants were recorded in CHHiP as having a history of diabetes and 38 (36%) hypertension, whereas primary care records indicated incidence prior to trial entry of 11 (10%) and 40 (38%) respectively. Concomitant medications were not collected in CHHiP but available in primary care records. This indicated that 44 (41.5%) men took aspirin, 65 (61.3%) statins, 14 (13.2%) metformin and 46 (43.4%) phosphodiesterase-5-inhibitors at some point before or after trial entry.Conclusions: We provide a set of recommendations on linkage and supplementation of trials. Data recorded in primary care are a rich resource and linkage could provide near real-time information to supplement trials and an efficient and cost-effective mechanism for long-term follow-up. In addition, standardised primary care data extracts could form part of RCT recruitment and conduct. However, this is at present limited by the variable quality and fragmentation of primary care data. [ABSTRACT FROM AUTHOR]

Background: Although there is trial evidence that complex interventions are effective for the self-management of heart failure, little evidence supports their effectiveness in routine practice. We used Social Practice Theory to guide a Type 1 Hybrid Trial: a mixed methods process evaluation of a complex intervention for heart failure. The objective of this paper is to explore the value of Social Practice Theory for implementation science.Methods: Social Practice Theory informed a mixed methods process evaluation of a multi-centre randomised controlled trial of a 12 week home-based intervention to optimise self-care support for people with heart failure and their caregivers - Rehabilitation EnAblement in Chronic Heart Failure (REACH-HF). Interviews were conducted with 19 people with heart failure and 17 caregivers at 4 months and 12 months after recruitment into the trial. Cases were constructed at the level of the individual, couple, facilitator and centre; and included multi-modal process and outcome data. Evaluative coding and subsequent within- and cross-case analyses enabled the development of a typology of relationships linking fidelity of intervention delivery and tailoring of content to individual needs and concerns. Social Practice Theory was used to interrogate the relationships between elements of the intervention and their implementation.Results: Of 216 trial participants, 107 were randomised to the intervention (REACH-HF plus usual care). The intervention was most effective when fidelity was high and delivery was tailored to the individual's needs, but less effective when both tailoring and fidelity were low. Theory-based analysis enabled us to model complex relationships between intervention elements (competencies, materials and meanings) and social context. The findings illustrate how intervention fidelity and tailoring are contextual and how the effectiveness of the REACH-HF intervention depended on both optimal alignment and implementation of these elements.Conclusion: The study demonstrates the utility of theory-based analysis which integrates data from multiple sources to highlight contexts and circumstances in which interventions work best. Social Practice Theory provides a framework for guiding and analysing the processes by which a complex intervention is evaluated in a clinical trial, and has the potential to guide context-specific implementation strategies for clinical practice.Trial Registration: ISRCTN, IISRCTN86234930 . Registered 13th November 2014. [ABSTRACT FROM AUTHOR]

Background: Bayesian adaptive methods are increasingly being used to design clinical trials and offer several advantages over traditional approaches. Decisions at analysis points are usually based on the posterior distribution of the treatment effect. However, there is some confusion as to whether control of type I error is required for Bayesian designs as this is a frequentist concept.Methods: We discuss the arguments for and against adjusting for multiplicities in Bayesian trials with interim analyses. With two case studies we illustrate the effect of including interim analyses on type I/II error rates in Bayesian clinical trials where no adjustments for multiplicities are made. We propose several approaches to control type I error, and also alternative methods for decision-making in Bayesian clinical trials.Results: In both case studies we demonstrated that the type I error was inflated in the Bayesian adaptive designs through incorporation of interim analyses that allowed early stopping for efficacy and without adjustments to account for multiplicity. Incorporation of early stopping for efficacy also increased the power in some instances. An increase in the number of interim analyses that only allowed early stopping for futility decreased the type I error, but also decreased power. An increase in the number of interim analyses that allowed for either early stopping for efficacy or futility generally increased type I error and decreased power.Conclusions: Currently, regulators require demonstration of control of type I error for both frequentist and Bayesian adaptive designs, particularly for late-phase trials. To demonstrate control of type I error in Bayesian adaptive designs, adjustments to the stopping boundaries are usually required for designs that allow for early stopping for efficacy as the number of analyses increase. If the designs only allow for early stopping for futility then adjustments to the stopping boundaries are not needed to control type I error. If one instead uses a strict Bayesian approach, which is currently more accepted in the design and analysis of exploratory trials, then type I errors could be ignored and the designs could instead focus on the posterior probabilities of treatment effects of clinically-relevant values. [ABSTRACT FROM AUTHOR]

