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Abstract Background Patients who suffer from chronic conditions or diseases are susceptible to experiencing repeated events of the same type (e.g. seizures), termed ‘recurrent events’. Prediction models can be used to predict the risk of recurrence so that intervention or management can be tailored accordingly, but statistical methodology can vary. The objective of this systematic review was to identify and describe statistical approaches that have been applied for the development and validation of multivariable prediction models with recurrent event data. A secondary objective was to informally assess the characteristics and quality of analysis approaches used in the development and validation of prediction models of recurrent event data. Methods Searches were run in MEDLINE using a search strategy in 2019 which included index terms and phrases related to recurrent events and prediction models. For studies to be included in the review they must have developed or validated a multivariable clinical prediction model for recurrent event outcome data, specifically modelling the recurrent events and the timing between them. The statistical analysis methods used to analyse the recurrent event data in the clinical prediction model were extracted to answer the primary aim of the systematic review. In addition, items such as the event rate as well as any discrimination and calibration statistics that were used to assess the model performance were extracted for the secondary aim of the review. Results A total of 855 publications were identified using the developed search strategy and 301 of these are included in our systematic review. The Andersen-Gill method was identified as the most commonly applied method in the analysis of recurrent events, which was used in 152 (50.5%) studies. This was closely followed by frailty models which were used in 116 (38.5%) included studies. Of the 301 included studies, only 75 (24.9%) internally validated their model(s) and three (1.0%) validated their model(s) in an external dataset. Conclusions This review identified a variety of methods which are used in practice when developing or validating prediction models for recurrent events. The variability of the approaches identified is cause for concern as it indicates possible immaturity in the field and highlights the need for more methodological research to bring greater consistency in approach of recurrent event analysis. Further work is required to ensure publications report all required information and use robust statistical methods for model development and validation. PROSPERO registration CRD42019116031.

ObjectivesThis study aims to review studies developing or validating a prediction model for transition to psychosis in individuals meeting At Risk Mental State (ARMS) criteria focussing on predictors that can be obtained as part of standard clinical practice. Prediction of transition is crucial to facilitating identification of patients who would benefit from cognitive behavioural therapy and, conversely, those that would benefit from less costly and less-intensive regular mental state monitoring. The review aims to determine whether prediction models rated as low risk of bias exist and, if not, what further research is needed within the field.DesignBibliographic databases (PsycINFO, Medline, EMBASE, CINAHL) were searched using index terms relating to the clinical field and prognosis from 1994, the initial year of the first prospective study using ARMS criteria, to July 2024. Screening of titles, abstracts, and subsequently full texts was conducted by two reviewers independently using predefined criteria. Study quality was assessed using the Prediction model Risk Of Bias ASessment Tool (PROBAST).SettingStudies in any setting were included.Primary and secondary outcome measuresThe primary outcome for the review was the identification of prediction models considering transition risk and a summary of their risk of bias.ResultsForty-eight unique prediction models considering risk of transition to psychosis were identified. Variables found to be consistently important when predicting transition were age, gender, global functioning score, trait vulnerability, and unusual thought content. PROBAST criteria categorised four unique prediction models as having an overall low-risk bias. Other studies were insufficiently powered for the number of candidate predictors or lacking enough information to draw a conclusion regarding risk of bias.ConclusionsTwo of the 48 identified prediction models were developed using current best practice statistical methodology, validated their model in independent data, and presented low risk of bias overall in line with the PROBAST guidelines. Any new prediction model built to evaluate the risk of transition to psychosis in people meeting ARMS criteria should be informed by the latest statistical methodology and adhere to the TRIPOD reporting guidelines to ensure that clinical practice is informed by the best possible evidence. External validation of such models should be carefully planned particularly considering generalisation across different countries.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPEROFILES/108488_PROTOCOL_20191127.pdf, identifier CRD42018108488.

