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Background: Central monitoring aims at improving the quality of clinical research by pro-actively identifying risks and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. This paper, focusing on statistical data monitoring (SDM), is the second of a series that attempts to quantify the impact of central monitoring in clinical trials. Material and Methods: Quality improvement was assessed in studies using SDM from a single large central monitoring platform. The analysis focused on a total of 1111 sites that were identified as at-risk by the SDM tests and for which the study teams conducted a follow-up investigation. These sites were taken from 159 studies conducted by 23 different clinical development organizations (including both sponsor companies and contract research organizations). Two quality improvement metrics were assessed for each selected site, one based on a site data inconsistency score (DIS, overall -log10P-value of the site compared with all other sites) and the other based on the observed metric value associated with each risk signal. Results: The SDM quality metrics showed improvement in 83% (95% CI, 80–85%) of the sites across therapeutic areas and study phases (primarily phases 2 and 3). In contrast, only 56% (95% CI, 41–70%) of sites showed improvement in 2 historical studies that did not use SDM during study conduct. Conclusion: The results of this analysis provide clear quantitative evidence supporting the hypothesis that the use of SDM in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Central monitoring, which typically includes the use of key risk indicators (KRIs), aims at improving the quality of clinical research by pro-actively identifying and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. However, there has to-date been a relative lack of direct quantitative evidence published supporting the claim that central monitoring actually leads to improved quality. Material and Methods: Nine commonly used KRIs were analyzed for evidence of quality improvement using data retrieved from a large central monitoring platform. A total of 212 studies comprising 1676 sites with KRI signals were used in the analysis, representing central monitoring activity from 23 different sponsor organizations. Two quality improvement metrics were assessed for each KRI, one based on a statistical score (p-value) and the other based on a KRI's observed value. Results: Both KRI quality metrics showed improvement in a vast majority of sites (82.9% for statistical score, 81.1% for observed KRI value). Additionally, the statistical score and the observed KRI values improved, respectively by 66.1% and 72.4% on average towards the study average for those sites showing improvement. Conclusion: The results of this analysis provide clear quantitative evidence supporting the hypothesis that use of KRIs in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites. [ABSTRACT FROM AUTHOR]

In some hospitals, the infusion is still done manually, where medical personnel observe the liquid droplets directly and then control the rate using a mechanical resistor (clamp). This method is certainly far from accurate. An infusion pump is a medical aid that functions to control and ensure the correct dose of infusion fluid given to patients treated. The purpose of this study was to analyze the accuracy of the infrared photodiode as a drop sensor based on the readings of the infusion pump monitoring system. This module consists of a photodiode infrared drop sensor module, a comparator circuit, a monostable circuit, a stepper motor, an L298N motor driver, and an ATmega328 microcontroller. The droplets were detected by an infrared photodiode sensor, then compared with a comparator and monostable circuit as an oscillator developer, and then the flow rate and residual volume readings were generated by the ATmega328 microcontroller. Next, this data has sent to the computer via the HC-11 wireless. The results of the flowrate module measurement show the highest error value of 3% at the 30 ml/hour setting and the lowest error value of 2.5% at the 60 ml/hour setting. Meanwhile, the results of the flow rate measurement using an infusion device analyzer obtained the highest error value of 4% at the setting of 30 ml/hour and 60 ml/hour, and the lowest error value of 0.8% at the setting of 100 ml/hour. Monitoring the infusion pump was designed centrally to facilitate the nurse's task in monitoring the infusion dose given to the patient accurately. Based on this research, the accuracy of the infrared sensor and photodiode is very good by looking at the existing error rate.

In some hospitals the infusion is still done manually, medical staff observes fluid drip directly and then controls its rate using a mechanical resistor (clamp), this method is certainly far from the level of accuracy. Infusion pump is a medical aid that has functions to control and ensure the correct dose of infusion fluid that is given to patients under treatment. The purpose of this study is to analyze the accuracy of the TCRT5000 as a drop sensor, based on readings of the infusion pump monitoring system. This module consists of a TCRT5000 drop sensor module, comparator circuit, monostable circuit, stepper motor, L298N motor driver, and ATmega328 microcontroller. The droplets are detected by the TCRT 5000 sensor, then amplified by a comparator and monostable circuit, then the flow rate and remaining volume readings are generated by the ATmega328 microcontroller. Furthermore, this data is sent to the Personal Computer (PC) via wireless HC-11. The results of the flow rate module measurement show that the highest error value is 4% at the 30 ml/hour setting, and the lowest error value is 1% at the 60 ml/hour setting. While the results of the flow rate measurement using an Infuse Device Analyzer, the highest error value is 2,2% at the 30 ml/hour setting, and the lowest error value is 0,58% at the 100 ml/hour setting. This infusion pump monitoring is designed centrally to facilitate the nurse's task in monitoring the infusion dose accurately that is given to the patient.