Background: A nested case-control study is an efficient design that can be embedded within an existing cohort study or randomised trial. It has a number of advantages compared to the conventional case-control design, and has the potential to answer important research questions using untapped prospectively collected data.Methods: We demonstrate the utility of the matched nested case-control design by applying it to a secondary analysis of the Abnormal Doppler Enteral Prescription Trial. We investigated the role of milk feed type and changes in milk feed type in the development of necrotising enterocolitis in a group of 398 high risk growth-restricted preterm infants.Results: Using matching, we were able to generate a comparable sample of controls selected from the same population as the cases. In contrast to the standard case-control design, exposure status was ascertained prior to the outcome event occurring and the comparison between the cases and matched controls could be made at the point at which the event occurred. This enabled us to reliably investigate the temporal relationship between feed type and necrotising enterocolitis.Conclusions: A matched nested case-control study can be used to identify credible associations in a secondary analysis of clinical trial data where the exposure of interest was not randomised, and has several advantages over a standard case-control design. This method offers the potential to make reliable inferences in scenarios where it would be unethical or impractical to perform a randomised clinical trial. [ABSTRACT FROM AUTHOR]

Background: While randomised controlled trials (RCTs) provide high-quality evidence to guide practice, much routine care is not based upon available RCTs. This disconnect between evidence and practice is not sufficiently well understood. This case study explores this relationship using a novel approach. Better understanding may improve trial design, conduct, reporting and implementation, helping patients benefit from the best available evidence.Methods: We employed a case-study approach, comprising mixed methods to examine the case of interest: the primary outcome paper of a surgical RCT (the TIME trial). Letters and editorials citing the TIME trial's primary report underwent qualitative thematic analysis, and the RCT was critically appraised using validated tools. These analyses were compared to provide insight into how the TIME trial findings were interpreted and appraised by the clinical community.Results: 23 letters and editorials were studied. Most authorship included at least one academic (20/23) and one surgeon (21/23). Authors identified wide-ranging issues including confounding variables or outcome selection. Clear descriptions of bias or generalisability were lacking. Structured appraisal identified risks of bias. Non-RCT evidence was less critically appraised. Authors reached varying conclusions about the trial without consistent justification. Authors discussed aspects of internal and external validity covered by appraisal tools but did not use these methodological terms in their articles.Conclusions: This novel method for examining interpretation of an RCT in the clinical community showed that published responses identified limited issues with trial design. Responses did not provide coherent rationales for accepting (or not) trial results. Findings may suggest that authors lacked skills in appraisal of RCT design and conduct. Multiple case studies with cross-case analysis of other trials are needed. [ABSTRACT FROM AUTHOR]

Background: It is important to estimate the treatment effect of interest accurately and precisely within the analysis of randomised controlled trials. One way to increase precision in the estimate and thus improve the power for randomised trials with continuous outcomes is through adjustment for pre-specified prognostic baseline covariates. Typically covariate adjustment is conducted using regression analysis, however recently, Inverse Probability of Treatment Weighting (IPTW) using the propensity score has been proposed as an alternative method. For a continuous outcome it has been shown that the IPTW estimator has the same large sample statistical properties as that obtained via analysis of covariance. However the performance of IPTW has not been explored for smaller population trials (< 100 participants), where precise estimation of the treatment effect has potential for greater impact than in larger samples.Methods: In this paper we explore the performance of the baseline adjusted treatment effect estimated using IPTW in smaller population trial settings. To do so we present a simulation study including a number of different trial scenarios with sample sizes ranging from 40 to 200 and adjustment for up to 6 covariates. We also re-analyse a paediatric eczema trial that includes 60 children.Results: In the simulation study the performance of the IPTW variance estimator was sub-optimal with smaller sample sizes. The coverage of 95% CI's was marginally below 95% for sample sizes < 150 and ≥ 100. For sample sizes < 100 the coverage of 95% CI's was always significantly below 95% for all covariate settings. The minimum coverage obtained with IPTW was 89% with n = 40. In comparison, regression adjustment always resulted in 95% coverage. The analysis of the eczema trial confirmed discrepancies between the IPTW and regression estimators in a real life small population setting.Conclusions: The IPTW variance estimator does not perform so well with small samples. Thus we caution against the use of IPTW in small sample settings when the sample size is less than 150 and particularly when sample size < 100. [ABSTRACT FROM AUTHOR]