Abstract The involvement of patients and the public in research is now an expectation in research with funders requesting a clear plan of involvement and engagement. In the United Kingdom involvement typically focuses on research prioritisation, design and delivery, in contrast activities that share the results of research or research methods more generally are considered to be engagement. Clinical trials tend to concentrate on involvement activities with less emphasis on engagement. To promote engagement activities in the context of clinical trials we asked people attending the 2022 International Clinical Trials Methodology Conference to share ideas on how we can best engage with patients and the public. Responses were reviewed and 22 themes identified. One suggestion was to create an advent calendar and so these 22 themes plus two from the authors were used as a daily tweet from the 1st to the 24th December 2022. Here we share these ideas and draw comparisons between engagement activities in research and traditions of the Christmas period. The ideas shared are not intended as a definitive list but instead a novel way to start discussions between experts by experience, researchers, health professionals and communities to facilitate co-production of meaningful engagement strategies.

Introduction Sleep and epilepsy have an established bidirectional relationship yet only one randomised controlled clinical trial has assessed the effectiveness of behavioural sleep interventions for children with epilepsy. The intervention was successful, but was delivered via face-to-face educational sessions with parents, which are costly and non-scalable to population level. The Changing Agendas on Sleep, Treatment and Learning in Epilepsy (CASTLE) Sleep-E trial addresses this problem by comparing clinical and cost-effectiveness in children with Rolandic epilepsy between standard care (SC) and SC augmented with a novel, tailored parent-led CASTLE Online Sleep Intervention (COSI) that incorporates evidence-based behavioural components.Methods and analyses CASTLE Sleep-E is a UK-based, multicentre, open-label, active concurrent control, randomised, parallel-group, pragmatic superiority trial. A total of 110 children with Rolandic epilepsy will be recruited in outpatient clinics and allocated 1:1 to SC or SC augmented with COSI (SC+COSI). Primary clinical outcome is parent-reported sleep problem score (Children’s Sleep Habits Questionnaire). Primary health economic outcome is the incremental cost-effectiveness ratio (National Health Service and Personal Social Services perspective, Child Health Utility 9D Instrument). Parents and children (≥7 years) can opt into qualitative interviews and activities to share their experiences and perceptions of trial participation and managing sleep with Rolandic epilepsy.Ethics and dissemination The CASTLE Sleep-E protocol was approved by the Health Research Authority East Midlands (HRA)–Nottingham 1 Research Ethics Committee (reference: 21/EM/0205). Trial results will be disseminated to scientific audiences, families, professional groups, managers, commissioners and policymakers. Pseudo-anonymised individual patient data will be made available after dissemination on reasonable request.Trial registration number ISRCTN13202325.

Abstract Background Test anxiety has a detrimental effect on test performance but current interventions for test anxiety have limited efficacy. Therefore, examination of newer psychological models of test anxiety is now required. Two transdiagnostic psychological models of emotional disorders that can account for anxiety are the intolerance of uncertainty model (IUM) and the Self-Regulatory Executive Function (S-REF) model. Intolerance of uncertainty, the stable disposition to find uncertainty distressing, is central to the IUM, while beliefs about thinking, metacognition, are central to the S-REF model. We tested for the first time the role of both intolerance of uncertainty and metacognitive beliefs in test anxiety. Methods A cross-sectional design was used, with college students (n = 675) completing questionnaires assessing their test anxiety, intolerance of uncertainty, and metacognitive beliefs. Hierarchical linear regressions examined if intolerance of uncertainty and metacognitive beliefs were associated with test anxiety, after controlling for age and gender. Results Females reported significantly more test anxiety than males. Partial correlations, controlling for gender, found intolerance of uncertainty and metacognitive beliefs were significantly and positively correlated with test anxiety. Hierarchical linear regressions found metacognitive beliefs explained an additional 13% of variance in test anxiety, after controlling for intolerance of uncertainty. When the order of entry was reversed, intolerance of uncertainty was only able to explain an additional 2% of variance, after controlling for metacognitive beliefs. In the final regression model, gender, intolerance of uncertainty and the metacognitive belief domains of ‘negative beliefs about the uncontrollability and danger of worry’ and ‘cognitive confidence’ were all significantly associated test anxiety, with ‘negative beliefs about the uncontrollability and danger of worry’ having the largest association. Conclusions Both intolerance of uncertainty and metacognitive beliefs are linked to test anxiety, but results suggest metacognitive beliefs have more explanatory utility, providing greater support for the S-REF model. Modification of intolerance of uncertainty and metacognitive beliefs could alleviate test anxiety and help students fulfil their academic potential.

