Determining the extent and frequency of on-site monitoring: a bayesian risk-based approach.

BACKGROUND: On-site monitoring is a crucial component of quality control in clinical trials. However, many cast doubt on its cost-effectiveness due to various issues, such as a lack of monitoring focus that could assist in prioritizing limited resources during a site visit. Consequently, an increasing number of trial sponsors are implementing a hybrid monitoring strategy that combines on-site monitoring with centralised monitoring. One of the primary objectives of centralised monitoring, as stated in the clinical trial guidelines, is to guide and adjust the extent and frequency of on-site monitoring. Quality tolerance limits (QTLs) introduced in ICH E6(R2) and thresholds proposed by TransCelerate Biopharma are two existing approaches for achieving this objective at the trial- and site-levels, respectively. The funnel plot, as another threshold-based site-level method, overcomes the limitation of TransCelerate's method by adjusting thresholds flexibly based on site sizes. Nonetheless, both methods do not transparently explain the reason for choosing the thresholds that they used or whether their choices are optimal in any certain sense. Additionally, related Bayesian monitoring methods are also lacking. METHODS: We propose a simple, transparent, and user-friendly Bayesian-based risk boundary for determining the extent and frequency of on-site monitoring both at the trial- and site-levels. We developed a four-step approach, including: 1) establishing risk levels for key risk indicators (KRIs) along with their corresponding monitoring actions and estimates; 2) calculating the optimal risk boundaries; 3) comparing the outcomes of KRIs against the optimal risk boundaries; and 4) providing recommendations based on the comparison results. Our method can be used to identify the optimal risk boundaries within an established risk level range and is applicable to continuous, discrete, and time-to-event endpoints. RESULTS: We evaluate the performance of the proposed risk boundaries via simulations that mimic various realistic clinical trial scenarios. The performance of the proposed risk boundaries is compared against the funnel plot using real clinical trial data. The results demonstrate the applicability and flexibility of the proposed method for clinical trial monitoring. Moreover, we identify key factors that affect the optimality and performance of the proposed risk boundaries, respectively. CONCLUSION: Given the aforementioned advantages of the proposed risk boundaries, we expect that they will benefit the clinical trial community at large, in particular in the realm of risk-based monitoring.

Effectiveness of remote risk-based monitoring and potential benefits for combination with direct data capture.

BACKGROUND: In recent years, alternative monitoring approaches, such as risk-based and remote monitoring techniques, have been recommended instead of traditional on-site monitoring to achieve more efficient monitoring. Remote risk-based monitoring (R2BM) is a monitoring technique that combines risk-based and remote monitoring and focuses on the detection of critical data and process errors. Direct data capture (DDC), which directly collects electronic source data, can facilitate R2BM by minimizing the extent of source documents that must be reviewed and reducing the additional workload on R2BM. In this study, we evaluated the effectiveness of R2BM and the synergistic effect of combining R2BM with DDC. METHODS: R2BM was prospectively conducted with eight participants in a randomized clinical trial using a remote monitoring system that uploaded photographs of source documents to a cloud location. Critical data and processes were verified by R2BM, and later, all were confirmed by on-site monitoring to evaluate the ability of R2BM to detect critical data and process errors and the workload of uploading photographs for clinical trial staff. In addition, the reduction of the number of uploaded photographs was evaluated by assuming that the DDC was introduced for data collection. RESULTS: Of the 4645 data points, 20.9% (n = 973, 95% confidence interval = 19.8-22.2) were identified as critical. All critical data errors corresponding to 5.4% (n = 53/973, 95% confidence interval = 4.1-7.1) of the critical data and critical process errors were detectable by R2BM. The mean number of uploaded photographs and the mean time to upload them per visit per participant were 34.4 ± 11.9 and 26.5 ± 11.8 min (mean ± standard deviation), respectively. When assuming that DDC was introduced for data collection, 45.0% (95% confidence interval = 42.2-47.9) of uploaded photographs for R2BM were reduced. CONCLUSIONS: R2BM can detect 100% of the critical data and process errors without on-site monitoring. Combining R2BM with DDC reduces the workload of R2BM and further improves its efficiency.

A Case Study in Application of the Risk Knowledge Infinity Cycle.

