dataesr/scientific-paragraphs-categorization huggingface dataset:
P183 Recruitment to randomised controlled trials in patients receiving unplanned hospital care: a systematic review and in-depth analysis using the ORRCA database Ceri Rowlands 1 , L. Rooshenhas 1 , Jonathan Rees 2 , Katherine Fairhurst 1 , Carrol Gamble 3 , Jane M Blazeby All papers were categorised according to the recruitment study design (randomised or non-randomised) and whether an intervention to optimise recruitment was evaluated. Additional categorisation addressed whether the paper evaluated recruitment to a real clinical RCT (host RCT) or potential recruitment to a RCT that did not yet exist (a 'hypothetical RCT'). Data extracted included i) perceived barriers to recruitment which formed the rationale for the study, ii) barriers to recruitment identified as the result of the recruitment stud and iii) types of intervention evaluated.
Interim results Of 3114 articles within the ORRCA database, 39 were eligible. Duplicate screening did not produce any unresolvable discrepancies. One paper used a randomised recruitment study design to evaluate an intervention, 11 evaluated an intervention through a nonrandomised study and 16 recruitment studies did not evaluate an intervention. A further 11 studies report results from community surveys of proposed hypothetical RCTs. Perceived barriers to recruitment included the clinical condition of the patient, patients impaired ability to provide valid informed consent and a narrow therapeutic time window. Further barriers to recruitment identified as the result of the recruitment study were clinician's refusal for patients to be approached, workload of the clinical team, insufficient approach of eligible participants and the use of surrogate decision makers (SDMs).
Methods Assuming log-hazard-ratio is a logistic function of continuous prognostic variable, the midpoint of the sigmoid curve (x_mp) would be a natural choice for the cutpoint. 100,000 survival datasets were generated via Monte Carlo simulations using R language. Each simulated dataset included 200 observations (x) with exponential distribution (similar to the number of the patients and the distribution of ARcopy numbers in the trial) and log-hazard-ratio (y) as a logistic function of x. Parameters of the steepness of the curve and location of the midpoint (x_mp) were randomly assigned in each run. For every simulated dataset, the best cutpoint was sought via the following iterative steps: (i) assigning 0 to all observations below copy number x_i and 1 to all observations equal to or above copy number x_i, (ii) fitting Cox model (for x_i), (iii) using the maximum values of the statistics of survival modelling including Hazard Ratio, Log-Likelihoodor Cox-Snell Pseudo-R-Squared (RSQ) -, Concordance Index, Waldtest, and Log-Rank-test as indicators (correlative measures) of the cutpoint, (iv) calculating the difference between the cutpoints suggested by each correlative measure and the true cutpoint (x_mp).
Method Electronic search of the following databases: Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, MEDLINE via Ovid will be conducted. An attempt to identify unpublished data by searching clinical trial registries, through clinicaltrails. gov, and the Saudi Food and Drug Administration (SFDA) registry will be conducted. The search strategy will contain a combination of mesh terms and keywords relevant to the study design. Identified RCTs will be exported to Endnote X7 to check and remove any duplication. All titles and abstracts of identified RCTs will be screened by two investigators for potential relevance. Reference lists of potential studies, systematic reviews and meta-analysis will be also reviewed manually to identify relevant original RCTs. Search will be limited to RCTs either phase II, III and IV, published in 2005 and above in both English and Arabic language.
Generic distributions were derived from a hierarchical model fitted to 64 meta-analyses from the Cochrane Database of Systematic Reviews. Opinion-based distributions were averaged across 12 assessors who read summary information on each trial in a new meta-analysis, and independently gave their opinions on bias. We developed three different approaches to combine generic evidence with detailed trial assessment. The first method statistically combines the generic and opinion-based bias distributions. In two alternative methods, assessors are provided with generic bias distributions and summary trial information, and asked to give their opinion on where in the distribution the particular trial might lie (numerically or by selecting broad areas of the distribution). In two case study meta-analyses, we adjusted for bias according to the set of distributions derived using each of the three approaches.
P234 Why do trials get suspended? A review of data from clinicaltrials. gov and ISRCTN Seonaidh Cotton, Chloe Brooks, Lynda Constable University of Aberdeen Correspondence: Seonaidh Cotton Trials 2017, 18(Suppl 1):P234 Background Within our trials unit two trials have recently been suspended, and then restarted. While there is a fairly mature literature on early termination of studies, there is a paucity of literature about the temporary suspension of studies. We aimed to document reasons for trial suspensions using data available on publically available registers of clinical trials (clinicaltrials. gov and ISRCTN). Methods Clinicaltrials. gov define a 'suspended' study as a 'study that has stopped recruiting or enrolling participants early, but may start again'. We searched clinicaltrials.
gov for interventional studies which had their recruitment status recorded as 'suspended': the search was run on 29 June 2016. ISRCTN do not have an equivalent term for suspended trials. The closest term is 'stopped' which includes studies that have never started along with those that have stopped prematurely. We searched ISRCTN for interventional studies which had their status recorded as 'stopped': the search was run between 18 July and 12 August 2016. For each suspended trial, a code was assigned to each trial to classify the reason for the suspension. The coding framework was developed inductively and continually refined during the process of coding. Results 837 trials registered on clinicaltrials. gov had their recruitment status designated as suspended. 403 trials registered on ISRCTN were recorded as 'stopped'.
