dataesr/scientific-paragraphs-categorization huggingface dataset:
These challenges were addressed by looking at a number of offline tracking systems that had failed to meet the needs of various clinical networks, and finding ways to improve upon them. An extensible. NET module was developed to store and display all relevant data for publications tracking. Web services were employed to connect the system to popular publication data stores such as The United States National Library of Medicine's PubMed database, Thomson Reuters's Web of Science database, and the International DOI Foundation's digital object identifier (DOI) to retrieve and store accurate information about publications at any point in the creation process. The presentation will discuss the different technical challenges of this approach and how they were resolved. Pros and cons of the approach will also be discussed along with methods used to report information out of the system.
Additionally, participant interview data may be used in real time for presentation to TMG and TSC meetings to allow timely protocol amendments in order to improve recruitment and retention of participants. This concurrent approach is being successfully used in an HTA funded, ongoing clinical trial (ROCS: NCT01915693), and has enabled protocol amendments, such as timing of recruitment and assessments, that have improved recruitment and improved data capture. Aims An integrated qualitative component within an adaptive design trial can inform the primary and secondary trial objectives in the recruitment and testing phases of the trial and, for the purposes of an adaptive MAMS design, the stop/keep criteria per arm by assessing, for example: The feasibility of patients; Recruitment to the trial by examining their experience of consent and recruitment, including reasons for declining participation; Participants' motivation to accept randomisation to different interventions; Potential improvements to recruitment processes; Participants' understanding and experience of each trial arm; Participants' experience and tolerability of toxicities; Participants' attitudes to the value of predicted treatment benefit.
Methods We piloted at two sites prior to the first main recruitment wave, and further optimised recruitment strategy in initial main trial waves. Strategies introduced both preceded and followed the piloting phase. These included 1) supplementing postal approaches to carers from their respective Local Authority (LA) or Independent Fostering Provider (IFP) by asking LAs and IFPs to select eligible carers from their databases for direct invitation, 2) increasing lead time from approaching provider to group initiation to up to four months, 3) modifying the allocation ratio from 1:1 to 2:1 (intervention:control) to increase the chances of obtaining the required training group size, 5) revising participant materials to better align with the expectations of foster carers.
In other words, might the effects of different treatments on a mediator (a paths or action theories) be associated with different mediator-outcome relationships (b paths or conceptual theories)? Methods Longitudinal structural equation models (SEM) for mediation were applied to two-arm subsets and the overall four-arm trial dataset to study longitudinal mediation of the effects of the PACE trial treatments. Fear avoidance (FA) was used as an example mediator and physical functioning (PF) as an example outcome  [2]. A single model was fitted to the dataset in each case with the pertinent contrasts obtained. Treatment by mediator interaction terms were used to assess differences in mediator-outcome effects (conceptual theories) for different treatments. Informal comparisons of the standard errors were used to assess precision.
Results The multiple arms/explanatory design combination provided both practical and statistical advantages. From the practical point of view: A) the results from several two arm trials were obtained from one trial, B) an explanatory design meant this was true for important secondary analyses as well, C) this particular design allowed for comparisons between active treatments and with the specialised medical care comparison arm. One statistical advantage was increased power. This may have been especially important for the mediation analysis, as there is often low power to detect mediated effects  [3]  and some useful methods for studying mediation suffer from lower precision. For example, there was 25% gain in the precision of the parameter estimate for the mediator-outcome relationship in the full four arm dataset as compared to the two arm subset.
Despite these advantages, PTFU is not routinely undertaken and, when implemented, methods vary widely. This review describes methods of PTFU used in recent large randomised trials, and will compare retention rates and study costs where such information is available, and may help promote the use of effective PTFU for ongoing and future large trials. Methods A systematic search of electronic databases and clinical trial registries was conducted using a pre-defined search strategy with the following inclusion criteria: i) randomized trials with 1000 or more participants, ii) published between March 2006-2016; iii) evaluation of medical, surgical or psychological interventions; iv) implementation of post-trial follow-up reported. Two reviewers screened and extracted data from eligible papers with the aim of 95% concordance and any discrepancies were resolved by a third reviewer.
Results The search strategy incorporated relevant papers from Cochrane Central Register, Embase, Medline and clinical trial registries yielding 50,153 papers from databases (49,915) and trial registries (218). After excluding duplicates (22,168), studies of children and animals (1649) and papers published before 2006 (9289). 17,027 abstracts were screened by 2 reviewers using a concordance strategy. Reviewers were 73% concordant for the first 10% of abstracts screened, but after discussion concordance rose to 99%. Following abstract screening, 239 papers and 218 protocols were eligible for full review and preliminary results suggest that around half will represent unique studies with relevant data to extract in the review. The length of PTFU ranged from 1-20 years and PTFU methods varied, including direct patient contact via clinic appointments, postal questionnaires, telephone interviews and indirect follow-up via national registries.