The Risk Knowledge Infinity (RKI) Cycle Framework was featured as part of the ICH-sanctioned training materials supporting the recent issuance of ICH Q9(R1) Quality Risk Management To support ICH Q9(R1) understanding and adoption, this paper presents a case study on the application of the RKI Cycle, based on an underlying out-of-specification investigation. This case study provides a stepwise walk-through of the cycle to illustrate how key concepts within the ICH Q9(R1) revision can be achieved through better connecting quality risk management and knowledge management with a framework such as the RKI Cycle.

A cumulative impact assessment on the marine capacity to supply ecosystem services.

Ecosystem services link the status of biodiversity and its functioning to societal goods and benefits contributing to human wellbeing. As such, they can play a key role in preserving the environment and managing natural resources and ecosystems to conserve nature's contributions to people. Identification of the main threats acting on the natural environment, and how these may impact its capacity to supply ecosystem services, is fundamental to the maintenance of these services. To that end, we present a novel approach based on a cumulative impacts assessment that 1) covers all relevant human activities and their pressures, 2) links impacts to the biotic groups that make up biodiversity and 3) provides an estimation of the Service Supply Potential based on the functioning of these biotic groups. Key proxy metrics to estimate this Service Supply Potential were identified from a literature review and quantified using a food web model (Ecopath with Ecosim). In addition to this quantitative information, the assessment of the capacity to supply ecosystem services was supplemented with expert judgement-based information to reflect the societal preferences that drive the allocation of human capital and turn these services into societal goods and benefits. As a proof of concept, the method was applied to the North Sea ecosystem. Results showed that, overall, the capacity of the North Sea to supply Cultural ecosystem services was most threatened, with an average potential decline of 50 % compared to an undisturbed situation. This was followed by the Provisioning ecosystem services with 46 % and the Regulation & Maintenance with 38 %. The main anthropogenic threats (excluding climate change) to the North Sea capacity to supply ecosystem services come primarily from fishing contributing to 51 % of the overall threat. Of the remaining 18 sectoral activities another 23 % was contributed by mining, non-renewable energy, tourism, and agriculture.

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

OBJECTIVES: Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. METHODS: QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. RESULTS: Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size and offering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. CONCLUSIONS: In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

A risk-based monitoring approach to source data monitoring and documenting monitoring findings.

BACKGROUND: Clinical trial monitoring is evolving from labor-intensive to targeted approaches. The traditional 100% Source Data Monitoring (SDM) approach fails to prioritize data by significance, diverting attention from critical elements. Despite regulatory guidance on Risk-Based Monitoring (RBM), its widespread implementation has been slow. METHODS: Our study teams assess the study's overall risk, document heightened and critical risks, and create a study-specific risk-based monitoring plan, integrating SDM and Central Data Monitoring (CDM). SDM combines a fixed list of pre-identified variables and a list of randomly identified variables to monitor. Identifying variables follows a two-step approach: first, a random sample of participants is selected, second, a random set of variables for each participant selected is identified. Sampling weights prioritize critical variables. Regular team meetings are held to discuss and compile significant findings into a Study Monitoring Report. RESULTS: We present a random SDM sample and a Study Monitoring Report. The random SDM output includes a look-up table for selected database elements. The report provides a holistic view of the study issues and overall health. CONCLUSIONS: The proposed random sampling method is used to monitor a representative set of critical variables, while the Study Monitoring Report is written to summarize significant monitoring findings and data trends. The report allows the sponsor to assess the current status of the study and data effectively. Communicating and sharing emerging insights facilitates timely adjustments of future monitoring activities, optimizing efficiencies, and study outcomes.

Good Statistical Monitoring: A Flexible Open-Source Tool to Detect Risks in Clinical Trials.

BACKGROUND: Risk-based quality management is a regulatory-recommended approach to manage risk in a clinical trial. A key element of this strategy is to conduct risk-based monitoring to detect potential risks to critical data and processes earlier. However, there are limited publicly available tools to perform the analytics required for this purpose. Good Statistical Monitoring is a new open-source solution developed to help address this need. METHODS: A team of statisticians, data scientists, clinicians, data managers, clinical operations, regulatory, and quality compliance staff collaborated to design Good Statistical Monitoring, an R package, to flexibly and efficiently implement end-to-end analyses of key risks. The package currently supports the mapping of clinical trial data from a variety of formats, evaluation of 12 key risk indicators, interactive visualization of analysis results, and creation of standardized reports. RESULTS: The Good Statistical Monitoring package is freely available on GitHub and empowers clinical study teams to proactively monitor key risks. It employs a modular workflow to perform risk assessments that can be customized by replacing any workflow component with a study-specific alternative. Results can be exported to other clinical systems or can be viewed as an interactive report to facilitate follow-up risk mitigation. Rigorous testing and qualification are performed as part of each release to ensure package quality. CONCLUSIONS: Good Statistical Monitoring is an open-source solution designed to enable clinical study teams to implement statistical monitoring of critical risks, as part of a comprehensive risk-based quality management strategy.