Abstract Background Data monitoring of clinical trials is a tool aimed at reducing the risks of random errors (e.g. clerical errors) and systematic errors, which include misinterpretation, misunderstandings, and fabrication. Traditional ‘good clinical practice data monitoring’ with on-site monitors increases trial costs and is time consuming for the local investigators. This paper aims to outline our approach of time-effective central data monitoring for the SafeBoosC-III multicentre randomised clinical trial and present the results from the first three central data monitoring meetings. Methods The present approach to central data monitoring was implemented for the SafeBoosC-III trial, a large, pragmatic, multicentre, randomised clinical trial evaluating the benefits and harms of treatment based on cerebral oxygenation monitoring in preterm infants during the first days of life versus monitoring and treatment as usual. We aimed to optimise completeness and quality and to minimise deviations, thereby limiting random and systematic errors. We designed an automated report which was blinded to group allocation, to ease the work of data monitoring. The central data monitoring group first reviewed the data using summary plots only, and thereafter included the results of the multivariate Mahalanobis distance of each centre from the common mean. The decisions of the group were manually added to the reports for dissemination, information, correcting errors, preventing furture errors and documentation. Results The first three central monitoring meetings identified 156 entries of interest, decided upon contacting the local investigators for 146 of these, which resulted in correction of 53 entries. Multiple systematic errors and protocol violations were identified, one of these included 103/818 randomised participants. Accordingly, the electronic participant record form (ePRF) was improved to reduce ambiguity. Discussion We present a methodology for central data monitoring to optimise quality control and quality development. The initial results included identification of random errors in data entries leading to correction of the ePRF, systematic protocol violations, and potential protocol adherence issues. Central data monitoring may optimise concurrent data completeness and may help timely detection of data deviations due to misunderstandings or fabricated data.

Abstract The COVID-19 pandemic has affected how clinical trials are managed, both within existing portfolios and for the rapidly developed COVID-19 trials. Sponsors or delegated organisations responsible for monitoring trials have needed to consider and implement alternative ways of working due to the national infection risk necessitating restricted movement of staff and public, reduced clinical staff resource as research staff moved to clinical areas, and amended working arrangements for sponsor and sponsor delegates as staff moved to working from home. Organisations have often worked in isolation to fast track mitigations required for the conduct of clinical trials during the pandemic; this paper describes many of the learnings from a group of monitoring leads based in United Kingdom Clinical Research Collaboration (UKCRC) Clinical Trials Unit (CTUs) within the UK. The UKCRC Monitoring Task and Finish Group, comprising monitoring leads from 9 CTUs, met repeatedly to identify how COVID-19 had affected clinical trial monitoring. Informed consent is included as a specific issue within this paper, as review of completed consent documentation is often required within trial monitoring plans (TMPs). Monitoring is defined as involving on-site monitoring, central monitoring or/and remote monitoring. Monitoring, required to protect the safety of the patients and the integrity of the trial and ensure the protocol is followed, is often best done by a combination of central, remote and on-site monitoring. However, if on-site monitoring is not possible, workable solutions can be found using only central or central and remote monitoring. eConsent, consent by a third person, or via remote means is plausible. Minimising datasets to the critical data reduces workload for sites and CTU staff. Home working caused by COVID-19 has made electronic trial master files (TMFs) more inviting. Allowing sites to book and attend protocol training at a time convenient to them has been successful and worth pursuing for trials with many sites in the future. The arrival of COVID-19 in the UK has forced consideration of and changes to how clinical trials are conducted in relation to monitoring. Some developed practices will be useful in other pandemics and others should be incorporated into regular use.

Central fetal monitoring systems transmit cardiotocograph data to a central site in a maternity service. Despite a paucity of evidence of safety, the installation of central fetal monitoring systems is common. This qualitative research sought to explore whether, and how, clinicians modified their clinical safety related behaviours following the introduction of a central monitoring system. An Institutional Ethnographic enquiry was conducted at an Australian hospital where a central fetal monitoring system had been installed in 2016. Informants (n = 50) were midwifery and obstetric staff. Data collection consisted of interviews and observations that were analysed to understand whether and how clinicians modified their clinical safety related behaviours. The introduction of the central monitoring system was associated with clinical decision making without complete clinical information. Midwives' work was disrupted. Higher levels of anxiety were described for midwives and birthing women. Midwives reported higher rates of intervention in response to the visibility of the cardiotocograph at the central monitoring station. Midwives described a shift in focus away from the birthing woman towards documenting in the central monitoring system. The introduction of central fetal monitoring prompted new behaviours among midwifery and obstetric staff that may potentially undermine clinical safety. This research raises concerns that central fetal monitoring systems may not promote safe intrapartum care. We argue that research examining the safety of central fetal monitoring systems is required. [ABSTRACT FROM AUTHOR]