Background: Falls are common among older people, and General Practitioners (GPs) could play an important role in implementing strategies to manage fall risk. Despite this, fall prevention is not a routine activity in general practice settings. The iSOLVE cluster randomised controlled trial aimed to evaluate implementation of a fall prevention decision tool in general practice. This paper sought to describe the strategies used and reflect on the enablers and barriers relevant to successful recruitment of general practices, GPs and their patients.Methods: Recruitment was conducted within the geographical area of a Primary Health Network in Northern Sydney, Australia. General practices and GPs were engaged via online surveys, mailed invitations to participate, educational workshops, practitioner networks and promotional practice visits. Patients 65 years or older were recruited via mailed invitations, incorporating the practice letterhead and the name(s) of participating GP(s). Observations of recruitment strategies, results and enabling factors were recorded in field notes as descriptive and narrative data, and analysed using mixed-methods.Results: It took 19 months to complete recruitment of 27 general practices, 75 GPs and 560 patients. The multiple strategies used to engage general practices and GPs were collectively useful in reaching the targeted sample size. Practice visits were valuable in engaging GPs and staff, establishing interest in fall prevention and commitment to the trial. A mix of small, medium and large practices were recruited. While some were recruited as a whole-practice, other practices had few or half of the number of GPs recruited. The importance of preventing falls in older patients, simplicity of research design, provision of resources and logistic facilitation of patient recruitment appealed to GPs. Recruitment of older patients was successfully achieved by mailed invitations which was a strategy that was familiar to practice staff and patients. Patient response rates were above the expected 10% for most practices. Many practices (n = 17) achieved the targeted number of 20 or more patients.Conclusions: Recruitment in general practice settings can be successfully achieved through multiple recruitment strategies, effective communication and rapport building, ensuring research topic and design suit general practice needs, and using familiar communication strategies to engage patients.Trial Registration: The trial was prospectively registered on 29 April 2015 with the Australian New Zealand Clinical Trial Registry www.anzctr.org.au (trial ID: ACTRN12615000401550). [ABSTRACT FROM AUTHOR]

Background: Most clinical trials with time-to-event primary outcomes are designed assuming constant event rates and proportional hazards over time. Non-constant event rates and non-proportional hazards are seen increasingly frequently in trials. The objectives of this review were firstly to identify whether non-constant event rates and time-dependent treatment effects were allowed for in sample size calculations of trials, and secondly to assess the methods used for the analysis and reporting of time-to-event outcomes including how researchers accounted for non-proportional treatment effects.Methods: We reviewed all original reports published between January and June 2017 in four high impact medical journals for trials for which the primary outcome involved time-to-event analysis. We recorded the methods used to analyse and present the main outcomes of the trial and assessed the reporting of assumptions underlying these methods. The sample size calculation was reviewed to see if the effect of either non-constant hazard rates or anticipated non-proportionality of the treatment effect was allowed for during the trial design.Results: From 446 original reports we identified 66 trials with a time-to-event primary outcome encompassing trial start dates from July 1995 to November 2014. The majority of these trials (73%) had sample size calculations that used standard formulae with a minority of trials (11%) using simulation for anticipated changing event rates and/or non-proportional hazards. Well-established analytical methods, Kaplan-Meier curves (98%), the log rank test (88%) and the Cox proportional hazards model (97%), were used almost exclusively for the main outcome. Parametric regression models were considered in 11% of the reports. Of the trials reporting inference from the Cox model, only 11% reported any results of testing the assumption of proportional hazards.Conclusions: Our review confirmed that when designing trials with time-to-event primary outcomes, methodologies assuming constant event rates and proportional hazards were predominantly used despite potential efficiencies in sample size needed or power achieved using alternative methods. The Cox proportional hazards model was used almost exclusively to present inferential results, yet testing and reporting of the pivotal assumption underpinning this estimation method was lacking. [ABSTRACT FROM AUTHOR]