Assessing the performance of methods for central statistical monitoring of a binary or continuous outcome in multi-center trials: A simulation study.

BACKGROUND: Quality study monitoring is fundamental to patient safety and data integrity. Regulators and industry consortia have increasingly advocated for risk-based monitoring (RBM) and central statistical monitoring (CSM) for more effective and efficient monitoring. Assessing which statistical methods underpin these approaches can best identify unusual data patterns in multi-center clinical trials that may be driven by potential systematic errors is important. METHODS: We assessed various CSM techniques, including cross-tests, fixed-effects, mixed-effects, and finite mixture models, across scenarios with different sample sizes, contamination rates, and overdispersion via simulation. Our evaluation utilized threshold-independent metrics such as the area under the curve (AUC) and average precision (AP), offering a fuller picture of CSM performance. RESULTS: All CSM methods showed consistent characteristics across center sizes or overdispersion. The adaptive finite mixture model outperformed others in AUC and AP, especially at 30% contamination, upholding high specificity unless converging to a single-component model due to low contamination or deviation. The mixed-effects model performed well at lower contamination rates. However, it became conservative in specificity and exhibited declined performance for binary outcomes under high deviation. Cross-tests and fixed-effects methods underperformed, especially when deviation increased. CONCLUSION: Our evaluation explored the merits and drawbacks of multiple CSM methods, and found that relying on sensitivity and specificity alone is likely insufficient to fully measure predictive performance. The finite mixture method demonstrated more consistent performance across scenarios by mitigating the influence of outliers. In practice, considering the study-specific costs of false positives/negatives with available resources for monitoring is important.

Perspectives of refugee parents and unaccompanied minors on initial health assessment and access to care.

To explore the needs, expectations, and experiences of asylum-seeking parents and unaccompanied minors under the age of 18 years on the initial health assessment for children and adolescents and access to care upon entry in the Netherlands, We conducted five semi-structured focus group discussions with asylum-seeking parents and unaccompanied minors, from Syria, Eritrea, Afghanistan, and other Middle-East and African countries, supported by professional interpreters. To triangulate findings, semi-structured interviews with health care professionals involved in care for refugee children were conducted. Transcripts of focus group discussions were inductively and deductively coded and content analyzed; transcripts of interviews were deductively coded and content analyzed. In total, 31 asylum-seeking participants: 23 parents of 101 children (between 0 and 18 years old), 8 unaccompanied minors (between 15 and 17 years), and 6 healthcare professionals participated. Parents and minors expressed that upon entry, their needs were met for vaccinations, but not for screening or care for physical and mental health problems. Parents, minors, and health professionals emphasized the necessity of appropriate information and education about health, diseases, and the health system. Cultural change was mentioned as stressful for the parent-child interaction and parental well-being.     Conclusion: The perspectives of refugee parents and unaccompanied minors revealed opportunities to improve the experience of and access to health care of refugees entering the Netherlands, especially risk-specific screening and more adequate education about health, diseases, and the Dutch health care system. What is Known: •  Refugees have specific health needs due to pre-flight, flight, and resettlement conditions. Health assessment upon entry was non-obligatory in the Netherlands, except for the tuberculosis screening. Health needs were not always met, and refugees experienced barriers in access to care. What is New: • The initial health assessment met the needs concerning vaccinations but mismatched the needs regarding physical and mental health assessment. Screening for specific risk-related diseases and mental health could enable refugee parents and minors to engage better with the health system.

Livestock and environmental characterization of Colombian municipalities: study of vesicular stomatitis.