Background: A central statistical assessment of the quality of data collected in clinical trials can improve the quality and efficiency of sponsor oversight of clinical investigations. Material and Methods: The database of a large randomized clinical trial with known fraud was reanalyzed with a view to identifying, using only statistical monitoring techniques, the center where fraud had been confirmed. The analysis was conducted with an unsupervised statistical monitoring software using mixed-effects statistical models. The statistical analyst was unaware of the location, nature, and extent of the fraud. Results: Five centers were detected as atypical, including the center with known fraud (which was ranked 2). An incremental analysis showed that the center with known fraud could have been detected after only 25% of its data had been reported. Conclusion: An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials. [ABSTRACT FROM AUTHOR]

Abstract Background Despite the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) encouraging the use of risk-based monitoring for trials in 2013, there remains a lack of evidence-based guidelines on how to monitor. We surveyed the academic United Kingdom Clinical Research Collaboration (UKCRC) registered clinical trials units (CTUs) to find out their policy on monitoring of phase III randomised clinical trials of an investigational medicinal product (CTIMPs). Methods An online survey of monitoring policy with sections on the CTU, central monitoring and on-site monitoring was sent to all 50 UKCRC registered CTUs in November 2018. Descriptive data analysis and tabulations are reported using the total number answering each question. Results A total of 43/50 (86%) of CTUs responded with 38 conducting phase III randomised CTIMP trials. Of these 38 CTUs, 34 finished the survey. Most CTUs (36/37, 97%) use a central monitoring process to guide, target or supplement site visits. More than half (19/36, 53%) of CTUs do not use an automated monitoring report when centrally monitoring trials and all units use trial team knowledge to make a final decision on whether an on-site visit is required. A total of 31/34 (91%) CTUs used triggers to decide whether or not to conduct an on-site monitoring visit. On-site, a mixture of source data verification and checking of processes was carried out. The CTUs overwhelmingly (27/34, 79%) selected optimising central monitoring as their most pressing concern. Conclusion The survey showed a wide variation in phase III randomised CTIMP trial monitoring practices by academic clinical trials units within a single research-active country. We urgently need to develop evidence-based regulator-agreed guidance for CTUs on best practice for both central and on-site monitoring and to develop tools for all CTUs to use.

Background: Data monitoring of clinical trials is a tool aimed at reducing the risks of random errors (e.g. clerical errors) and systematic errors, which include misinterpretation, misunderstandings, and fabrication. Traditional 'good clinical practice data monitoring' with on-site monitors increases trial costs and is time consuming for the local investigators. This paper aims to outline our approach of time-effective central data monitoring for the SafeBoosC-III multicentre randomised clinical trial and present the results from the first three central data monitoring meetings.Methods: The present approach to central data monitoring was implemented for the SafeBoosC-III trial, a large, pragmatic, multicentre, randomised clinical trial evaluating the benefits and harms of treatment based on cerebral oxygenation monitoring in preterm infants during the first days of life versus monitoring and treatment as usual. We aimed to optimise completeness and quality and to minimise deviations, thereby limiting random and systematic errors. We designed an automated report which was blinded to group allocation, to ease the work of data monitoring. The central data monitoring group first reviewed the data using summary plots only, and thereafter included the results of the multivariate Mahalanobis distance of each centre from the common mean. The decisions of the group were manually added to the reports for dissemination, information, correcting errors, preventing furture errors and documentation.Results: The first three central monitoring meetings identified 156 entries of interest, decided upon contacting the local investigators for 146 of these, which resulted in correction of 53 entries. Multiple systematic errors and protocol violations were identified, one of these included 103/818 randomised participants. Accordingly, the electronic participant record form (ePRF) was improved to reduce ambiguity.Discussion: We present a methodology for central data monitoring to optimise quality control and quality development. The initial results included identification of random errors in data entries leading to correction of the ePRF, systematic protocol violations, and potential protocol adherence issues. Central data monitoring may optimise concurrent data completeness and may help timely detection of data deviations due to misunderstandings or fabricated data. [ABSTRACT FROM AUTHOR]

