Obesity in London 1700-1850: the evidence

This study explores the potential of macroscopic osteoarchaeological techniques to reveal the presence of obesity in 282 skeletons drawn from 1700-1850 London. Obesity-related pathology (diffuse idiopathic skeletal hyperostosis and distal-interphalangeal, knee and hip osteoarthritis) and bone geometry (femoral cross-sectional measurements and 1st lumbar vertebral area) are compared in assemblages of high and low status, with the hypothesis that those of high status were more likely to have had an obesogenic lifestyle than their lower status counterparts. It explores the reasons for studying the osteoarchaeology of obesity in skeletons, briefly investigating the extent of obesity in this historical and geographical context and its link with status. The study provides a history of obesity during the period, looking at the language used to describe it, how the medical profession understood it, and how the obese were viewed by wider society. Thereafter follows a literature review of the osteology of obesity, including examination of the clinical and archaeological research on body mass indicators. The thesis then describes the methodology employed in the study, along with detailed study questions and hypotheses. The four sites from which the skeletons were selected are then discussed and the historical context of life and burial in London given. There is an extensive presentation and discussion of the study’s results, including methods used to calculate prevalence and diagnostic criteria. The study found that DISH and femoral cross-sectional measurements show promise as obesity indicators, producing results consistent with those of higher status having a greater prevalence of obesity, although osteoarthritis and 1st lumbar vertebral area failed to indicate that those of higher status had a higher prevalence of obesity. In conclusion, recommendations are made regarding the calculation of prevalence, diagnostic criteria for DISH, and the need for larger sample sizes supported by large multi-site databases

Monitoring metrics over time: Why clinical trialists need to systematically collect site performance metrics

Background: Over the last decade, there has been an increasing interest in risk-based monitoring (RBM) in clinical trials, resulting in a number of guidelines from regulators and its inclusion in ICH GCP. However, there is a lack of detail on how to approach RBM from a practical perspective, and insufficient understanding of best practice. Purpose: We present a method for clinical trials units to track their metrics within clinical trials using descriptive statistics and visualisations. Research Design: We suggest descriptive statistics and visualisations within a SWAT methodology. Study Sample: We illustrate this method using the metrics from TEMPER, a monitoring study carried out in three trials at the MRC Clinical Trials Unit at UCL. Data Collection: The data collection for TEMPER is described in DOI: 10.1177/1740774518793379. Results: We show the results and discuss a protocol for a Study-Within-A-Trial (SWAT 167) for those wishing to use the method. Conclusions: The potential benefits metric tracking brings to clinical trials include enhanced assessment of sites for potential corrective action, improved evaluation and contextualisation of the influence of metrics and their thresholds, and the establishment of best practice in RBM. The standardisation of the collection of such monitoring data would benefit both individual trials and the clinical trials community

Lack of transparent reporting of trial monitoring approaches in randomised controlled trials: A systematic review of contemporary protocol papers

Background: Monitoring is essential to ensure patient safety and data integrity in clinical trials as per Good Clinical Practice. The Standard Protocol Items: Recommendations for Interventional Trials Statement and its checklist guides authors to include monitoring in their protocols. We investigated how well monitoring was reported in published ‘protocol papers’ for contemporary randomised controlled trials. Methods: A systematic search was conducted in PubMed to identify eligible protocol papers published in selected journals between 1 January 2020 and 31 May 2020. Protocol papers were classified by whether they reported monitoring and, if so, by the details of monitoring. Data were summarised descriptively. Results: Of 811 protocol papers for randomised controlled trials, 386 (48%; 95% CI: 44%–51%) explicitly reported some monitoring information. Of these, 20% (77/386) reported monitoring information consistent with an on-site monitoring approach, and 39% (152/386) with central monitoring, 26% (101/386) with a mixed approach, while 14% (54/386) did not provide sufficient information to specify an approach. Only 8% (30/386) of randomised controlled trials reported complete details about all of scope, frequency and organisation of monitoring; frequency of monitoring was the least reported. However, 6% (25/386) of papers used the term ‘audit’ to describe ‘monitoring’. Discussion: Monitoring information was reported in only approximately half of the protocol papers. Suboptimal reporting of monitoring hinders the clinical community from having the full information on which to judge the validity of a trial and jeopardises the value of protocol papers and the credibility of the trial itself. Greater efforts are needed to promote the transparent reporting of monitoring to journal editors and authors

P203: Lack of transparent reporting of trial monitoring approaches in randomised controlled trials: a systematic review of contemporary protocol papers

Background: Monitoring is essential to ensure patient safety and data integrity in clinical trials as per Good Clinical Practice. The SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) checklist guides authors to include monitoring in their protocols. We investigated the reporting prevalence and detail of monitoring in published “protocol papers” for contemporary randomised controlled trials (RCTs). Methods: A systematic search was conducted in PubMed to identify eligible protocol papers published in key journals. Articles were further classified by whether they reported monitoring. Descriptive data were summarised for the reporting prevalence of monitoring and the reporting extent. / Results: Of 811 protocol papers, 386 RCTs (48%; 95% CI: 44% to 51%) explicitly reported monitoring information. In particular, 20% (77/386) of RCTs reporting monitoring information described an approach consistent with on-site monitoring, 39% (152/386) with central monitoring, 26% (101/386) with a mixed approach, whilst 14% (54/386) did not provide sufficient information to specify an approach. Only 8% (30/386) of RCTs reported complete details about scope, frequency and organisation, and the monitoring frequency was the least reported. Moreover, 6% (25/386) of protocol papers interchangeably used “audit” to describe “monitoring”. / Discussion: Monitoring information was only reported in half of the published protocols. Suboptimal reporting of monitoring, as shown in this study, hinders the clinical community from having the full information on which to judge the validity of a trial externally and jeopardises the value of protocol papers and trial credibility. Greater efforts are needed to promote the transparent reporting of monitoring to journal editors and authors

Access to routinely collected health data for clinical trials - review of successful data requests to UK registries.