Abstract Background In an individually randomised controlled trial where the treatment is delivered by a health professional it seems likely that the effectiveness of the treatment, independent of any treatment effect, could depend on the skill, training or even enthusiasm of the health professional delivering it. This may then lead to a potential clustering of the outcomes for patients treated by the same health professional, but similar clustering may not occur in the control arm. Using four case studies, we aim to provide practical guidance and recommendations for the analysis of trials with some element of clustering in one arm. Methods Five approaches to the analysis of outcomes from an individually randomised controlled trial with clustering in one arm are identified in the literature. Some of these methods are applied to four case studies of completed randomised controlled trials with clustering in one arm with sample sizes ranging from 56 to 539. Results are obtained using the statistical packages R and Stata and summarised using a forest plot. Results The intra-cluster correlation coefficient (ICC) for each of the case studies was small (

Abstract Background Although covariate adjustment in the analysis of randomised trials can be beneficial, adjustment for continuous covariates is complicated by the fact that the association between covariate and outcome must be specified. Misspecification of this association can lead to reduced power, and potentially incorrect conclusions regarding treatment efficacy. Methods We compared several methods of adjustment to determine which is best when the association between covariate and outcome is unknown. We assessed (a) dichotomisation or categorisation; (b) assuming a linear association with outcome; (c) using fractional polynomials with one (FP1) or two (FP2) polynomial terms; and (d) using restricted cubic splines with 3 or 5 knots. We evaluated each method using simulation and through a re-analysis of trial datasets. Results Methods which kept covariates as continuous typically had higher power than methods which used categorisation. Dichotomisation, categorisation, and assuming a linear association all led to large reductions in power when the true association was non-linear. FP2 models and restricted cubic splines with 3 or 5 knots performed best overall. Conclusions For the analysis of randomised trials we recommend (1) adjusting for continuous covariates even if their association with outcome is unknown; (2) keeping covariates as continuous; and (3) using fractional polynomials with two polynomial terms or restricted cubic splines with 3 to 5 knots when a linear association is in doubt.

Background: In an individually randomised controlled trial where the treatment is delivered by a health professional it seems likely that the effectiveness of the treatment, independent of any treatment effect, could depend on the skill, training or even enthusiasm of the health professional delivering it. This may then lead to a potential clustering of the outcomes for patients treated by the same health professional, but similar clustering may not occur in the control arm. Using four case studies, we aim to provide practical guidance and recommendations for the analysis of trials with some element of clustering in one arm. Methods: Five approaches to the analysis of outcomes from an individually randomised controlled trial with clustering in one arm are identified in the literature. Some of these methods are applied to four case studies of completed randomised controlled trials with clustering in one arm with sample sizes ranging from 56 to 539. Results are obtained using the statistical packages R and Stata and summarised using a forest plot. Results: The intra-cluster correlation coefficient (ICC) for each of the case studies was small (<0.05) indicating little dependence on the outcomes related to cluster allocations. All models fitted produced similar results, including the simplest approach of ignoring clustering for the case studies considered. Conclusions: A partially clustered approach, modelling the clustering in just one arm, most accurately represents the trial design and provides valid results. Modelling homogeneous variances between the clustered and unclustered arm is adequate in scenarios similar to the case studies considered. We recommend treating each participant in the unclustered arm as a single cluster. This approach is simple to implement in R and Stata and is recommended for the analysis of trials with clustering in one arm only. However, the case studies considered had small ICC values, limiting the generalisability of these results. [ABSTRACT FROM AUTHOR]

Background: Although covariate adjustment in the analysis of randomised trials can be beneficial, adjustment for continuous covariates is complicated by the fact that the association between covariate and outcome must be specified. Misspecification of this association can lead to reduced power, and potentially incorrect conclusions regarding treatment efficacy.Methods: We compared several methods of adjustment to determine which is best when the association between covariate and outcome is unknown. We assessed (a) dichotomisation or categorisation; (b) assuming a linear association with outcome; (c) using fractional polynomials with one (FP1) or two (FP2) polynomial terms; and (d) using restricted cubic splines with 3 or 5 knots. We evaluated each method using simulation and through a re-analysis of trial datasets.Results: Methods which kept covariates as continuous typically had higher power than methods which used categorisation. Dichotomisation, categorisation, and assuming a linear association all led to large reductions in power when the true association was non-linear. FP2 models and restricted cubic splines with 3 or 5 knots performed best overall.Conclusions: For the analysis of randomised trials we recommend (1) adjusting for continuous covariates even if their association with outcome is unknown; (2) keeping covariates as continuous; and (3) using fractional polynomials with two polynomial terms or restricted cubic splines with 3 to 5 knots when a linear association is in doubt. [ABSTRACT FROM AUTHOR]