Background: Risk-based monitoring (RBM) is a slow uptake in some trial sponsors. There are three main reasons for this. First, there is the fear of making large investments into advanced RBM technology solutions. Second, it is considered that RBM is most suitable for large, complex trials. Third, there is the fear of errors in both critical and non-critical data, appearing as reduced on-site monitoring is being conducted. Methods: Our RBM team identified, evaluated, and mitigated trial risks, as well as devised a monitoring strategy. The clinical research associate (CRA) assessed the site risks, and the RBM team conducted central monitoring. We compared all data errors and on-site monitoring time between the partial switching sites [sites that had switched to partial source data verification (SDV) and source data review (SDR)] and the 100% SDV and SDR sites (sites that had implemented 100% SDV and SDR). Results: Partial switching sites did not require any critical data correction and had a smaller number of data corrections through on-site monitoring than the 100% SDV and SDR sites. The RBM strategy reduced the on-site monitoring time by 30%. Conclusions: The results suggest that RBM can be successfully implemented through the use of site risk assessment and central monitoring with practically no additional investment in technology and still produced similar results in terms of subject safety and data quality, as well as the cost savings that have been reported in global complex studies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected how clinical trials are managed, both within existing portfolios and for the rapidly developed COVID-19 trials. Sponsors or delegated organisations responsible for monitoring trials have needed to consider and implement alternative ways of working due to the national infection risk necessitating restricted movement of staff and public, reduced clinical staff resource as research staff moved to clinical areas, and amended working arrangements for sponsor and sponsor delegates as staff moved to working from home.Organisations have often worked in isolation to fast track mitigations required for the conduct of clinical trials during the pandemic; this paper describes many of the learnings from a group of monitoring leads based in United Kingdom Clinical Research Collaboration (UKCRC) Clinical Trials Unit (CTUs) within the UK.The UKCRC Monitoring Task and Finish Group, comprising monitoring leads from 9 CTUs, met repeatedly to identify how COVID-19 had affected clinical trial monitoring. Informed consent is included as a specific issue within this paper, as review of completed consent documentation is often required within trial monitoring plans (TMPs). Monitoring is defined as involving on-site monitoring, central monitoring or/and remote monitoring.Monitoring, required to protect the safety of the patients and the integrity of the trial and ensure the protocol is followed, is often best done by a combination of central, remote and on-site monitoring. However, if on-site monitoring is not possible, workable solutions can be found using only central or central and remote monitoring. eConsent, consent by a third person, or via remote means is plausible. Minimising datasets to the critical data reduces workload for sites and CTU staff. Home working caused by COVID-19 has made electronic trial master files (TMFs) more inviting. Allowing sites to book and attend protocol training at a time convenient to them has been successful and worth pursuing for trials with many sites in the future.The arrival of COVID-19 in the UK has forced consideration of and changes to how clinical trials are conducted in relation to monitoring. Some developed practices will be useful in other pandemics and others should be incorporated into regular use. [ABSTRACT FROM AUTHOR]

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.

Background/Aims: Clinical trials should be designed and managed to minimise important errors with potential to compromise patient safety or data integrity, employ monitoring practices that detect and correct important errors quickly, and take robust action to prevent repetition. Regulators highlight the use of risk-based monitoring, making greater use of centralised monitoring and reducing reliance on centre visits. The TEMPER study was a prospective evaluation of triggered monitoring (a risk-based monitoring method), whereby centres are prioritised for visits based on central monitoring results. Conducted in three UK-based randomised cancer treatment trials of investigational medicine products with time-to-event outcomes, it found high levels of serious findings at triggered centre visits but also at visits to matched control centres that, based on central monitoring, were not of concern. Here, we report a detailed review of the serious findings from TEMPER centre visits. We sought to identify feasible, centralised processes which might detect or prevent these findings without a centre visit. Methods: The primary outcome of this study was the proportion of all 'major' and 'critical' TEMPER centre visit findings theoretically detectable or preventable through a feasible, centralised process. To devise processes, we considered a representative example of each finding type through an internal consensus exercise. This involved (a) agreeing the potential, by some described process, for each finding type to be centrally detected or prevented and (b) agreeing a proposed feasibility score for each proposed process. To further assess feasibility, we ran a consultation exercise, whereby the proposed processes were reviewed and rated for feasibility by invited external trialists. Results: In TEMPER, 312 major or critical findings were identified at 94 visits. These findings comprised 120 distinct issues, for which we proposed 56 different centralised processes. Following independent review of the feasibility of the proposed processes by 87 consultation respondents across eight different trial stakeholder groups, we conclude that 306/312 (98%) findings could theoretically be prevented or identified centrally. Of the processes deemed feasible, those relating to informed consent could have the most impact. Of processes not currently deemed feasible, those involving use of electronic health records are among those with the largest potential benefit. Conclusions: This work presents a best-case scenario, where a large majority of monitoring findings were deemed theoretically preventable or detectable by central processes. Caveats include the cost of applying all necessary methods, and the resource implications of enhanced central monitoring for both centre and trials unit staff. Our results will inform future monitoring plans and emphasise the importance of continued critical review of monitoring processes and outcomes to ensure they remain appropriate. [ABSTRACT FROM AUTHOR]