Test anxiety is common among university students and impairs examination performance. Existing interventions for test anxiety are not particularly effective. Prior to developing an effective intervention, the key psychological beliefs that predict test anxiety need to be identified. Two transdiagnostic models, the intolerance of uncertainty (IU) and Self-Regulatory Executive Function (S-REF) models, propose different beliefs that result in emotional disorder, with beliefs about uncertainty emphasized in the IU model, and metacognitive beliefs emphasized in S-REF model. This study examines if IU and metacognitive beliefs predict test anxiety, and, if the relationship between these beliefs and examination performance is mediated by test anxiety. Undergraduates (n = 134) completed self-report questionnaires at two time points, approximately 3 months apart. At Time 1, during term time, participants completed questionnaires measuring their IU and metacognitive beliefs. At Time 2, participants completed a measure of “state” test anxiety immediately before their examination. IU and metacognitive beliefs were significantly positively correlated with test anxiety, but regression analyses found only the metacognitive belief domain “negative beliefs about the uncontrollability and danger of worry” predicted test anxiety. The relationship between “negative beliefs about the uncontrollability and danger of worry” and examination performance was mediated by the worry dimension of test anxiety. Overall, “negative beliefs about the uncontrollability and danger of worry” appear key to test anxiety. Modification of these metacognitive beliefs in the context of a well-being or study skills program for students could reduce test anxiety and ultimately improve academic performance.

Abstract Background Building a dataset of individual participant data (IPD) for meta-analysis represents considerable research investment as well as collaboration across multiple institutions and researchers. Making arrangements to curate and share the dataset beyond the IPD meta-analysis project for which it was established, for reuse in future research projects, would maximise the value of this investment. Methods Our aim was to establish the Cochrane repository for individual patient data from clinical trials in pregnancy and childbirth (CRIB) as an example of how an IPD repository could become part of Cochrane infrastructure. We believed that establishing CRIB under Cochrane auspices would engender trust and encourage trial investigators to share data, and at the same time position Cochrane to take steps towards expanding the number of reviews with IPD synthesis. Results CRIB was designed as a web-based platform to receive, host and facilitate onward sharing of de-identified data. Development was not straightforward and we did not fully achieve our aim as intended. We describe the challenges encountered and suggest ways that future repositories might overcome these. In particular, securing the legal agreements required to facilitate data sharing proved to be the main barrier, being time-consuming and more complex than anticipated. Conclusions We would recommend that researchers conducting IPD meta-analysis should consider discussing the option to transfer the curated IPD datasets to a repository at the end of the initial meta-analysis and this should be recognised within the data sharing agreements made with the original data contributors.

Abstract Background Complications after major surgery are a significant cause of morbidity and mortality. Neck dissection is one of the most commonly performed major operations in Head and Neck Surgical Oncology. Significant surgical complications occur in approximately 10–20% of all patients, increasing to 40% in patients who have had previous treatment to the area or have multiple co-morbidities and/or polypharmacy. Current evidence suggests that fibrin sealants (FS) may have potential clinical advantages in Head and Neck Surgery through the reduction of complications, volume of wound drainage and retention time of the drains. However, a paucity of high-quality trial-based evidence means that a surgical trial to determine the effectiveness of FS in reducing the rate and severity of complications in patients undergoing lateral neck dissection is warranted. The DEFeND randomised external pilot trial will address critical questions on how well key components of the proposed study design work together as well as the feasibility of a future phase III trial. Methods The study design that is being piloted is that of a two-arm, parallel group, superiority trial with block randomisation in a 1:1 allocation ratio. The interventional arm will constitute the application of FS (Artiss, Baxter Healthcare Ltd.) to the surgical wound following completion of a neck dissection procedure, in addition to standard of care (SOC). The control arm will constitute SOC alone. Eligible patients will include patients who require a lateral neck dissection with a minimum of three cervical nodal levels. Patients who require bilateral neck procedures or undergoing immediate reconstruction with free or regional flaps will be excluded. The outcomes being assessed will be recruitment rate, screened to randomisation rate, fidelity of blinding process using blinding indices, number of missing or incomplete data entries, number of protocol deviations and number of losses to follow-up. Suitability of the outcome measures proposed for the future phase III trial will also be assessed. Discussion The anticipated challenges for this study will be recruitment, complexity of the intervention and adherence to the protocol. The outcomes will inform the design, feasibility and conduct of a future phase III surgical trial. Trial registration First participant randomised: November 06, 2018; UKCRN Portfolio ID: 37896; ISRCTN99181100 .