Background: Surveys are commonly used in health research to assess patient satisfaction with hospital care. Achieving an adequate response rate, in the face of declining trends over time, threatens the quality and reliability of survey results. This paper evaluates a strategy to increase the response rate in a postal satisfaction survey with women who had recently given birth.Methods: A sample of 2048 Australian women who had recently given birth at seven maternity units in New South Wales were invited to participate in a postal survey about their recent experiences with maternity care. The study design included a randomised controlled trial that tested two types of pre-notification letter (with or without the option of opting out of the survey). The study also explored the acceptability of a request for consent to link survey data with existing routinely collected health data (omitting the latter data items from the survey reduced survey length and participant burden). This consent was requested of all women.Results: The survey had an overall response rate of 46% (913 completed surveys returned, total sample 1989). Women receiving the pre-notification letter with the option of opting out of the survey were more likely to actively decline to participate than women receiving the letter without this option, although the overall numbers of women declining were small (27 versus 12). Letter type was not significantly associated with the return of a completed survey. Among women who completed the survey, 97% gave consent to link their survey data with existing health data.Conclusions: The two types of pre-notification letters used in our study did not influence the survey response rate. However, seeking consent for record linkage was highly acceptable to women who completed the survey, and represents an important strategy to add to the arsenal for designing and implementing effective surveys. In addition to aspects of survey design, future research should explore how to more effectively influence personal constructs that contribute to the decision to participate in surveys. [ABSTRACT FROM AUTHOR]

Background: Using patient-reported outcome measures (PROMs) to assess Quality of Life (QoL) is well established, but commonly-used PROM item-sets do not necessarily capture what all respondents consider important. Measuring complex constructs is particularly difficult in randomised controlled trials (RCTs). The Mother-Generated Index (MGI) is a validated antenatal and postnatal QoL instrument in which the variables and scores are completely respondent-driven. This paper reports on the feasibility and acceptability of the MGI in an RCT, and compares the resulting variables and QoL scores with more commonly used instruments.Methods: The single-page MGI was included at the end of a ten page questionnaire pack and posted to the RCT participants at baseline (28-32 weeks' gestation) and follow-up (six weeks postnatal). Feasibility and acceptability were assessed by ease of administration, data entry and completion rates. Variables cited by women were analysed thematically. MGI QoL scores were compared with outcomes from the EQ-5D-3 L; Edinburgh Postnatal Depression Scale; Satisfaction With Life Scale; and State Trait Anxiety Inventory.Results: Six hundred and seventy eight pregnant women returned the pack at baseline; 668 completed the MGI (98.5%); 383/400 returns at follow up included a completed MGI (95.7%). Quantitative data were scanned into SPSS using a standard data scanning system, and were largely error-free; qualitative data were entered manually. The variables recorded by participants on the MGI forms incorporated many of those in the comparison instruments, and other outcomes commonly used in intrapartum trials, but they also revealed a wider range of issues affecting their quality of life. These included financial and work-related worries; moving house; and concerns over family illness and pets. The MGI scores demonstrated low-to-moderate correlation with other tools (all r values p < .01).Conclusions: Without face-to-face explanation and at the end of a long questionnaire, the MGI was feasible to use, and acceptable to RCT participants. It allowed individual participants to include issues that were important to them, but which are not well captured by existing tools. The MGI unites the explanatory power of qualitative research with the comparative power of quantitative designs, is inexpensive to administer, and requires minimal linguistic and conceptual translation.Trial Registration: ISRCTN27575146 (date assigned 23 March 2011). [ABSTRACT FROM AUTHOR]

Background: Recruitment to clinical trials is often problematic, with many trials failing to recruit to their target sample size. As a result, patient care may be based on suboptimal evidence from underpowered trials or non-randomised studies.Methods: For many conditions patients will require treatment on several occasions, for example, to treat symptoms of an underlying chronic condition (such as migraines, where treatment is required each time a new episode occurs), or until they achieve treatment success (such as fertility, where patients undergo treatment on multiple occasions until they become pregnant). We describe a re-randomisation design for these scenarios, which allows each patient to be independently randomised on multiple occasions. We discuss the circumstances in which this design can be used.Results: The re-randomisation design will give asymptotically unbiased estimates of treatment effect and correct type I error rates under the following conditions: (a) patients are only re-randomised after the follow-up period from their previous randomisation is complete; (b) randomisations for the same patient are performed independently; and (c) the treatment effect is constant across all randomisations. Provided the analysis accounts for correlation between observations from the same patient, this design will typically have higher power than a parallel group trial with an equivalent number of observations.Conclusions: If used appropriately, the re-randomisation design can increase the recruitment rate for clinical trials while still providing an unbiased estimate of treatment effect and correct type I error rates. In many situations, it can increase the power compared to a parallel group design with an equivalent number of observations. [ABSTRACT FROM AUTHOR]