Background: We assessed the feasibility of a hybrid monitoring system (minimal on-site monitoring + strategic central monitoring) used at the academic research office at Asan Medical Center (Seoul, Korea) in monitoring investigator-sponsored oncology trials. Methods: Monitoring findings in three oncology trials conducted between 2014 and 2017 were compared. A confirmatory source data verification (SDV) was carried out in the low-risk trial and compared with the central monitoring findings. The economic advantages of central monitoring were tested by calculating the monitoring hours per patient. Results: A total of 50, 118, 228 patients were enrolled in the high-, intermediate-, and low-risk trials, respectively. The high-risk trial was monitored through 42 on-site visits (1299 findings); the intermediate-risk trial had 79 monitorings (on-site, 24%; central, 76%; 1464 findings); the low-risk trial had 197 monitorings (on-site, 4%; central, 96%; 3364 findings). Central monitoring was more effective than on-site monitoring in revealing minor errors such as "missing case report forms" and "data outliers" (both P < 0.0001), and showed comparable results in revealing major issues such as investigational product compliance and delayed reporting of serious adverse events (both P > 0.05). Confirmatory SDV in the low-risk trial revealed more findings than central monitoring in the "inconsistent data" and "inappropriate adverse event" categories. The total monitoring hours per patient were lower in the intermediate- and low-risk trials than in the high-risk trial (8.1 and 7.3 vs. 14.3 h, respectively). Conclusion: Our hybrid monitoring system showed acceptable feasibility in revealing both major and minor issues in multi-center oncology investigator-sponsored trials. [ABSTRACT FROM AUTHOR]

The loosely coupled independent hardware of any kind under internet protocol refers to the Internet of Things (IoT). The IoT network has often framed by the composition of various standards, techniques, and services that are having diversified privacy & security prerequisites. Therefore, it has noted that paradigm IoT has similar problems of security as cloud services, the internet, and "mobile communication networks". Nevertheless, the outdated countermeasures of security, & implementation of privacy cannot be applied directly to the technologies of IoT because of confined IoT elements computing power, the maximum amount of interrelated devices & data sharing among users & objects. The proposals of IDS for the IoT will be placed to be distributed or central system or in the combination of bi-phase systems. The traditional intrusion detection strategies detect intrusion either by signature, anomalies, or a combination of any of these. Due to the limited resources of the devices placed in IoT networks, the intrusion detection strategies should perform the intrusion defense under the constrained resources of the corresponding devices. Regarding this argument, a novel specification measure that allows each of the devices falls in an IoT network to defend the intrusion at a corresponding device level. The method explored in this manuscript is a specification approach that determines Specification Heuristics to assess the scope of intrusion in IoT network requests. [ABSTRACT FROM AUTHOR]

Background: Evidence from prospectively designed studies to guide on-site monitoring practices for randomized trials is limited. A cluster randomized study, nested within the Strategic Timing of AntiRetroviral Treatment (START) trial, was conducted to evaluate on-site monitoring. Methods: Sites were randomized to either annual on-site monitoring or no on-site monitoring. All sites were centrally monitored, and local monitoring was carried out twice each year. Randomization was stratified by country and projected enrollment in START. The primary outcome was a participant-level composite outcome including components for eligibility errors, consent violations, use of antiretroviral treatment not recommended by protocol, late reporting of START primary and secondary clinical endpoints (defined as the event being reported more than 6 months from occurrence), and data alteration and fraud. Logistic regression fixed effect hierarchical models were used to compare on-site versus no on-site monitoring for the primary composite outcome and its components. Odds ratios and 95% confidence intervals comparing on-site monitoring versus no on-site monitoring are cited. Results: In total, 99 sites (2107 participants) were randomized to receive annual on-site monitoring and 97 sites (2264 participants) were randomized to be monitored only centrally and locally. The two monitoring groups were well balanced at entry. In the on-site monitoring group, 469 annual on-site monitoring visits were conducted, and 134 participants (6.4%) in 56 of 99 sites (57%) had a primary monitoring outcome. In the no on-site monitoring group, 85 participants (3.8%) in 34 of 97 sites (35%) had a primary monitoring outcome (odds ratio = 1.7; 95% confidence interval: 1.1–2.7; p = 0.03). Informed consent violations accounted for most outcomes in each group (56 vs 41 participants). The largest odds ratio was for eligibility violations (odds ratio = 12.2; 95% confidence interval: 1.8–85.2; p = 0.01). The number of participants with a late START primary endpoint was similar for each monitoring group (23 vs 16 participants). Late START grade 4 and unscheduled hospitalization events were found for 34 participants in the on-site monitoring group and 19 participants in the no on-site monitoring group (odds ratio = 2.0; 95% confidence interval: 1.1–3.7; p = 0.02). There were no cases of data alteration or fraud. Based on the travel budget for on-site monitoring and the hours spent conducting on-site monitoring, the estimated cost of on-site monitoring was over US$2 million. Conclusion: On-site monitoring led to the identification of more eligibility and consent violations and START clinical events being reported more than 6 months from occurrence as compared to no on-site monitoring. Considering the nature of the excess monitoring outcomes identified at sites receiving on-site monitoring, as well as the cost of on-site monitoring, the value to the START study was limited. [ABSTRACT FROM AUTHOR]