It was not possible to identify the reason for suspension/stopping for 40% of those recorded as suspended on clinicaltrials. gov and 8% of those recorded as stopped on ISRCTN: either no reason was given, or the reason was not clear or ambiguous. The review of reasons for suspension identified five main themes: drug/intervention issues (drug/intervention safety issues, drug/intervention supply issues); trial evaluation (futility, review of trial/changes to protocol); funding (funding issues); recruitment (primarily slow accrual, but some cases of rapid accrual); and running of the trial (operational issues, staffing, wider organisational issues). The proportions of trials suspended/stopped for each of these reasons differed between the two trial registers. 19% of those suspended on clinicaltrials.
Methods Programmers developed an in-house Microsoft Access database used to track receipt of IRB approvals for numerous studies from multiple ccs. The custom database has capacity to monitor approval expiration dates for an unlimited number of studies and ccs, and it can generate automated reports displaying information on all documented approvals. The system has functionality to produce reports organized by individual protocol and/or CC, as well as the ability to highlight IRB approvals that must be renewed within the next three months. Coordinators formalized communication procedures for collecting updated approvals and informing ccs of the status of information currently on file. We established a central email account to which ccs submit updated documentation. Upon receipt of documentation a DCC coordinator acknowledges delivery, files documentation, and enters updated information into the database.
Results The development and implementation of a database increased efficiencies both for ccs and DCC s. The processes reduced the volume of email regarding IRB approvals sent to ccs. Individual emails to ccs notifying them an approval is about to expire are no longer necessary. Instead, a single monthly email is sent to all ccs indicating updated IRB reports have been posted and should be reviewed. Automated highlighting of approvals that will expire soon has also reduced the burden on the DCC coordinators and minimized the likelihood of oversight. The creation of a central database and formalized procedures have streamlined internal regulatory processes for DCC staff. If questions arise about an approval for a specific CC, DCC staff can access the database to look up information needed.
Methods The CPRD I database was interrogated to create a pre-screened list of patients located in practices close to the research sites in central London. The search engine used a study-specific validated codelist and algorithm. Using diagnostic codes alone, this algorithm had a Positive Predictive Value (PPV) of 86. 5% which is improved slightly by including use of antibiotics and oral corticosteroids in previous 12 months and spirometry; spirometry was not used as a screening criterion within the I but was subsequently performed at the research site. Patients were excluded if they were either current smokers or aged < 35 years old. Participating GPs were provided with a pre-screened list from which to identify and select suitable patients to receive information about the study. Potential participants could contact the research team directly to be enrolled.
Factors relating to statistics and clinical data management (CDM) however, are crucial to the planning, design, conduct, monitoring, analyses and reporting of trials. Reliable results from statistical analyses are imperative to ensure confidence in the clinical interpretation of treatments. Some key trial statistical and CDM aspects include: choosing trial design, variables and outcomes, building databases, planning and implementing randomization schedules, sample size calculations, statistical monitoring and quality control, maintaining accurate statistical documentation, Source Data Verification, interim and statistical analysis and translating statistical results into clinically meaningful findings. One key aspect to ensuring this 'statistical rigour' , is having competent and enthusiastic inter-disciplinary 'Trialists' -most notably: trial statisticians, data managers and principal investigators (PIs).
P251 Use of routine databases to aid the design of multicentre surgical trials with length-of-stay as the primary outcome Olympia Papachristofi, Linda Sharples London School of Hygiene and Tropical Medicine Correspondence: Olympia Papachristofi Trials 2017, 18(Suppl 1):P251 Background Complex surgical interventions are an indispensable part of modern healthcare and there is increasing recognition that novel procedures should undergo the same rigorous evaluation as other non-invasive treatments. However, the multi-component nature of surgery complicates evaluation. For instance, surgical procedures are delivered by multidisciplinary teams and thus their outcome may vary due to patient characteristics, skill of the operators and the environment within which they are conducted. Recognition and accommodation of this variation is important in order to design adequately powered related trials.