Results At the Birmingham Clinical Trials Unit (BCTU) at the University of Birmingham, we have developed a clinical trials clerkship where fellows spend on average 15 days (including a 3 day Research Methods Course) in BCTU over a 12 month period. The fellows will have the opportunity to learn about various trial processes from study set-up, protocol and case report form development, regulatory requirements, trial management, database development, statistical aspects of clinical studies, recruitment strategies, data management and monitoring, trial steering committees, interim and final data analysis and submitting for publication. Each fellow is assigned a senior trialist who acts as their mentor, who is responsible for working with the fellow to tailor the training and learning experience.
The importance of translation of clinical trials into catchment populations of Cancer Centers coupled with the advent of molecularly targeted agents and emphasis in precision medicine resulting in smaller patient pool within a single institution entail the need to engage multiple sites for the design and implementation of clinical trials. The conduct of multi-center studies is necessarily complex, requiring informatics tools and data management processes that need a coordinated effort necessitating an infrastructure akin to Data Coordinating Centers. We present a model whereby biostatistics and informatics core facilities partner with community based research networks to manage multi-center clinical trials. More specifically, we focus on three critical areas in informatics and data management namely i) development of an integrated set of standard operating procedures (SOPs) between the community based network and MRU pertaining to all aspects of data management; ii) improving utilization of a clinical trial management system (CTMS), a biospecimen management system and customized database applications to accommodate multi-center studies and iii) adopting and expanding automated statistical programs to monitor protocol-specific triggers including subject accrual, safety, and efficacy endpoints into a multiple site setting.
P359 Database through an iterative process of regression analysis and reassessment. The coefficients of the characteristics (including age, smoking status, comorbidities (rhinitis, diabetes, ischaemic heart disease, anxiety and/or depression, and anaphylaxis), BTS treatment step, paracetamol treatment, lower respiratory tract infection, oral corticosteroid therapy or hospitalisation in the previous year, body mass index and blood eosinophil count) then are used in an algorithm to calculate a risk score which was validated in a second database, the Secure Anonymised Information Linkage databank. Implementing an At-Risk Register Using this algorithm, the ARRISA-UK search tool identifies at-risk individuals in general practices. Search reports from the GP clinical database system for the characteristics above are analysed, and the risk assessment is flagged in relevant patient records via specific Read or SNOMED codes.
Project development System specifications were then prepared collaboration with a data systems developer. A relational database design and (Oracle) written in Java was used for development. After an initial prototype was developed the system was released for user testing by trials staff from the discrete functions within the trials teams. Once the system was qualified, but prior to rollout, existing study tracking data was imported into the new CTMS system. Reports (both within and across projects) were developed prior to release to enable staff to access key information.
This presentation will briefly discuss these activities, which are aimed to address the clinical trials process from the time new ideas are generated to sharing data to the public. The initiatives covers NIH review and selection of trials to fund, clinical trials management and oversight, and data sharing. More specifically, this presentation will discuss the variety of new NIH policies, including Good Clinical Practice (GCP) training requirements for investigator and NIH staff, using clinical trials specific Funding Opportunity Announcements (FOAs), including appropriate expertise to review sessions, using a protocol template (required for FDA studies), using a single Institution Review Board (SIRB), and utilizing clinicaltrials. Gov to register and upload results.
P378 Cost effective interactive live clinical data monitoring dashboards with drill-down Amarnath Vijayarangan Emmes Services Pvt Ltd. Trials 2017, 18(Suppl 1):P378 Periodical data review is very important and highly recommended for all the ongoing clinical studies to ensure the data integrity and quality. Each clinical study requires experts from various functional groups like SAS programming, Biostatistics, Data Management and so on. Each one of them have various data review requirements and also one cannot expect everyone to familiar with SAS programming as clinical datasets are often available as SAS datasets. Statisticians prefer summary level data whereas others might need to look at the summary level as well as granular level data. These reports are static and hence end users do not have any choice to customize or drill down the reports on their own.
The Source data path changes of Pivot reports while moving the reports Solution: There is a wide discussion can be seen in various online forums on how to solve the pivot source data path changes while moving the files with pivot tables. None of the solutions discussed online are simple and reliable. The simplest solution is open the file and using SAVE AS option save it in the desired location instead of the coping the master dashboard. How simple it is. 3. Pivot based reports are static with the number of rows in excel. But for an ongoing study, this will be increasing and every time user cannot change their reports for the rows. It becomes tedious to manually do the same on several reports and it is error prone. Solution: While creating the pivot reports, 50000 rows are selected and Whenever any SAS dataset exceeds 50000 rows, email notification is sent.