Background: Despite the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) encouraging the use of risk-based monitoring for trials in 2013, there remains a lack of evidence-based guidelines on how to monitor. We surveyed the academic United Kingdom Clinical Research Collaboration (UKCRC) registered clinical trials units (CTUs) to find out their policy on monitoring of phase III randomised clinical trials of an investigational medicinal product (CTIMPs).Methods: An online survey of monitoring policy with sections on the CTU, central monitoring and on-site monitoring was sent to all 50 UKCRC registered CTUs in November 2018. Descriptive data analysis and tabulations are reported using the total number answering each question.Results: A total of 43/50 (86%) of CTUs responded with 38 conducting phase III randomised CTIMP trials. Of these 38 CTUs, 34 finished the survey. Most CTUs (36/37, 97%) use a central monitoring process to guide, target or supplement site visits. More than half (19/36, 53%) of CTUs do not use an automated monitoring report when centrally monitoring trials and all units use trial team knowledge to make a final decision on whether an on-site visit is required. A total of 31/34 (91%) CTUs used triggers to decide whether or not to conduct an on-site monitoring visit. On-site, a mixture of source data verification and checking of processes was carried out. The CTUs overwhelmingly (27/34, 79%) selected optimising central monitoring as their most pressing concern.Conclusion: The survey showed a wide variation in phase III randomised CTIMP trial monitoring practices by academic clinical trials units within a single research-active country. We urgently need to develop evidence-based regulator-agreed guidance for CTUs on best practice for both central and on-site monitoring and to develop tools for all CTUs to use. [ABSTRACT FROM AUTHOR]

Abstract Background The costs of medical research are a concern. Clinical Trials Units (CTUs) need to better understand variations in the costs of their activities. Methods Representatives of ten CTUs and two grant-awarding bodies pooled their experiences in discussions over 1.5 years. Five of the CTUs provided estimates of, and written justification for, costs associated with CTU activities required to implement an identical protocol. The protocol described a 5.5-year, nonpharmacological randomized controlled trial (RCT) conducted at 20 centres. Direct and indirect costs, the number of full time equivalents (FTEs) and the FTEs attracting overheads were compared and qualitative methods (unstructured interviews and thematic analysis) were used to interpret the results. Four members of the group (funding-body representatives or award panel members) reviewed the justification statements for transparency and information content. Separately, 163 activities common to trials were assigned to roles used by nine CTUs; the consistency of role delineation was assessed by Cohen’s κ. Results Median full economic cost of CTU activities was £769,637 (range: £661,112 to £1,383,323). Indirect costs varied considerably, accounting for between 15% and 59% (median 35%) of the full economic cost of the grant. Excluding one CTU, which used external statisticians, the total number of FTEs ranged from 2.0 to 3.0; total FTEs attracting overheads ranged from 0.3 to 2.0. Variation in directly incurred staff costs depended on whether CTUs: supported particular roles from core funding rather than grants; opted not to cost certain activities into the grant; assigned clerical or data management tasks to research or administrative staff; employed extensive on-site monitoring strategies (also the main source of variation in non-staff costs). Funders preferred written justifications of costs that described both FTEs and indicative tasks for funded roles, with itemised non-staff costs. Consistency in role delineation was fair (κ = 0.21–0.40) for statisticians/data managers and poor for other roles (κ

Background/aims In multi-site clinical trials, where trial data and conduct are scrutinised centrally with pre-specified triggers for visits to sites, targeted monitoring may be an efficient way to prioritise on-site monitoring. This approach is widely used in academic trials, but has never been formally evaluated. Methods TEMPER assessed the ability of targeted monitoring, as used in three ongoing phase III randomised multi-site oncology trials, to distinguish sites at which higher and lower rates of protocol and/or Good Clinical Practice violations would be found during site visits. Using a prospective, matched-pair design, sites that had been prioritised for visits after having activated ‘triggers’ were matched with a control (‘untriggered’) site, which would not usually have been visited at that time. The paired sites were visited within 4 weeks of each other, and visit findings are recorded and categorised according to the seriousness of the deviation. The primary outcome measure was the proportion of sites with ≥1 ‘Major’ or ‘Critical’ finding not previously identified centrally. The study was powered to detect an absolute difference of ≥30% between triggered and untriggered visits. A sensitivity analysis, recommended by the study’s blinded endpoint review committee, excluded findings related to re-consent. Additional analyses assessed the prognostic value of individual triggers and data from pre-visit questionnaires completed by site and trials unit staff. Results In total, 42 matched pairs of visits took place between 2013 and 2016. In the primary analysis, 88.1% of triggered visits had ≥1 new Major/Critical finding, compared to 81.0% of untriggered visits, an absolute difference of 7.1% (95% confidence interval −8.3%, +22.5%; p = 0.365). When re-consent findings were excluded, these figures reduced to 85.7% versus 59.5%, (difference = 26.2%, 95% confidence interval 8.0%, 44.4%; p = 0.007). Individual triggers had modest prognostic value but knowledge of the trial-related activities carried out by site staff may be useful. Conclusion Triggered monitoring approaches, as used in these trials, were not sufficiently discriminatory. The rate of Major and Critical findings was higher than anticipated, but the majority related to consent and re-consent with no indication of systemic problems that would impact trial-wide safety issues or integrity of the results in any of the three trials. Sensitivity analyses suggest triggered monitoring may be of potential use, but needs improvement and investigation of further central monitoring triggers is warranted. TEMPER highlights the need to question and evaluate methods in trial conduct, and should inform further developments in this area. [ABSTRACT FROM AUTHOR]