Abstract Background Prognostic models for repeated events of the same type are highly useful in predicting when a patient may have a recurrence of a chronic disease or illness. Whilst methods are currently available for analysing recurrent event data in prognostic models, to our knowledge, most are not widely known or applied in a medical setting. As a result, often only the first recurrence is analysed meaning valuable information for multiple recurrences is discarded. Therefore, the aim of this review is to systemically review models for repeated medical events of the same type, to determine what modelling techniques are available and how they are applied. Methods MEDLINE will be used as the primary method to search sources. Various databases from the Cochrane Library and EMBASE will also be searched. Trial registries such as Clinicaltrials.gov.uk will be searched, as will registered trials that are ongoing and not yet published. Abstracts submitted to conferences will also be searched, and non-English sources will also be considered. Studies to be included in the review will be decided based on PICO guidelines, where the study population and outcomes correspond to this study’s aims and target population. The prognostic models used in each study chosen for inclusion in the review will be summarised qualitatively. Discussion As recurrent event data is not widely analysed in prognostic models, the results from this systematic review will identify which methods are available and which are commonly used. It is also unknown if certain methods which will be identified in the review perform better given certain conditions. Therefore, if included studies assess predictive performance, the results of this review could also provide evidence to determine if certain models are better fitting dependant on the event rate of the chronic condition. The results will be used to determine if model selection varies across disease area. The review will also provide an insight into the development of any new methods used for analysing recurrent events. Trial registration The review has been registered on PROSPERO (CRD42019116031).

Background Neurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome. Methods We conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models. Results We included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67–82]), than encephalopathy (54% [42–65]). Intensive care use was high (38% [35–41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27–32]. The hazard of death was comparatively lower for patients in the WHO European region. Interpretation Neurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission.

Rationale Clinical trials are the gold standard for testing interventions. COVID-19 has further raised their public profile and emphasised the need to deliver better, faster, more efficient trials for patient benefit. Considerable overlap exists between data required for trials and data already collected routinely in electronic healthcare records (EHRs). Opportunities exist to use these in innovative ways to decrease duplication of effort and speed trial recruitment, conduct and follow-up.Approach The National Institute of Health Research (NIHR), Health Data Research UK and Clinical Practice Research Datalink co-organised a national workshop to accelerate the agenda for ‘data-enabled clinical trials’. Showcasing successful examples and imagining future possibilities, the plenary talks, panel discussions, group discussions and case studies covered: design/feasibility; recruitment; conduct/follow-up; collecting benefits/harms; and analysis/interpretation.Reflection Some notable studies have successfully accessed and used EHR to identify potential recruits, support randomised trials, deliver interventions and supplement/replace trial-specific follow-up. Some outcome measures are already reliably collected; others, like safety, need detailed work to meet regulatory reporting requirements. There is a clear need for system interoperability and a ‘route map’ to identify and access the necessary datasets. Researchers running regulatory-facing trials must carefully consider how data quality and integrity would be assessed. An experience-sharing forum could stimulate wider adoption of EHR-based methods in trial design and execution.Discussion EHR offer opportunities to better plan clinical trials, assess patients and capture data more efficiently, reducing research waste and increasing focus on each trial’s specific challenges. The short-term emphasis should be on facilitating patient recruitment and for postmarketing authorisation trials where research-relevant outcome measures are readily collectable. Sharing of case studies is encouraged. The workshop directly informed NIHR’s funding call for ambitious data-enabled trials at scale. There is the opportunity for the UK to build upon existing data science capabilities to identify, recruit and monitor patients in trials at scale.