Methods The NIHR HTA Tranexamic acid for intracerebral Haemorrhage (TICH-2) trial is a randomised, controlled, international, multicentre trial aiming to recruit 2,000 patients. Data volume in such a trial is large, and having numerous centres makes solving data queries time consuming. The database in TICH-2 has built in logic checks allowing real time validation and this identifies most but not all data inconsistencies. A program was created that looks at the data and checks it for values which lie outside the normal ranges or are inconsistent between different data fields, this picks up queries that the real time validation missed to ensure data is as complete as possible. The checks are run for one centre at a time and sent to trial coordinators, within the trial coordinating centre, to go through them and resolve with sites.
For the REST internal pilot the following analyses will be completed: The overall recruitment rate will be compared to target recruitment of 7 per month. Analysis of separation in terms of tidal volume (ml/kg predicted bodyweight) between the two arms will be undertaken on an intention to treat and a per protocol basis including the patients in receipt of VV-ECCO2R on day 2 and 3. Completeness of datasets with respect to the primary outcome measure of 90 day mortality will be assessed for patients recruited in the first 3 months. Like a full trial SAP, the SAP for the internal pilot was reviewed by the Data Monitoring and Ethics Committee and the Trial Management Group. Discussion A formal SAP for an internal pilot is not standard clinical trial methodology and there are pros and cons to having one which are open for discussion.
Method The Glasgow Safe Haven is a collaboration between University of Glasgow (UOG) and Greater Glasgow & Clyde Health Board. Approval is sought from a local privacy advisory committee for access to nonconsented data on a per research project basis. Once approved, the data are extracted, anonymised and placed on an analytical platform hosted by UOG where researchers can then perform their analysis in a strictly monitored environment. In order to become a "safe haven" analytical platform, certain requirements need to be adhered to. Security is paramount and for very sensitive datasets the creation and use of a "safe room" is required.
The entire study drug shipping process, from the central pharmacy to the clinical sites, including optional multiple regional drug depots, is tracked and information is shared among collaborators in real time. Central and local pharmacy staff are notified by automated emails when study drug distribution actions are requested. This module has been implemented in 8 multicenter trials, and received very positive feedback from investigators. This presentation will discuss the database design and implementation of the automated study drug distribution coordination module in the clinical trial management system.
Challenges CTC-STOP blood samples are received and processed by the central laboratory. CTC counts are recorded in the central laboratory' s information management system (LIMS). The LIMS cannot be programmed to calculate or alert staff to CTC progressions and the ICR-CTSU cannot access LIMS. CTC counts are required by the ICR-CTSU trial team for CTC progression calculations within the Electronic Data Capture (EDC) system, so treatment switch recommendations can be issued promptly. Manual data re-entry of the CTC counts into an EDC database would be time consuming and could result in transcription errors; therefore a system was designed to automatically import the CTC counts from the LIMS into the EDC system. Solution An in-house C#. net application was developed by ICR-CTSU to verify and import CTC counts provided by the central laboratory LIMS.
The application locates and verifies the LIMS export file before validating the subject identifiers and trial visit timepoint for each CTC count. Once validated, the data is imported into the EDC system using its Application Programming Interface. The application runs automatically every day via Windows Task Scheduler and generates a log file for each export file to detail any errors found during processing and the number of data rows successfully imported into the EDC system. When the application has completed processing the log file is emailed to the ICR-CTSU trial team for review. Details of each export file processed are also written to a database history table. The CTC counts imported into the EDC system are used to perform computations to ascertain whether a treatment switch is required by calculating whether each subject has progressed or responded.
Methods We reviewed all AE reported in two clinical trials performed in our clinical trials network, protect (NCT00822900) and ATACH (NCT01176565) both of which were reported in the New England Journal of Medicine. For each trial, a splintered and collapsed list of PT were compared. Protect published the collapsed list and ATACH the splintered list. Splintered lists were generated primarily by autocoding, with manual coding by a data manager during the conduct of the trial when autocoding failed. Collapsed lists were generated from the splintered lists using clinical judgement by the trial investigators in protect at the end of the trial. For ATACH, the collapsed list was generated in part by investigators at the end of the trial and in part by the authors of this abstract. All splintered and collapsed lists used only meddra PT.