Background It is often desirable to account for centre-effects in the analysis of multicentre randomised trials, however it is unclear which analysis methods are best in trials with a binary outcome. Methods We compared the performance of four methods of analysis (fixed-effects models, randomeffects models, generalised estimating equations (GEE), and Mantel-Haenszel) using a reanalysis of a previously reported randomised trial (MIST2) and a large simulation study. Results The re-analysis of MIST2 found that fixed-effects and Mantel-Haenszel led to many patients being dropped from the analysis due to over-stratification (up to 69% dropped for Mantel- Haenszel, and up to 33% dropped for fixed-effects). Conversely, random-effects and GEE included all patients in the analysis, however GEE did not reach convergence. Estimated treatment effects and p-values were highly variable across different analysis methods. The simulation study found that most methods of analysis performed well with a small number of centres. With a large number of centres, fixed-effects led to biased estimates and inflated type I error rates in many situations, and Mantel-Haenszel lost power compared to other analysis methods in some situations. Conversely, both random-effects and GEE gave nominal type I error rates and good power across all scenarios, and were usually as good as or better than either fixed-effects or Mantel-Haenszel. However, this was only true for GEEs with non-robust standard errors (SEs); using a robust 'sandwich' estimator led to inflated type I error rates across most scenarios. Conclusions With a small number of centres, we recommend the use of fixed-effects, random-effects, or GEE with non-robust SEs. Random-effects and GEE with non-robust SEs should be used with a moderate or large number of centres. [ABSTRACT FROM AUTHOR]

Background Gestational diabetes mellitus (GDM) is a risk factor for the development of type 2 diabetes. Lifestyle intervention can prevent progression to type 2 diabetes in high risk populations. We designed a randomised controlled trial (RCT) to evaluate the effectiveness of an established lifestyle intervention compared to standard care for delaying diabetes onset in European women with recent GDM. Recruitment into the RCT was more challenging than anticipated with only 89 of 410 (22%) women agreeing to participate. This paper identifies factors that could enhance participation of the target population in future interventions. Methods We hypothesised that women who agreed to participate would have higher diabetes risk profiles than those who declined, and secondly that it would be possible to predict participation on the bases of those risk factors. To test our hypothesis, we identified the subset of women for whom we had comprehensive data on diabetes risks factors 3-5 years following GDM, reducing the sample to 43 participants and 73 decliners. We considered established diabetes risk factors: smoking, daily fruit and vegetable intake, participation in exercise, family history of diabetes, glucose values and BMI scores on post-partum rescreens, use of insulin during pregnancy, and age at delivery. We also analysed narrative data from 156 decliners to further understand barriers to and facilitators of participation. Results Two factors differentiated participants and decliners: age at delivery (with women older than 34 years being more likely to participate) and insulin use during pregnancy (with women requiring the use of insulin in pregnancy less likely to participate). Binary logistic regression confirmed that insulin use negatively affected the odds of participation. The most significant barriers to participation included the accessibility, affordability and practicality of the intervention. Conclusions Women with recent GDM face multiple barriers to lifestyle change. Intervention designers should consider: (i) the practicalities of participation for this population, (ii) research designs that capitalise on motivational differences between participants, (iii) alleviating concerns about long-term diabetes management. We hope this work will support future researchers in developing interventions that are more relevant, effective and successful in recruiting the desired population. [ABSTRACT FROM AUTHOR]

