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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. [ABSTRACT FROM AUTHOR]

In today's competitive landscape, fulfilling customer expectations and achieving a competitive edge are crucial for business success. These objectives can be attained by effective monitoring in both manufacturing and service sectors to enhance quality, reduce variation, and augment productivity. The control chart, a widely used tool for this purpose, has attracted significant attention from researchers for its ability to detect anomalies and manage out‐of‐control situations. The optimization of control charts, a central focus of this review, not only enhances the detection effectiveness but also maintains the desired false alarm rate, thus ensuring efficient process control without additional cost, complexity, or operational challenges for shop floor personnel. The optimization process involves adjusting charting parameters like the sample size, sampling interval, and control limits within a hypothesis testing framework, thereby achieving optimal system performance. Numerous optimization models have been developed to enhance control chart performance. This paper introduces a classification scheme to analyze and categorize the existing research on control chart optimization. By conducting a thorough review of more than 240 articles, the study pinpoints research gaps and offers valuable insights, thereby advancing the future research in this domain. [ABSTRACT FROM AUTHOR]

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]

Statistical monitoring of the level of economic growth momentum transformation is the basis for implementing quantitative policies for economic growth transformation. Based on theoretical analysis and combined with the process of national economic operation, this paper designs an economic growth momentum transformation monitoring indicator system consisting of four dimensions, supply and demand, factors, consumption and trade, using a comprehensive evaluation method to compile the economic growth momentum transformation level index. Using mainland China's provincial data from 2002 to 2021, we calculated the economic growth momentum transformation level of each province and extracted corresponding characteristics. The research found that the economic growth momentum transformation level of provincial economies in China exhibits stage-wise and dimensional differentiation over time, and it has category agglomeration and tiered transfer characteristics in space.

In clinical trial monitoring, substantial resources are allocated to perform source data verification (SDV). SDV ensures accurate and reliable transcription of trial participant information. Clinical site visits are typically scheduled at a fixed frequency for SDV, without objectively factoring in individual site workload. This often results in wasted resources and directly influences clinical trial cost. We leveraged historical data from several hundred clinical trials to predict SDV workload at trial sites using machine learning. Specifically, we adopted cross industry standard process for data mining (CRISP-DM) process model and devised a novel deep learning pipeline for longitudinal clinical trial data. The deep learning pipeline, which comprises recurrent neural network-based encoder and decoder, ingests multivariate sequence data from study sites and predicts SDV workload for future months. We also developed an efficient model enhancement workflow, in a data science platform that facilitates machine learning operations best practices, for timely adaptation of new features and changes. Several enhancement iterations have been performed since the launch of first SDV workload prediction model, resulting in a more accurate latest model compared to previous versions. We discuss these enhancements in the context of CRISP-DM phases. In conclusion, the SDV workload prediction model has enabled informed planning and optimization of resources within clinical trial operations.

Background: Statistical monitoring involves the review of prospective study data collected in participating sites to detect intra/inter patients and sites inconsistencies. We report methods and results of statistical monitoring in a phase IV clinical trial. Method: PRO-MSACTIVE is a study evaluating ocrelizumab in active relapsing multiple sclerosis (RMS) patients in France. Specific statistical methods (volcano plots, mahalanobis distance, funnel plot …) have been applied to a SDTM database to detect potential issues. R-Shiny application was developed to generate an interactive web application in order to ease site and/or patients identification during statistical data review meetings. Results: The PRO-MSACTIVE study enrolled 422 patients in 46 centers between July 2018 and August 2019. Three data review meetings were held between April and October 2019 and 14 standard and planned tests were run on study data, with a total of 15 (32.6%) sites identified as needing review or investigation. Overall 36 findings were identified during the meetings: duplicate records, outliers, inconsistent delays between dates. Conclusion: Statistical monitoring is useful to identify unusual or clustered data patterns that might be revealing issues that could impact the data integrity and/or may potentially impact patients’ safety. With anticipated and appropriate interactive data visualization, early signals can easily be identified or reviewed by the study team and appropriate actions be set up and assigned to the most appropriate function for a close follow-up and resolution. Interactive statistical monitoring is time consuming to initiate using R-Shiny, but is time saving after the 1st data review meeting (DRV).(ClinicalTrials.gov identifier: NCT03589105; EudraCT identifier: 2018-000780-91)