Results Three registries were included and searched; clinicaltrials. gov, International Standard Randomised Controlled Trial Number Register (ISRCTN) and World Health Organisation (WHO) International Clinical Trials Registry Platform (ICTRP). The initial search in 2013 found an extremely small proportion of the 382,944 trials being carried out worldwide were utilising qualitative methods. The overall percentage of registered trials confirmed as including qualitative methods was less than 0. 2%. This number appears to have increased over time, but is limited to 'developed countries' such as the U. K. and U. S. A. Most trials using qualitative methods appeared to be non-clinical, and were mostly testing behavioural interventions (87%). Of the small percentage of those trials which appeared clinical in nature, drug trials appeared to utilise qualitative methods more than either medical device or surgical trials (7%, 4%, 2% respectively).
Methods The screening tool was developed based on the principles of prognostic model development, using data from the KAT randomized clinical trial  [1]  which contains pre-operative and 12 months outcome data on more than 2,192 KR patients. As a proxy for poor outcome, since the KAT trial did not include the LLFDI score, poor outcome was defined as a score 26 in the Oxford Knee Score (OKS). Pre-operative characteristics considered as candidate predictors in the development of the screening tool included age, sex, height, body mass index (BMI), mobility, ASA grade, SF-12 (questions 6 and 11) and OKS (question 1) components. Multivariate imputation by chained equations (MICE) was used to handle missing data in the KAT dataset. Ten complete datasets were produced by MICE. One of these datasets was selected at random and multivariable logistic regression models were fitted to identify the statistically significant predictors of poor outcome after KR.
Results Subjects in the KAT dataset were aged 71(SD = 7. 1) years on average, with a mean ASA grade of 2(SD = 0. 6). From a total set of nine candidate predictors, four were selected for the screening tool: BMI, ASA grade, OKS question 1 and SF-12 question 6. Model discrimination, as measured by the c-index was 0. 67. The screening tool score range is 0-10 and patients scoring 5 or more (29% of the KAT sample) are considered at increased risk of poor outcome following KR.
Search strategy The ORRCA (Online Resource for Recruitment Research in Clinical Trials; www. orrca. org. uk) database was utilised in this systematic review. ORRCA includes studies of all designs, systematically extracted from the literature, reporting on recruitment into RCTs and nonrandomised clinical studies. In this review, ORRCA was searched for primary research reports of studies that reported on recruitment to RCTs in adult patients receiving UHC. Development of study categories Reading the articles led to initial categorisation of the recruitment studies into those with a randomised or non-randomised recruitment designs. Iterative refinement of the study structured categories through discussion between study authors (CR, JMB, LR, JR). It was noted that papers reporting surveys in the community (community consultations) had been undertaken to establish the likelihood of recruitment success or acceptability of a trial.
Methods We illustrate with both real and simulated datasets that the use of log transformed data when the data naturally bounded is not always appropriate. Particularly in randomised studies, it is often the case that a log transformation may be suitable for one, but not both arms of the study. Further, interpretations of the data are typically dependent on differences between means and may ignore changes in variation. We show how beta regression and fractional logistic/probit modelling directly relate to the original (untransformed) scale can account for shifts in both location and spread. We also provide suggestions on sample size estimation. Conclusions Analysis of phase II trials that use continuous fractional outcomes should reflect the underlying nature of the data recorded. We hope to have increased researchers' awareness of better methodology that will enhance comparative analysis, and to provide suggestions for statistical colleagues who may be asked to perform such work.
Methods The project will update the systematic review of  Barnard et al. (2010). This update will include methods identified and published from August 2008 until present. The Online Resource for Recruitment Research in Clinical Trials database (ORRCA) will be used to identify relevant literature. Newly identified methods will be assessed for eligibility. Methods will be assigned to existing proposed classifications of unconditional, conditional, Poisson, Bayesian and Monte Carlo Simulation Markov Model with new classifications as appropriate. The assumptions made by each method will be identified and compared between models. Levels of information required to implement the models, will be considered and applied to real examples of ongoing or recently completed clinical trials.
For instance, forest plots provide no insight on the overlap of different subgroups; additionally, whether or not a subgroup's confidence interval crosses the no-effect point does not necessarily imply a lack of effect or contribute an effect to the subgroup. It is therefore crucial to correctly depict the effect sizes and information, particularly in order to prevent overstating effects. To develop an optimal visualization approach, we assessed graphical approaches for subgroup analysis under a synthetic dataset. Several techniques (such as level plots, barcharts, Venn diagrams, tree plots, forest plots and matrix plots) were applied to exhibit certain subgroup information. Some have been further improved by mitigating their original demerits. In final, we summarise the general strengths and failings of the graphical approaches and outline potential visualisation techniques.