Abstract Background Psychotic disorders affect about 3% of the population worldwide and are associated with high personal, social and economic costs. They tend to have their first onset in adolescence. Increasing emphasis has been placed on early intervention to detect illness and minimise disability. In the late 1990s, criteria were developed to identify individuals at high risk for psychotic disorder. These are known as the at-risk mental state (ARMS) criteria. While ARMS individuals have a risk of psychosis much greater than the general population, most individuals meeting the ARMS criteria will not develop psychosis. Despite this, the National Institute for Health and Care Excellence recommends cognitive behavioural therapy (CBT) for all ARMS people. Clinical prediction models that combine multiple patient characteristics to predict individual outcome risk may facilitate identification of patients who would benefit from CBT and conversely those that would benefit from less costly and less intensive regular mental state monitoring. The study will systematically review the evidence on clinical prediction models aimed at making individualised predictions for the transition to psychosis. Methods Database searches will be conducted on PsycINFO, Medline, EMBASE and CINAHL. Reference lists and subject experts will be utilised. No language restrictions will be placed on publications, but searches will be restricted to 1994 onwards, the initial year of the first prospective study using ARMS criteria. Studies of any design will be included if they examined, in ARMS patients, whether more than one factor in combination is associated with the risk of transition to psychosis. Study quality will be assessed using the prediction model risk of bias assessment tool (PROBAST). Clinical prediction models will be summarised qualitatively, and if tested in multiple validation studies, their predictive performance will be summarised using a random-effects meta-analysis model. Discussion The results of the review will identify prediction models for the risk of transition to psychosis. These will be informative for clinicians currently treating ARMS patients and considering potential preventive interventions. The conclusions of the review will also inform the possible update and external validation of prediction models and clinical prediction rules to identify those at high or low risk of transition to psychosis. Trial registration The review has been registered with PROSPERO (CRD42018108488).

Introduction Asthma affects millions of children worldwide—1.1 million children in the UK. Asthma symptoms cannot be cured but can be controlled with low-dose inhaled corticosteroids (ICSs) in the majority of individuals. Treatment with a low-dose ICS, however, fails to control asthma symptoms in around 10%–15% of children and this places the individual at increased risk for an asthma attack. At present, there is no clear preferred treatment option for a child whose asthma is not controlled by low-dose ICS and international guidelines currently recommend at least three treatment options. Herein, we propose a systematic review and individual participant data network meta-analysis (IPD-NMA) aiming to synthesise all available published and unpublished evidence from randomised controlled trials (RCTs) to establish the clinical effectiveness of pharmacological treatments in children and adolescents with uncontrolled asthma on ICS and help to make evidence-informed treatment choices. This will be used to parameterise a Markov-based economic model to assess the cost-effectiveness of alternative treatment options in order to inform decisions in the context of drug formularies and clinical guidelines.Methods and analysis We will search in MEDLINE, the Cochrane Library, the Cochrane Central Register of Controlled Trials (CENTRAL), Embase, NICE Technology Appraisals and the National Institute for Health Research (NIHR) Health Technology Assessment series for RCTs of interventions in patients with uncontrolled asthma on ICS. All studies where children and adolescents were eligible for inclusion will be considered, and authors or sponsors will be contacted to request IPD on patients aged

Abstract Background There is increasing recognition that establishing a core set of outcomes to be evaluated and reported in trials of interventions for particular conditions will improve the usefulness of health research. There is no established core outcome set for childhood epilepsy. The aim of this work is to select a core outcome set to be used in evaluative research of interventions for children with rolandic epilepsy, as an exemplar of common childhood epilepsy syndromes. Methods First we will identify what outcomes should be measured; then we will decide how to measure those outcomes. We will engage relevant UK charities and health professional societies as partners, and convene advisory panels for young people with epilepsy and parents of children with epilepsy. We will identify candidate outcomes from a search for trials of interventions for childhood epilepsy, statutory guidance and consultation with our advisory panels. Families, charities and health, education and neuropsychology professionals will be invited to participate in a Delphi survey following recommended practices in the development of core outcome sets. Participants will be able to recommend additional outcome domains. Over three rounds of Delphi survey participants will rate the importance of candidate outcome domains and state the rationale for their decisions. Over the three rounds we will seek consensus across and between families and health professionals on the more important outcomes. A face-to-face meeting will be convened to ratify the core outcome set. We will then review and recommend ways to measure the shortlisted outcomes using clinical assessment and/or patient-reported outcome measures. Discussion Our methodology is a proportionate and pragmatic approach to expediently produce a core outcome set for evaluative research of interventions aiming to improve the health of children with epilepsy. A number of decisions have to be made when designing a study to develop a core outcome set including defining the scope, choosing which stakeholders to engage, most effective ways to elicit their views, especially children and a potential role for qualitative research.