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]

Monitoring large-scale datastreams with limited resources has become increasingly important for real-time detection of abnormal activities in many applications. Despite the availability of large datasets, the challenges associated with designing an efficient change-detection when clustering or spatial pattern exists are not yet well addressed. In this article, a design-adaptive testing procedure is developed when only a limited number of streaming observations can be accessed at each time. We derive an optimal sampling strategy, the pattern-oriented-sampling, with which the proposed test possesses asymptotically and locally best power under alternatives. Then, a sequential change-detection procedure is proposed by integrating this test with generalized likelihood ratio approach. Benefiting from dynamically estimating the optimal sampling design, the proposed procedure is able to improve the sensitivity in detecting clustered changes compared with existing procedures. Its advantages are demonstrated in numerical simulations and a real data example. Ignoring the neighboring information of spatially structured data will tend to diminish the detection effectiveness of traditional detection procedures. for this article are available online. [ABSTRACT FROM AUTHOR]

Process monitoring using profile data remains an important and challenging problem in various manufacturing industries. Motivated by an application case of motherboard testing processes, we develop a novel modeling and monitoring framework for heterogeneous multivariate profiles. In this framework, a heterogeneous graphical model is constructed to depict the complicated heterogeneous relationship among profile channels. Then monitoring the heterogeneous relationship among profile channels can be reduced to monitoring the graphical networks. Besides, we investigate several theoretical results concerning the accuracy of the estimated graphical structure. Finally, we demonstrate the proposed method through extensive simulations and a real case study. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to statistical monitoring of online social networks where the edges represent the number of communications between ties at each time stamp. Since the available methods in the literature are limited to the assumption that the set of all interacting individuals is fixed during the monitoring horizon and their corresponding attributes do not change over time, the proposed method of this study tackles these limitations due to the properties of the random effect concepts. The application of appropriate parameter estimation technique is involved in the Likelihood Ratio Testing (LRT) approach considering two different statistics and the longitudinal network data are monitored. The performance of the proposed method is verified using numerical examples including simulation studies as well as an illustrative example. [ABSTRACT FROM AUTHOR]

The article is aimed at improving the approaches to monitoring the performance of collective accommodation facilities (CAF) taking into account changes in the methodology of statistical research of tourism in Ukraine. The specifics of the implementation of statistical monitoring of collective accommodation facilities are considered in view of changes in the methodological provisions for the organization of the State statistical observation in tourism in 2018. The withdrawal of individuals - entrepreneurs from the objects of statistical monitoring is proved to be impractical. This conclusion is based on the experimental correlations between the effective indicator of «income from services provided without VAT» and the factors such as: average capacity; ratio of use of the CAF capacities; average annual actual costs for one day of accommodation. All the dependencies obtained are adequate according to the criteria of mathematical statistics and are subject to the normal distribution law, and the statistical samples of performance of all the CAF are homogeneous both for each individual group and in general in aggregate. The determination of existing regularities of performance of various objects of the CAF demonstrates the need to further improve the statistical observation method base in the tourism sector of business, taking into account regional characteristics.