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.

Background: Traditional site-monitoring techniques are not optimal in finding data fabrication and other nonrandom data distributions with the greatest potential for jeopardizing the validity of study results. TransCelerate BioPharma conducted an experiment testing the utility of statistical methods for detecting implanted fabricated data and other signals of noncompliance. Methods: TransCelerate tested statistical monitoring on a data set from a chronic obstructive pulmonary disease (COPD) clinical study with 178 sites and 1554 subjects. Fabricated data were selectively implanted in 7 sites and 43 subjects by expert clinicians in COPD. The data set was partitioned to simulate studies of different sizes. Analyses of vital signs, spirometry, visit dates, and adverse events included distributions of standard deviations, correlations, repeated values, digit preference, and outlier/inlier detection. An interpretation team, including clinicians, statisticians, site monitoring, and data management, reviewed the results and created an algorithm to flag sites for fabricated data. Results: The algorithm identified 11 sites (19%), 19 sites (31%), 28 sites (16%), and 45 sites (25%) as having potentially fabricated data for studies 2A, 2, 1A, and 1, respectively. For study 2A, 3 of 7 sites with fabricated data were detected, 5 of 7 were detected for studies 2 and 1A, and 6 of 7 for study 1. Except for study 2A, the algorithm had good sensitivity and specificity (>70%) for identifying sites with fabricated data. Conclusions: We recommend a cross-functional, collaborative approach to statistical monitoring that can adapt to study design and data source and use a combination of statistical screening techniques and confirmatory graphics. [ABSTRACT FROM AUTHOR]

The sense of agency refers to the feeling of authorship that “I am the one who is controlling external events through my own action”. A distinction between explicit judgement of agency and implicit feeling of agency has been proposed theoretically. However, there has not been sufficient experimental evidence to support this distinction. We have assessed separate explicit and implicit agency measures in the same population and investigated their relationships. Intentional binding task was employed as an implicit measure and self-other attribution task as an explicit measure, which are known to reflect clinical symptoms of disorders in the sense of agency. The results of the implicit measure and explicit measure were not correlated, suggesting dissociation of the explicit judgement of agency and the implicit feeling of agency. [ABSTRACT FROM AUTHOR]

The article reports on a study conducted by TransCelerate which describes the different approaches used by companies in central monitoring of the workflows in clinical trial management. It explores the on-site and off-site monitoring in data verification and predictive detection of quality in desired outcomes. It details the initial implementation steps aimed at discovering the capabilities of the organization.

Background Some level of monitoring is usually required during a clinical trial to protect the rights and safety of trial participants and to safeguard the quality and reliability of trial results. Although there is increasing support for the use of risk-proportionate approaches to achieve these aims, the variety of methods and lack of an empirical evidence base can present challenges for clinical trial practitioners. Methods This paper describes the monitoring methods and procedures that are utilised by a noncommercial clinical trials unit which coordinates a range of clinical trials across a variety of clinical areas with different associated risks. Results Monitoring activities and approaches should be selected to be proportionate to the risks identified within a trial. A risk-proportionate approach to monitoring is described giving details of methods that may be considered by clinical trial practitioners during the development of a trial monitoring plan. An example risk assessment and corresponding monitoring plan for a low risk (type A in the Medicines and Healthcare Products Regulatory Agency (MHRA) classification system) pediatric trial is provided for illustration. Conclusion We present ideas for developing a monitoring plan for a clinical trial of an investigational medicinal product based on our experience. Alternative approaches may be relevant or preferable in other settings based on inherent risk. [ABSTRACT FROM AUTHOR]