Abstract Background Glioblastoma is the commonest form of malignant brain tumour in adults, affecting 2–3 people per 100,000 per year. Despite current treatment options including surgical resection, radiotherapy and temozolomide chemotherapy, overall survival at 2 years is approximately 27%, with a median survival of 12–14 months. The ketogenic diet (KD) is postulated to work by simulating the metabolic response to fasting by promoting the utilisation of ketones as a primary energy source, and depriving the glycolytic pathways utilised by malignant glioma cells for growth. At present, there is no consensus as to which KD is preferable, with previous case series using different KDs, at different points in the treatment pathway. The aim of this randomised pilot study is to investigate protocol feasibility, tolerability and the impact on patient health and quality of life of two different KDs within an NHS setting. The results of this pilot study will inform which KD will be most deliverable and adhered to by patients in order to test for effectiveness in future trials. Methods A prospective, non-blinded, randomised, pilot study will be undertaken in 12 patients with newly diagnosed glioblastoma treated by surgical resection. Patients will be randomised in a ratio of 1:1, using a permuted block randomisation method to one of two diets; the modified ketogenic diet and the medium chain triglyceride ketogenic diet. Primary data collection will take place 12 weeks after starting the diet and secondary data collection after 12 months. Feasibility will be assessed by retention and recruitment rates, ability to enrol patients prior to starting chemoradiotherapy, dietary compliance and adjustments, ketone levels, glucose levels and intervention time. Patient impact will be assessed through quality of life and food acceptability questionnaires, gastrointestinal side effects and changes to biochemical markers and anthropometric measures, assessed at regular intervals. Discussion The results of this pilot study will be used to inform the feasibility, methodological design and power calculations of future phase III clinical trials investigating the effectiveness of KD as an adjuvant therapy in the management of glioblastoma. Trial registration ISRCTN71665562 and NCT03075514 .

Abstract Background Demands are increasingly being made for clinical trialists to actively share individual participant data (IPD) collected from clinical trials using responsible methods that protect the confidentiality and privacy of clinical trial participants. Clinical trialists, particularly those receiving public funding, are often concerned about the additional time and money that data-sharing activities will require, but few published empirical data are available to help inform these decisions. We sought to evaluate the activity and resources required to prepare anonymised IPD from a clinical trial in anticipation of a future data-sharing request. Methods Data from two UK publicly funded clinical trials were used for this exercise: 2437 participants with epilepsy recruited from 90 hospital outpatient clinics in the SANAD trial and 146 children with neuro-developmental problems recruited from 18 hospitals in the MENDS trial. We calculated the time and resources required to prepare each anonymised dataset and assemble a data pack ready for sharing. Results The older SANAD trial (published 2007) required 50 hours of staff time with a total estimated associated cost of £3185 whilst the more recently completed MENDS trial (published 2012) required 39.5 hours of staff time with total estimated associated cost of £2540. Conclusions Clinical trial researchers, funders and sponsors should consider appropriate resourcing and allow reasonable time for preparing IPD ready for subsequent sharing. This process would be most efficient if prospectively built into the standard operational design and conduct of a clinical trial. Further empirical examples exploring the resource requirements in other settings is recommended. Trial registration SANAD: International Standard Randomised Controlled Trials Registry: ISRCTN38354748 . Registered on 25 April 2003. MENDS: EU Clinical Trials Register Eudract 2006-004025-28 . Registered on 16 May 2007. International Standard Randomised Controlled Trials Registry: ISRCTN05534585 /MREC 07/MRE08/43. Registered on 26 January 2007.