Background: Poor response rates can jeopardise the validity of the findings of epidemiological surveys. The aim of this study was to undertake a randomised controlled trial to determine the effectiveness of different strategies for maximizing parental consent rates for dental health surveys of young children. Methods: The trial took place within the 2007/2008 NHS Epidemiological Dental Health Survey of 5-year-old children in the North West of England. Schools were randomised to one of five interventions: multiple letters to parents; promoting the research by providing additional information to parents and children; a financial incentive to the school; a financial incentive to the school administrator plus direct mailing to parents; and a control intervention comprising of usual practice, that is a single letter home to parents via the children. Results: A total of 335 schools (11,088 children) were recruited. The mean percentage consent rates ranged from 47% (financial incentive to school administrator plus direct mailing) to 63% (multiple letters). Pair-wise comparisons indicated that the multiple letter group had a statistically significantly greater consent rate than the financial incentive to the school administrator plus direct mailing group and promoting the research by providing additional information group, but was not statistically significantly different from the financial incentive to the school group and the control group. Conclusions: There was little evidence to show that any of the five interventions made a significant difference to consent rates when compared to the control group. Financial incentives to schools were less effective than multiple reminder letters to parents. Trials should be built into surveys to test different interventions, in different contexts to expand the evidence base for improving consent rates in health surveillance programmes. [ABSTRACT FROM AUTHOR]

Background: When multiple prognostic factors are adjusted for in the analysis of a randomised trial, it is unclear (1) whether it is necessary to account for each of the strata, formed by all combinations of the prognostic factors (stratified analysis), when randomisation has been balanced within each stratum (stratified randomisation), or whether adjusting for the main effects alone will suffice, and (2) the best method of adjustment in terms of type I error rate and power, irrespective of the randomisation method. Methods: We used simulation to (1) determine if a stratified analysis is necessary after stratified randomisation, and (2) to compare different methods of adjustment in terms of power and type I error rate. We considered the following methods of analysis: adjusting for covariates in a regression model, adjusting for each stratum using either fixed or random effects, and Mantel-Haenszel or a stratified Cox model depending on outcome. Results: Stratified analysis is required after stratified randomisation to maintain correct type I error rates when (a) there are strong interactions between prognostic factors, and (b) there are approximately equal number of patients in each stratum. However, simulations based on real trial data found that type I error rates were unaffected by the method of analysis (stratified vs unstratified), indicating these conditions were not met in real datasets. Comparison of different analysis methods found that with small sample sizes and a binary or time-to-event outcome, most analysis methods lead to either inflated type I error rates or a reduction in power; the lone exception was a stratified analysis using random effects for strata, which gave nominal type I error rates and adequate power. Conclusions: It is unlikely that a stratified analysis is necessary after stratified randomisation except in extreme scenarios. Therefore, the method of analysis (accounting for the strata, or adjusting only for the covariates) will not generally need to depend on the method of randomisation used. Most methods of analysis work well with large sample sizes, however treating strata as random effects should be the analysis method of choice with binary or time-to-event outcomes and a small sample size. [ABSTRACT FROM AUTHOR]

Background: A systematic review, with or without a meta-analysis, should be undertaken to determine if the research question of interest has already been answered before a new trial begins. There has been limited research on how systematic reviews are used within the design of new trials, the aims of this study were to investigate how systematic reviews of earlier trials are used in the planning and design of new randomised trials. Methods: Documentation from the application process for all randomised trials funded by the National Institute for Health Research Health Technology Assessment (NIHR HTA) between 2006 and 2008 were obtained. This included the: commissioning brief (if appropriate), outline application, minutes of the Board meeting in which the outline application was discussed, full application, detailed project description, referee comments, investigator response to referee comments, Board minutes on the full application and the trial protocol. Data were extracted on references to systematic reviews and how any such reviews had been used in the planning and design of the trial. Results: 50 randomised trials were funded by NIHR HTA during this period and documentation was available for 48 of these. The cohort was predominately individually randomised parallel trials aiming to detect superiority between two treatments for a single primary outcome. 37 trials (77.1%) referenced a systematic review within the application and 20 of these (i.e. 41.7% of the total) used information contained in the systematic review in the design or planning of the new trial. The main areas in which systematic reviews were used were in the selection or definition of an outcome to be measured in the trial (7 of 37, 18.9%), the sample size calculation (7, 18.9%), the duration of follow up (8, 21.6%) and the approach to describing adverse events (9, 24.3%). Boards did not comment on the presence/absence or use of systematic reviews in any application. Conclusions: Systematic reviews were referenced in most funded applications but just over half of these used the review to inform the design. There is an expectation from funders that applicants will use a systematic review to justify the need for a new trial but no expectation regarding further use of a systematic review to aid planning and design of the trial. Guidelines for applicants and funders should be developed to promote the use of systematic reviews in the design and planning of randomised trials, to optimise delivery of new studies informed by the most up-to-date evidence base and to minimise waste in research. [ABSTRACT FROM AUTHOR]