Background Comprehensive care in homozygous sickle cell disease (HbSS) entails universal neonatal screening and subsequent monitoring of identified patients, a process which has been streamlined in the neighbouring island of Jamaica. In preparation for a similar undertaking in Barbados, we have developed a database of persons with known HbSS, and have piloted processes for documenting clinical manifestations. We now present a brief clinical profile of these findings with comparisons to the Jamaican cohort. Methods HbSS participants were recruited from clinics and support groups. A history of select clinical symptoms was taken and blood and urine samples and echocardiograms were analysed. A reanalysis of data from a previous birth cohort was completed. Results Forty-eight persons participated (32 F/16 M); age range 10-62 yrs. 94% had a history of ever having a painful crisis. In the past year, 44% of participants had at least one crisis. There were >69 crises in 21 individuals; 61% were self-managed at home and the majority of the others were treated and discharged from hospital; few were admitted. The prevalence of chronic leg ulceration was 27%. Forty-two persons had urinalysis, 44% were diagnosed with albuminuria (urinary protein/creatinine ratio ≥30 mg/g). Thirty-two participants had echocardiography, 28% had a TRJV ≥ 2.5 m/s. Re-analysis of the incidence study revealed a sickle gene frequency (95%CI) of 2.01% (0.24 to 7.21). Conclusion Although we share a common ancestry, it is thought that HbSS is less common and less severe in Barbados compared to Jamaica. The Jamaican studies reported a sickle gene frequency of 3.15 (2.81 to 3.52); the prevalence of chronic leg ulcers and albuminuria was 29.5% and 42.5% respectively. These comparisons suggest that our initial thoughts may be speculative and that HbSS may be an underestimated clinical problem in Barbados. A prospective neonatal screening programme combined with centralized, routine monitoring of HbSS morbidity and outcomes will definitively answer this question and will improve the evidence-based care and management of HbSS in Barbados. [ABSTRACT FROM AUTHOR]

Abstract: The sense of agency is a central aspect of human self-consciousness and refers to the experience of oneself as the agent of one’s own actions. Several different cognitive theories on the sense of agency have been proposed implying divergent empirical approaches and results, especially with respect to neural correlates. A multifactorial and multilevel model of the sense of agency may provide the most constructive framework for integrating divergent theories and findings, meeting the complex nature of this intriguing phenomenon. [Copyright &y& Elsevier]

Any attempt to elucidate the nature and mechanism of passivity phenomena, i.e., experiences that one’s conscious actions or thoughts have not been ‘willed’ by oneself, requires an integrative philosophical–neurobiological approach. The model proposed here adopts some fundamental positions that have long been advocated by philosophers and theoretical psychologists and have now found support from functional neuroanatomy. First, we experience our actions not from the standpoint of the executive but through the perception of its effects. Second, the ‘self’ is not an agent of behaviour. Third, behaviour is energised and integrated by basic drives (instincts). Fourth, the view that the experience of an acting self is related to drive reduction associated with voluntary actions is perhaps less well developed. The model thus proposes that passivity phenomena are actions that are induced by the perception of salient events but that are not integrated with or conducive to the overall motivational state of the organism. It has been suggested that, following the perception of salient events, competition arises between automatic response tendencies seeking expression. The prefrontal cortex appears to play an important role not only in determining which events are to be perceived but also which of the corresponding response dispositions is to be selected and actualised in overt behaviour. Thus, action selection is the outcome of competition between response tendencies in the context of prefrontal biasing signals that represent drives and strivings for goals. Action selection may be uncoupled from drives and strivings as a result of a lowering of the threshold for action selection—as is suggested to be the case in schizophrenic passivity phenomena—or due to disconnection from prefrontal regions—as may be the case in the alien limb syndrome. [Copyright &y& Elsevier]

With the growing use of online study management systems and rapid availability of data, timely data review and quality assessments are necessary to ensure proper clinical trial implementation. In this report we describe central monitoring used to ensure protocol compliance and accurate data reporting, implemented during a large phase 3 clinical trial. The Tuberculosis Trials Consortium (TBTC) Study 31/AIDS Clinical Trials Group (ACTG) study A5349 (S31) is an international, multi-site, randomized, open-label, controlled, non-inferiority phase 3 clinical trial comparing two 4-month regimens to a standard 6 month regimen for treatment of drug-susceptible tuberculosis (TB) among adolescents and adults with a sample size of 2500 participants. Central monitoring utilized primary study data in a five-tiered approach, including (1) real-time data checks & topic-specific intervention reports, (2) missing forms reports, (3) quality assurance metrics, (4) critical data reports and (5) protocol deviation identification, aimed to detect and resolve quality challenges. Over the course of the study, 240 data checks and reports were programed across the five tiers used. This use of primary study data to identify issues rapidly allowed the study sponsor to focus quality assurance and data cleaning activities on prioritized data, related to protocol compliance and accurate reporting of study results. Our approach enabled us to become more efficient and effective as we informed sites about deviations, resolved missing or inconsistent data, provided targeted guidance, and gained a deeper understanding of challenges experienced at clinical trial sites. Trial registration: This trial was registered with ClinicalTrials.gov (Identifier: NCT02410772) on April 8, 2015. • Informed sites about deviations and resolved missing or inconsistent data. • Minimized future deviations, queries, and data inconsistencies. • Tailored training messages, creating a cycle of quality improvement. [ABSTRACT FROM AUTHOR]