Abstract Background Greater transparency, including sharing of patient-level data for further research, is an increasingly important topic for organisations who sponsor, fund and conduct clinical trials. This is a major paradigm shift with the aim of maximising the value of patient-level data from clinical trials for the benefit of future patients and society. We consider the analysis of shared clinical trial data in three broad categories: (1) reanalysis - further investigation of the efficacy and safety of the randomized intervention, (2) meta-analysis, and (3) supplemental analysis for a research question that is not directly assessing the randomized intervention. Discussion In order to support appropriate interpretation and limit the risk of misleading findings, analysis of shared clinical trial data should have a pre-specified analysis plan. However, it is not generally possible to limit bias and control multiplicity to the extent that is possible in the original trial design, conduct and analysis, and this should be acknowledged and taken into account when interpreting results. We highlight a number of areas where specific considerations arise in planning, conducting, interpreting and reporting analyses of shared clinical trial data. A key issue is that that these analyses essentially share many of the limitations of any post hoc analyses beyond the original specified analyses. The use of individual patient data in meta-analysis can provide increased precision and reduce bias. Supplemental analyses are subject to many of the same issues that arise in broader epidemiological analyses. Specific discussion topics are addressed within each of these areas. Summary Increased provision of patient-level data from industry and academic-led clinical trials for secondary research can benefit future patients and society. Responsible data sharing, including transparency of the research objectives, analysis plans and of the results will support appropriate interpretation and help to address the risk of misleading results and avoid unfounded health scares.

Aim: We performed a systematic review of the evidence for effectiveness and acceptability of different ketogenic diets (KDs) in the therapeutic management of gliomas. Methods: The search strategy included searches of seven electronic databases. Data extraction and quality assessment were undertaken independently by two authors. Results: No randomized clinical trials were identified. Six studies (n = 39) met the eligibility criteria for this review – all were case series or reports and therefore at high risk of bias. All studies reported overall or progression-free survival; however the effectiveness of KD interventions could not be established. Dietary acceptability was not reported. Conclusion: The effectiveness and acceptability of KDs in the management of gliomas is unknown and high quality randomized controlled trials are needed.

ObjectivesTo develop prognostic models for risk of a breakthrough seizure, risk of seizure recurrence after a breakthrough seizure, and likelihood of achieving 12-month remission following a breakthrough seizure. A breakthrough seizure is one that occurs following at least 12 months remission whilst on treatment.MethodsWe analysed data from the SANAD study. This long-term randomised trial compared treatments for participants with newly diagnosed epilepsy. Multivariable Cox models investigated how clinical factors affect the probability of each outcome. Best fitting multivariable models were produced with variable reduction by Akaike's Information Criterion. Risks associated with combinations of risk factors were calculated from each multivariable model.ResultsSignificant factors in the multivariable model for risk of a breakthrough seizure following 12-month remission were number of tonic-clonic seizures by achievement of 12-month remission, time taken to achieve 12-month remission, and neurological insult. Significant factors in the model for risk of seizure recurrence following a breakthrough seizure were total number of drugs attempted to achieve 12-month remission, time to achieve 12-month remission prior to breakthrough seizure, and breakthrough seizure treatment decision. Significant factors in the model for likelihood of achieving 12-month remission after a breakthrough seizure were gender, age at breakthrough seizure, time to achieve 12-month remission prior to breakthrough, and breakthrough seizure treatment decision.ConclusionsThis is the first analysis to consider risk of a breakthrough seizure and subsequent outcomes. The described models can be used to identify people most likely to have a breakthrough seizure, a seizure recurrence following a breakthrough seizure, and to achieve 12-month remission following a breakthrough seizure. The results suggest that focussing on achieving 12-month remission swiftly represents the best therapeutic aim to reduce the risk of a breakthrough seizure and subsequent negative outcomes. This will aid individual patient risk stratification and the design of future epilepsy trials.

Jayne Tierney and colleagues offer guidance on how to spot a well-designed and well-conducted individual participant data meta-analysis.