Amid the surge in data volume generated across various fields of knowledge, there is an increasing necessity for advanced analytical methodologies to effectively process and utilize this information. Particularly in the field of animal health, this approach is pivotal for enhancing disease understanding, surveillance, and management. The main objective of the study was to conduct a comprehensive livestock and environmental characterization of Colombian municipalities and examine their relationship with the distribution of vesicular stomatitis (VS). Utilizing satellite imagery to delineate climatic and land use profiles, along with data from the Colombian Agricultural Institute (ICA) concerning animal populations and their movements, the research employed Principal Component Analysis (PCA) to explore the correlation between environmental and livestock-related variables. Additionally, municipalities were grouped through a Hierarchical Clustering process. The assessment of risk associated with VS was carried out using a Generalized Linear Model. This process resulted in the formation of four distinct clusters: three primarily characterized by climatic attributes and one predominantly defined by livestock characteristics. Cluster 1, identified as "Andino" due to its climatic and environmental features, exhibited the highest odds ratio for VS occurrence. The adopted methodology not only provides a deeper understanding of the local population and its context, but also offers valuable insights for enhancing disease surveillance and control programs.

Lessons from CanSpotASF: Moving towards risk-based African Swine Fever surveillance with rule-out testing in Western Canada.

African swine fewer (ASF) is a serious disease present in Africa, Eurasia, and the Caribbean but not in continental North America. CanSpotASF describes the ASF surveillance in Canada as a phased in approach. The first enhancement to the passive surveillance was the risk-based early detection testing (rule-out testing) where eligible cases were tested for ASF virus (ASFv). The objective was to describe how the eligibility criteria were applied to cases in western Canada. In particular, to assess if cases tested for ASFv had eligible conditions and if pathology cases with eligible conditions were tested for ASFv based on the data collated by Canada West Swine Health Intelligence Network (CWSHIN) from British Columbia, Alberta, Saskatchewan, and Manitoba. The study period was August 2020 to December 2022 and the data included two study laboratories. We found that over 90% of cases tested for ASFv had eligible conditions as defined in CanSpotASF. The eligibility criteria were applied at three stages of the disease investigation process: 1) the clinical presentation in the herd; 2) at the initial laboratory assessment; and 3) the final pathology diagnosis. At the two study laboratories the proportion of all submitted cases (culture, serology, PCR, pathology) tested for ASFv was very low 1%. However, in the pathology cases specifically targeted in CanSpotASF, and the proportion of tested cases was 12%. In addition, for eligible pathology cases (eligible diagnosis or test) the proportion tested was higher 15%. These results indicated that CanSpotASF targeted herds with submissions for pathological examination and to some degree eligible conditions which would be herds with health issues (known or unknown). We interpret this as a first step towards risk-based surveillance with health as the defining factor.

Science- and Risk-Based Stability Strategies to Support Product Lifecycle Changes.

ICH Q12 asserts that science- and risk-based approaches are applicable to stability studies supporting Chemistry, Manufacturing and Controls (CMC) post-approval changes (PAC) to enable more timely implementation; however, no guidance or specific examples are provided to demonstrate how prior knowledge of the product can inform the risk assessment for the proposed change(s). Ten diverse case studies are presented in this manuscript to demonstrate how science- and risk-based stability strategies were used to support drug substance and product CMC PAC and lifecycle management activities. The accumulated stability knowledge held by original manufacturers of marketed products is substantial, and different elements of this knowledge base were used to assess the risks and impact of the proposed changes for confident change management. This paper provides ways to leverage science- and risk-based stability strategies as part of the post-approval change-management risk-mitigation strategy, which may enable a reduced stability data commitment and/or a reduced reporting category for change implementation.

Communicating the results of risk-based breast cancer screening through visualizations of risk: a participatory design approach.