The duration of postoperative Length-Of-Stay (LOS) (or incidence of prolonged hospitalisation) has been the focus of many surgical trials as it is a principal driver of surgical costs, and acts as a surrogate for a range of post-operative complications. Therefore it is important to understand how and why this outcome varies, so that recommendations for trial design and analysis can be made. Traditionally, surgical trial design suffered from a lack of detailed multicentre data. The current availability of high-quality, routinely collected administrative databases allows us to explore current practice and outcomes, in order to inform trial design in this context.
The proposed approach is exploring and automating the feature of sending SMS to mobile phones from Email to serve the following various activities in clinical trials. 1. Reporting/Notifying the stake holders about importance occurrences of events 2. Notifying the programming team about the current program status. These two activities are completely SAS data driven. In clinical trials, SAS is one of the widely used software for the data management, analysis and reporting since the clinical trial datasets are often available in SAS format. The analysis reports (Tables, Listings & Figures) are generated using SAS programming language for the FDA submission. SAS programming language based automation is implemented for these two activities. 3. Engaging Study participants 4. Reminder notifications to the project team on various activities.
Implementation For paper trials, CRFs are received by ICR-CTSU and are entered onto the database by DMs. CRFs are stored chronologically in a patient folder, and are available for review at the time of data entry. Manual checks are performed on all CRFs when the DMs at ICR-CTSU transcribe data into the database. In-built database validations flag discrepancies to DMs s during data entry. Resulting queries are documented, printed and sent to sites periodically and several months can pass before query resolution occurs. Data entry of different forms at any one time means trends in errors in data reporting are not always easily identified. In EDC trials, participating sites enter data into an EDC system. Database validations highlight potential discrepancies to sites at the time of data entry so that they can correct issues immediately, reducing the number of data queries required.
DMs at ICR-CTSU receive daily automated emails listing newly completed electronic CRFs from the database tracking system. Manual review of specific forms is performed as required, for example, on trial entry forms and important safety and endpoint data. Only one form per patient can be open in the database for review at a time therefore DMs also use advanced data review software to programme checks which identify data discrepancies across forms for all trial participants. DMs run programmed checks at a frequency determined by their priority, to systematically identify potential discrepancies. Data are reviewed in context, and queries are raised electronically. These are immediately available for sites to review and provide a response. Trends in data entry errors can be readily identified during review of the programmed checks.
In light of these developments, the NIHR is developing a revised position on the sharing of Anonymised Individual Participant Data (IPD) generated by NIHR-funded research and a managed-access system to support this. This seeks to build upon and strengthen NIHR activity in this area and initially includes; Confirmation that anonymised datasets from NIHR funded research should be available for further analysis wherever possible. NIHR data will be released via a 'Managed Access' System, subject to data use agreement. NIHR protocols should contain a 'Data Sharing Plan' which will be publically available. NIHR publications must include a data sharing statement/access link which clearly explain how data can be requested. The NIHR is aware of the demands placed on researchers in this area, and the need to retain a focus on this area.
Results Of the 186941 trials in clinicaltrials. gov, 1567 (0. 8%) were studying one rare condition only and with prevalence information from Orphadata. There were 19 trials studying disease with prevalence <1/ 1,000,000, 126 trials with 1-9/1,000,000, 791 trials with 1-9/100,000 and 631 trials with 1-5/10,000. Of these, 1160 were described as phase 2 trials. All the covariates described above except type of lead sponsor were significant and thus, were included in the regression model. The estimated mean sample sizes in phase 2 trials were similar across all prevalence classes after adjusting for other covariates; 15. 7 (95% CI, 8. 7-28. 1), 26. 2 (16. 1-42. 6), 33. 8 (22. 1-51. 7) and 35. 6 (23. 3-54. 3) for prevalence <1/1,000,000, 1-9/1,000,000, 1-9/100,000 and 1-5/10,000, respectively. Phase 3 trials of rarer diseases (<1/ 1,000,000 and 1-9/1,000,000) had similar size (estimated mean, 19.
Methods The content of 59 questionnaires in the Database of Instruments for Resource-Use Measurement (www. dirum. org) was analysed to generate a list of 60 resource-use items relative to an NHS and personal social services perspective (e. g. Visits to healthcare professionals in the community; prescribed medications). An electronic Delphi survey was developed, and health economists with experience of conducting economic evaluations in the UK were recruited through personal approaches and a general request to the Health Economists Study Group mailing list (a professional group based in, though not restricted to, the UK). Respondents were asked to rate items on a scale of 1 to 9 according to the importance of the item in a generic context, and were encouraged to comment on their choices or add additional items.