In this paper, we develop a statistical monitoring scheme for multivariate count data. In many applications involving multivariate count data, individual variables are not only correlated to each other, but also over-dispersed. Traditional statistical monitoring methods for multivariate count data that assume simple statistical models fail to fit the data collected when the underlying process is under normal working state, also referred to as the in-control state. Therefore, we propose a monitoring scheme which is based on the Poisson–multivariate Gaussian mixed model. Although such models are quite flexible, efficient statistical monitoring schemes for such models have not been developed. In this paper, we develop likelihood ratio test-based monitoring schemes that are shown to be superior to standard multivariate statistical monitoring schemes. The key challenge in developing likelihood ratio test for the Poisson–multivariate Gaussian mixed models is that the likelihood function can only be calculated by multidimensional numerical integration. We tackle this issue using an approximation of this complex likelihood function. [ABSTRACT FROM PUBLISHER]

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]

The research is aimed at statistical monitoring the global container shipping market, identifying the problems of this market and finding ways to solve them. A statistical survey of the world’s container shipping market has found that 4/5 of all traffic is carried out by sea, 1/3 of them is carried out by means of containers. It is defined that further increases in the price of oil may negatively affect the profitability of maritime carriers and the overall dynamics of global demand for container shipments. The introduction of new environmental restrictions on the content of sulphur in ship fuel from the beginning of 2020 (the so-called IMO-2020 norms) has led to a revitalization of the process of fleet modernization together with investments in the installation of ship exhaust cleaning systems. The activities of terminal and line operators have been revised in terms of development strategy with the shift of priorities to the adjacent segment – logistics (Maersk, DP World, Cosco, Contship Italia). This tendency has led to an increase in interest in feeder operations and, as a result, an increase in demand for the construction of small feeder container ships. The direct dependence of the container market on politics has been indicated. The «trade war» between the USA and China impacts the development of the container shipping market in the regional aspect. The unfavorable situation in the Asia-Europe transport market segment was largely due to political instability in Europe. It is determined that, despite the active integration of carriers into enlarged alliances and the creation of multimodal corridors of delivery of goods, most market participants recognize that the Asia-Europe railway corridor becomes a full-fledged and sometimes more profitable option for container shipping.

A novel analytic methodology for centralized statistical monitoring of the clinical trial interim enrollment performance is developed. The underlying technique uses a Poisson-gamma (PG) enrollment model developed by Anisimov and Fedorov in the previous publications. This technique is extended to modeling clinical centers performance and detecting outliers (low and high enrolling centers) using interim enrollment data and different approaches (P-values, 2D-classification). To analyze country/region performance, a new technique based on the approximation of the enrollment process in country/region by a PG process with aggregated parameters is developed. The developed methodology is applied to the analysis of the interim enrollment performance of a real clinical trial. The results support the efficiency of using this methodology in monitoring of various enrollment characteristics. [ABSTRACT FROM AUTHOR]

Monitoring the performance of a photovoltaic (PV) system when environmental parameters are not available is very difficult. Comparing the energy datasets of the arrays belonging to the same PV plant is one strategy. If the extension of a PV plant is limited, all the arrays are subjected to the same environmental conditions. Therefore, identical arrays produce the same energy amount, whatever the solar radiation and cell temperature. This is valid for small- to medium-rated power PV plants (3–50 kWp) and, moreover, this typology of PV plants sometimes is not equipped with a meteorological sensor system. This paper presents a supervision methodology based on comparing the average energy of each array and the average energy of the whole PV plant. To detect low-intensity anomalies before they become failures, the variability of the energy produced by each array is monitored by using the Bollinger Bands (BB) method. This is a statistical tool developed in the financial field to evaluate the stock price volatility. This paper introduces two modifications in the standard BB method: the exponential moving average (EMA) instead of the simple moving average (SMA), and the size of the width of BB, set to three times the standard deviation instead of four times. Until the produced energy of each array is contained in the BB, a serious anomaly is not present. A case study based on a real operating 19.8 kWp PV plant is discussed.