BACKGROUND: Risk-based breast cancer (BC) screening raises new questions regarding information provision and risk communication. This study aimed to: 1) investigate women's beliefs and knowledge (i.e., mental models) regarding BC risk and (risk-based) BC screening in view of implications for information development; 2) develop novel informational materials to communicate the screening result in risk-based BC screening, including risk visualizations of both quantitative and qualitative information, from a Human-Centered Design perspective. METHODS: Phase 1: Interviews were conducted (n = 15, 40-50 years, 5 lower health literate) on women's beliefs about BC risk and (risk-based) BC screening. Phase 2: In three participatory design sessions, women (n = 4-6 across sessions, 40-50 years, 2-3 lower health literate) made assignments and created and evaluated visualizations of risk information central to the screening result. Prototypes were evaluated in two additional sessions (n = 2, 54-62 years, 0-1 lower health literate). Phase 3: Experts (n = 5) and women (n = 9, 40-74 years) evaluated the resulting materials. Two other experts were consulted throughout the development process to ensure that the content of the information materials was accurate. Interviews were transcribed literally and analysed using qualitative thematic analysis, focusing on implications for information development. Notes, assignments and materials from the participatory design sessions were summarized and main themes were identified. RESULTS: Women in both interviews and design sessions were positive about risk-based BC screening, especially because personal risk factors would be taken into account. However, they emphasized that the rationale of risk-based screening and classification into a risk category should be clearly stated and visualized, especially for higher- and lower-risk categories (which may cause anxiety or feelings of unfairness due to a lower screening frequency). Women wanted to know their personal risk, preferably visualized in an icon array, and wanted advice on risk reduction and breast self-examination. However, most risk factors were considered modifiable by women, and the risk factor breast density was not known, implying that information should emphasize that BC risk depends on multiple factors, including breast density. CONCLUSIONS: The information materials, including risk visualizations of both quantitative and qualitative information, developed from a Human-Centered Design perspective and a mental model approach, were positively evaluated by the target group.

The development and analysis of a Japanese modern comprehensive clinical data management training program.

Background: Clinical data management (CDM) collects, integrates, and makes data available. It plays a vital role in clinical research. However, there are few opportunities for Japanese clinical data managers to learn about its systematic framework, particularly in academic research organizations. While Japanese-language CDM training exists, its effectiveness in a Japanese context requires clarification. Objectives: We aimed to develop an advanced program of instruction for professionals to understand CDM and to determine the effectiveness of the training program. Methods and results: We developed an advanced program including risk-based monitoring and the Clinical Data Interchange Standards Consortium on a trial basis for clinical data managers to provide them with a comprehensive understanding of CDM. Fifty-two people attended the program and reported that they were highly satisfied with it. Conclusions: To provide comprehensive CDM training in Japan, it is imperative to continue improving the content and develop an advanced program. Due to the recent tightening of clinical research regulations and the development and dissemination of various systems for conducting clinical research, the competency-based educational program requires further development.

Differential changes in maternal proinflammatory IL6 plasmalevels as a putatively surrogate marker of candidacy andclinical utility during mid- and late pregnancy hyperglycemia:interventional impact of clinical pharmacist on maternal andneonatal outcomes in a randomized clinical trial / Cambios diferenciales en los niveles plasmáticos de IL6 proinflamatoria materna como un marcador supuestamente sustituto de candidatura y utilidad clínica durante la hiperglucemia a mitad y final del embarazo: impacto intervencionista del farmacéutico clínico en los resultados maternos y neonatales en un ensayo clínico aleatorizado

Background/methods: The impact of clinical pharmacist on undiagnosed pregnancy hyperglycemia (PHG) in mid- and late- pregnancy as a major preventable cause of maternal and neonatal (M/N) complications is investigated. This longitudinal randomized controlled study of changes in plasma levels of predictive/prognostic/diagnostic biomarkers of oxytocin, thrombospondin, MCP1, IL6, MIF, insulin and LAR and undesirable M/N pregnancy outcomes in women with/out PHG (pregnancy normoglycemia; PNG) following the implementation of clinical pharmacist interventions were investigated. Results: A total of 68 PHG (36 intervention vs. 32 non-intervention) vs. 21 PNG participants were enrolled at 20–28 weeks and followed up till delivery. BMI of intervention PHG (unlike non-intervention) was greater (p=0.036) compared to PNG’s. LAR and insulin, oxytocin, thrombospondin1, adiponectin and MCP1 plasma levels and their differences between 2nd and 3rd pregnancy trimesters lacked discrepancies in participants. Both PHG groups in mid pregnancy had substantially greater HbA1c %, FPG and IL6 levels vs. PNG, while PHG non-intervention’ leptin was greater than PNG’s. In late pregnancy, greater SBP, IL6 and MIF levels between either PHG groups vs. PNG’s were observed. Unlike PHG non-intervention and PNG; IL6 level in PHG intervention group decreased (-2.54±6.61; vs. non-intervention PHG’s 4.26±5.28; p<0.001 and vs. PNG’s 2.30±4.27; p=0.023). None of the assessed M/N outcomes was found of differential significance between any of the three study groups. Conclusions: Proinflammatory IL6 as a robust and generalizable cardiometabolic risk-based and related pharmacotherapy biomarker in mid and late hyperglycemic pregnancy with likely implications of novel therapeutic targets was delineated by clinical pharmacist interventions.(AU)

Does Central Statistical Monitoring Improve Data Quality? An Analysis of 1,111 Sites in 159 Clinical Trials.