BACKGROUND: Clinical trials generally each collect their own data despite routinely collected health data (RCHD) increasing in quality and breadth. Our aim is to quantify UK-based randomised controlled trials (RCTs) accessing RCHD for participant data, characterise how these data are used and thereby recommend how more trials could use RCHD. METHODS: We conducted a systematic review of RCTs accessing RCHD from at least one registry in the UK between 2013 and 2018 for the purposes of informing or supplementing participant data. A list of all registries holding RCHD in the UK was compiled. In cases where registries published release registers, these were searched for RCTs accessing RCHD. Where no release register was available, registries were contacted to request a list of RCTs. For each identified RCT, information was collected from all publicly available sources (release registers, websites, protocol etc.). The search and data extraction were undertaken between January and May 2019. RESULTS: We identified 160 RCTs accessing RCHD between 2013 and 2018 from a total of 22 registries; this corresponds to only a very small proportion of all UK RCTs (about 3%). RCTs accessing RCHD were generally large (median sample size 1590), commonly evaluating treatments for cancer or cardiovascular disease. Most of the included RCTs accessed RCHD from NHS Digital (68%), and the most frequently accessed datasets were mortality (76%) and hospital visits (55%). RCHD was used to inform the primary trial (82%) and long-term follow-up (57%). There was substantial variation in how RCTs used RCHD to inform participant outcome measures. A limitation was the lack of information and transparency from registries and RCTs with respect to which datasets have been accessed and for what purposes. CONCLUSIONS: In the last five years, only a small minority of UK-based RCTs have accessed RCHD to inform participant data. We ask for improved accessibility, confirmed data quality and joined-up thinking between the registries and the regulatory authorities. TRIAL REGISTRATION: PROSPERO CRD42019123088

What is the purpose of clinical trial monitoring?

Background: The sources of information on clinical trial monitoring do not give information in an accessible language and do not give detailed guidance. In order to enable communication and to build clinical trial monitoring tools on a strong easily communicated foundation, we identified the need to define monitoring in accessible language. Methods: In a three-step process, the material from sources that describe clinical trial monitoring were synthesised into principles of monitoring. A poll regarding their applicability was run at a UK national academic clinical trials monitoring meeting. Results: The process derived 5 key principles of monitoring: keeping participants safe and respecting their rights, having data we can trust, making sure the trial is being run as it was meant to be, improving the way the trial is run and preventing problems before they happen. Conclusion: From the many sources mentioning monitoring of clinical trials, the purpose of monitoring can be summarised simply as 5 principles. These principles, given in accessible language, should form a firm basis for discussion of monitoring of clinical trials

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p

Getting our ducks in a row: The need for data utility comparisons of healthcare systems data for clinical trials

Background: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. “Data Utility Comparison Studies” (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS. // Methods-and-Results: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at “patient-level” or “trial-level”, depending on the item of interest and trial status. // Discussion: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p

Early warnings and repayment plans: novel trial management methods for monitoring and managing data return rates in a multi-centre phase III randomised controlled trial with paper Case Report Forms

Abstract Background Monitoring and managing data returns in multi-centre randomised controlled trials is an important aspect of trial management. Maintaining consistently high data return rates has various benefits for trials, including enhancing oversight, improving reliability of central monitoring techniques and helping prepare for database lock and trial analyses. Despite this, there is little evidence to support best practice, and current standard methods may not be optimal. Methods We report novel methods from the Trial of Imaging and Schedule in Seminoma Testis (TRISST), a UK-based, multi-centre, phase III trial using paper Case Report Forms to collect data over a 6-year follow-up period for 669 patients. Using an automated database report which summarises the data return rate overall and per centre, we developed a Microsoft Excel-based tool to allow observation of per-centre trends in data return rate over time. The tool allowed us to distinguish between forms that can and cannot be completed retrospectively, to inform understanding of issues at individual centres. We reviewed these statistics at regular trials unit team meetings. We notified centres whose data return rate appeared to be falling, even if they had not yet crossed the pre-defined acceptability threshold of an 80% data return rate. We developed a set method for agreeing targets for gradual improvement with centres having persistent data return problems. We formalised a detailed escalation policy to manage centres who failed to meet agreed targets. We conducted a post-hoc, descriptive analysis of the effectiveness of the new processes. Results The new processes were used from April 2015 to September 2016. By May 2016, data return rates were higher than they had been at any time previously, and there were no centres with return rates below 80%, which had never been the case before. In total, 10 centres out of 35 were contacted regarding falling data return rates. Six out of these 10 showed improved rates within 6–8 weeks, and the remainder within 4 months. Conclusions Our results constitute preliminary effectiveness evidence for novel methods in monitoring and managing data return rates in randomised controlled trials. We encourage other researchers to work on generating better evidence-based methods in this area, whether through more robust evaluation of our methods or of others