The decision on treatment was at discretion of the patients and the clinicians based on standard of care. The information system and database were developed, maintained and managed by an information technology service provider and professional statistical and methodological terms Results As of Oct 31, 2016, 38 tertiary or secondary hospitals across China had participated in this registry, and totally122,987 patients and 486,976 visits were recorded. Among them 61. 9% were men; the average age at inclusion was 44. 3 years, with 67. 7% of them being between 30 and 60 years old. Approximately 6. 7% were initially diagnosed with immune tolerance phase of HBV infection, 68. 5% with chronic hepatitis B, 14% with cirrhosis (compensated 6. 3%; decompensated 7. 7%), 1. 8% with HCC, and 9% with others (including acute-on-chronic failure and recovery phase of HBV infection).
P269 A systematic review identifying neonatal core data elements for clinical trials, and assessment of data completeness of identified data elements using the national neonatal research database (NNRD) Sena Jawad, Daniel Gray, Neena Modi, Chris Gale Imperial College London Correspondence: Sena Jawad Trials 2017, 18(Suppl 1):P269 Background Point-of-care registry trials extract data from existing Electronic Patient Records (EPR). Poor accuracy and completeness of EPR data are key barriers to the success of such trials. Neonatal care in the United Kingdom has a well-developed EPR database, the National Neonatal Research Database (NNRD). We hypothesised that within neonatal clinical trials there are a subset of common data items reported across important trials, and aimed to assess the completeness of these items in the NNRD.
Conclusion High impact neonatal clinical trials share core data elements. In the UK these data can be obtained reliably from the NNRD, a well-established EPR-derived national database, supporting its use for point-of-care registry trials. is currently following 1,214 of the 1,441 originally randomized participants (94% of the surviving). During the 30 years of follow-up, continual efforts were made to modernize systems and data processing to improve efficiency and reduce cost. Methods Participants were administered a resting 12-lead ECG at each annual visit during DCCT/EDIC. Paper tracings and accompanying paper tracking forms were sent from the 27 clinics to the Data Coordinating Center (DCC), then forwarded in monthly batches to the Central ECG Reading Center (CERC) for analysis. In 2012 the paper tracking form was replaced with direct entry into a proprietary data management system.
P275 Using REDCAP's piping feature to aid in longitudinal data collection Jennifer Talton, Phillip Summers, Kathryn Melius, Thomas A. Arcury Wake Forest School of Medicine Correspondence: Jennifer Talton Trials 2017, 18(Suppl 1):P275 REDCAP (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies and is free to non-profit organizations who join the redcap Consortium. Its capabilities include the building of custom databases and surveys (that supports online or offline data capture); it also has data management features, such as participant scheduling calendars. Projects which do not need highly specialized data collection tools or have the resources for programmer support to create such tools can greatly benefit from REDCAP's features, which are continually being advanced.
Background The REVEAL study is a streamlined randomized controlled trial (RCT) investigating the effects of adding the CETP inhibitor anacetrapib to LDL-lowering treatment with atorvastatin among 30 000 study participants at high risk of vascular disease. It is coordinated by the CTSU at the University of Oxford (the trial Sponsor) with 6 Regional Coordinating Centres responsible for the conduct of the trial in 431 sites in 10 countries. Laptops are provided to all sites so that participant visit data can be captured directly into the electronic Case Report Form (ECRF) using software specifically designed for this purpose by CTSU. The laptop database therefore contains the source data. Methods Features of the laptop system used include: Consistency in laptop setup by utilising a Windows 7 image which also allows for regional variations where required.
Data entry (which can be done offline) directly into an ECRF provides immediate validation of data and a full audit trail, whilst site investigators retain access to their participants? Data held locally and securely both during and after the trial. The use of a 'thick client' rather than a web-browser provides resilience against internet outages whilst enabling timely data management across multiple countries and time zones. Potential drawbacks include reliance on a good internet connection for regular data transfer (ideally at least daily), central study databases not being updated in real time (although this is much less of a problem than systems that rely on transcription of data from paper forms), and restrictions on moving participant identifiable data out of some countries. In addition the provision of laptops can be expensive and resource intense requiring remote support and maintenance over many years.
P277 Tracking data requests from initiation to publication through the use of a web-enabled database system Michael Ham The Global Network's (GN) clinical trials produce rich data sets to inform investigators about treatments to prevent, diagnose, or treat human diseases. From these datasets, the clinical investigators publish results from their preplanned investigation to inform colleagues of the outcomes of the clinical trial. These same datasets can be used for secondary analyses to leverage the information available. All of these publications enrich the understanding of a disease state and possible therapies as well as serve as measurable deliverables, demonstrating productivity to the funding source. Therefore, we needed a system to track 1) data requests (for secondary analyses), 2) pre-publication development (includes all primary and secondary reports using a clinical trial dataset) and 3) post-publication tracking (citation counts, journal impact factor and author h-index).