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 -log10 P-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.

Comprehensive Assessment of Risk-Based Quality Management Adoption in Clinical Trials.

BACKGROUND: Risk-based monitoring (RBM) and risk-based quality management (RBQM) offer a compelling approach to increase efficiency, speed and quality in clinical trials by prioritizing and mitigating risks related to essential safety and efficacy data. Since 2013, the FDA and EMA have encouraged the use of RBM/RBQM, however adoption has been slow with limited understanding of the barriers to adoption. METHODS: The Tufts Center for the Study of Drug Development conducted an online survey among pharmaceutical, biotechnology, and contract research organizations and gathered 206 responses on 32 distinct RBQM practices. RESULTS: On average, companies implemented RBQM in 57% of their clinical trials. Lower levels of adoption were observed among companies conducting fewer than 25 trials annually (48%) compared to those conducting more than 100 trials annually (63%). Primary barriers to adoption include lack of organizational knowledge and awareness, mixed perceptions of the value proposition of RBQM, and poor change management planning and execution. Insights into improving the level of adoption are discussed.

Modeling strain variability in Campylobacter jejuni thermal inactivation by quantifying the number of strains required.

There is a limited understanding of the survival responses of Campylobacter jejuni during thermal processing, which must be investigated for appropriate risk assessment and processing. Therefore, we aimed to elucidate the survival response of C. jejuni and develop a predictive model considering strain variability and uncertainty, which are important for quantitative microbial risk assessment (QMRA) or risk-based processing control measures. We employed the most probable curve (MPC) method to consider the uncertainty in cell concentrations. Further, the multivariate normal (MVN) distribution served as a model for strain variability in bacterial survival behavior. The prediction curves from the MVN successfully captured the parameter variability of the most probable curves of each strain. More than ten reference strains effectively described the strain variability in parameters using the MVN distribution. The findings indicated that, with sufficient strain data, the MVN could estimate the strain variability, including unknown strains. The multi-level model for strain variability can potentially become a specialized tool for QMRA and risk-based processing controls. The combined approach of MPC and MVN provides valuable insights into strain variability, emphasizing the importance of accounting for variability and uncertainty in predictive models for QMRA and risk-based processing control measures.

Assessing the impact of risk-based data monitoring on outcomes for a paediatric multicentre randomised controlled trial.

BACKGROUND/AIMS: Regulatory guidelines recommend that sponsors develop a risk-based approach to monitoring clinical trials. However, there is a lack of evidence to guide the effective implementation of monitoring activities encompassed in this approach. The aim of this study was to assess the efficiency and impact of the risk-based monitoring approach used for a multicentre randomised controlled trial comparing treatments in paediatric patients undergoing cardiac bypass surgery. METHODS: This is a secondary analysis of data from a randomised controlled trial that implemented targeted source data verification as part of the risk-based monitoring approach. Monitoring duration and source to database error rates were calculated across the monitored trial dataset. The monitored and unmonitored trial dataset, and simulated trial datasets with differing degrees of source data verification and cohort sizes were compared for their effect on trial outcomes. RESULTS: In total, 106,749 critical data points across 1,282 participants were verified from source data either remotely or on-site during the trial. The total time spent monitoring was 365 hours, with a median (interquartile range) of 10 (7, 16) minutes per participant. An overall source to database error rate of 3.1% was found, and this did not differ between treatment groups. A low rate of error was found for all outcomes undergoing 100% source data verification, with the exception of two secondary outcomes with error rates >10%. Minimal variation in trial outcomes were found between the unmonitored and monitored datasets. Reduced degrees of source data verification and reduced cohort sizes assessed using simulated trial datasets had minimal impact on trial outcomes. CONCLUSIONS: Targeted source data verification of data critical to trial outcomes, which carried with it a substantial time investment, did not have an impact on study outcomes in this trial. This evaluation of the cost-effectiveness of targeted source data verification contributes to the evidence-base regarding the context where reduced emphasis should be placed on source data verification as the foremost monitoring activity.