In the United Kingdom and other European Union countries guidelines for driving following a first unprovoked seizure require the risk of another seizure in the next year to be less than 20%. Using data from one clinical trial, we previously developed a prognostic model to inform driving guidelines. The objective of this work is to externally validate our published model and demonstrate its generalisability.A cohort of 620 people with a first unprovoked seizure was used to develop the original model which included variables for aetiology, first degree relative with epilepsy, seizures only while asleep, electroencephalogram, computed tomography or magnetic resonance scan result, and treatment policy. The validation cohorts consisted of 274 (United Kingdom), 305 (Italy), and 847 (Australia) people. The model was evaluated using discrimination and calibration methods. A covariate, missing from the Italian dataset, was handled via five imputation methods. Following external validation, the model was fitted to a pooled population comprising all validation datasets and the development dataset. The model was stratified by dataset.The model generalised relatively well. All methods of imputation performed fairly similarly. At six months, the risk of a seizure recurrence following a first ever seizure, based on the pooled datasets, is 15% (95% CI: (12% to 18%)) for patients who are treated immediately and 18% (95% CI: (15 to 21%)) otherwise. Individuals can be reliably stratified into risk groups according to the clinical factors included in the model.Our prognostic model, used to inform driving regulations, has been validated and consequently has been proven as a valuable tool for predicting risk of seizure recurrence following a first seizure in people with various combinations of risk factors. Additionally, there is evidence to support one worldwide overall prognostic model for risk of second seizure following a first.

Calls have been made for increased access to individual participant data (IPD) from clinical trials, to ensure that complete evidence is available. However, despite the obvious benefits, progress towards this is frustratingly slow. In the meantime, many systematic reviews have already collected IPD from clinical trials. We propose that a central repository for these IPD should be established to ensure that these datasets are safeguarded and made available for use by others, building on the strengths and advantages of the collaborative groups that have been brought together in developing the datasets.Evaluate the level of support, and identify major issues, for establishing a central repository of IPD.On-line survey with email reminders.71 reviewers affiliated with the Cochrane Collaboration's IPD Meta-analysis Methods Group were invited to participate.30 (42%) invitees responded: 28 (93%) had been involved in an IPD review and 24 (80%) had been involved in a randomised trial. 25 (83%) agreed that a central repository was a good idea and 25 (83%) agreed that they would provide their IPD for central storage. Several benefits of a central repository were noted: safeguarding and standardisation of data, increased efficiency of IPD meta-analyses, knowledge advancement, and facilitating future clinical, and methodological research. The main concerns were gaining permission from trial data owners, uncertainty about the purpose of the repository, potential resource implications, and increased workload for IPD reviewers. Restricted access requiring approval, data security, anonymisation of data, and oversight committees were highlighted as issues under governance of the repository.There is support in this community of IPD reviewers, many of whom are also involved in clinical trials, for storing IPD in a central repository. Results from this survey are informing further work on developing a repository of IPD which is currently underway by our group.

BackgroundSource data verification (SDV) is a resource intensive method of quality assurance frequently used in clinical trials. There is no empirical evidence to suggest that SDV would impact on comparative treatment effect results from a clinical trial.MethodsData discrepancies and comparative treatment effects obtained following 100% SDV were compared to those based on data without SDV. Overall survival (OS) and Progression-free survival (PFS) were compared using Kaplan-Meier curves, log-rank tests and Cox models. Tumour response classifications and comparative treatment Odds Ratios (ORs) for the outcome objective response rate, and number of Serious Adverse Events (SAEs) were compared. OS estimates based on SDV data were compared against estimates obtained from centrally monitored data.FindingsData discrepancies were identified between different monitoring procedures for the majority of variables examined, with some variation in discrepancy rates. There were no systematic patterns to discrepancies and their impact was negligible on OS, the primary outcome of the trial (HR (95% CI): 1.18(0.99 to 1.41), p = 0.064 with 100% SDV; 1.18(0.99 to 1.42), p = 0.068 without SDV; 1.18(0.99 to 1.40), p = 0.073 with central monitoring). Results were similar for PFS. More extreme discrepancies were found for the subjective outcome overall objective response (OR (95% CI): 1.67(1.04 to 2.68), p = 0.03 with 100% SDV; 2.45(1.49 to 4.04), p = 0.0003 without any SDV) which was mostly due to differing CT scans.InterpretationQuality assurance methods used in clinical trials should be informed by empirical evidence. In this empirical comparison, SDV was expensive and identified random errors that made little impact on results and clinical conclusions of the trial. Central monitoring using an external data source was a more efficient approach for the primary outcome of OS. For the subjective outcome objective response, an independent blinded review committee and tracking system to monitor missing scan data could be more efficient than SDV.