Summary: Mahalanobis distance is often recommended to identify patients or clinical sites that are considered unusual in clinical trials. Patients extreme in one or more covariates may be considered outliers in that they reside some distance from the multivariate mean, which can be thought of as the center of the data cloud. Less often discussed, patients whose data are believed to be "too good to be true" are located near the centroid as inliers. In order to efficiently investigate these anomalies for potential lapses in data quality, it is important to understand how the individual variables contribute to each multivariate outlier. There is a lack of literature describing a reasonable workflow for identification of outliers and their subsequent investigation to understand how each variable contributes to an observation being considered extreme. We describe how to identify multivariate inliers and outliers, classify outliers according to varying levels of severity, and summarize the contributions of variables using principal components in a manner that is accessible to a wide audience with straightforward interpretation. We illustrate how numerous data visualizations, including Pareto plots, can facilitate further review even in studies containing numerous observations and variables. We illustrate these methodologies using data from a multicenter clinical trial. [ABSTRACT FROM AUTHOR]

Background: The quality of data from clinical trials has received a great deal of attention in recent years. Of central importance is the need to protect the well-being of study participants and maintain the integrity of final analysis results. However, traditional approaches to assess data quality have come under increased scrutiny as providing little benefit for the substantial cost. Numerous regulatory guidance documents and industry position papers have described risk-based approaches to identify quality and safety issues. In particular, the position paper of TransCelerate BioPharma recommends defining risk thresholds to assess safety and quality risks based on past clinical experience. This exercise can be extremely time-consuming, and the resulting thresholds may only be relevant to a particular therapeutic area, patient or clinical site population. In addition, predefined thresholds cannot account for safety or quality issues where the underlying rate of observing a particular problem may change over the course of a clinical trial, and often do not consider varying patient exposure. Methods: In this manuscript, we appropriate rules commonly utilized for funnel plots to define a traffic-light system for risk indicators based on statistical criteria that consider the duration of patient follow-up. Further, we describe how these methods can be adapted to assess changing risk over time. Finally, we illustrate numerous graphical approaches to summarize and communicate risk, and discuss hybrid clinical-statistical approaches to allow for the assessment of risk at sites with low patient enrollment. Results: We illustrate the aforementioned methodologies for a clinical trial in patients with schizophrenia. Conclusions: Funnel plots are a flexible graphical technique that can form the basis for a riskbased strategy to assess data integrity, while considering site sample size, patient exposure, and changing risk across time. [ABSTRACT FROM AUTHOR]

Air quality management and monitoring are vital to maintaining clean air, which is necessary for the health of human, vegetation, and ecosystems. Ozone pollution is one of the main pollutants that negatively affect human health and ecosystems. This paper reports the development of an unsupervised and efficient scheme to detecting anomalies in unlabeled ozone measurements. This scheme combines a deep belief networks (DBNs) model and a one-class support vector machine (OCSVM). The DBN model accounts for nonlinear variations in the ground-level ozone concentrations, while OCSVM detects the abnormal ozone measurements. The performance of this approach is evaluated using real data from Isère in France. We also compare the detection quality of DBN-based detection schemes to that of deep stacked auto-encoders, restricted Boltzmann machines-based OCSVM, and DBN-based clustering procedures (i.e., K-means, Birch, and expectation–maximization). The results show that the developed strategy is able to identify anomalies in ozone measurements. [ABSTRACT FROM AUTHOR]

Purpose Clinical trial monitoring is an essential component of drug development aimed at safeguarding subject safety, data quality, and protocol compliance by focusing sponsor oversight on the most important aspects of study conduct. In recent years, regulatory agencies, industry consortia, and nonprofit collaborations between industry and regulators, such as TransCelerate and International Committee for Harmonization, have been advocating a new, risk-based approach to monitoring clinical trials that places increased emphasis on critical data and processes and encourages greater use of centralized monitoring. However, how best to implement risk-based monitoring (RBM) remains unclear and subject to wide variations in tools and methodologies. The nonprescriptive nature of the regulatory guidelines, coupled with limitations in software technology, challenges in operationalization, and lack of robust evidence of superior outcomes, have hindered its widespread adoption. Methods We describe a holistic solution that combines convenient access to data, advanced analytics, and seamless integration with established technology infrastructure to enable comprehensive assessment and mitigation of risk at the study, site, and subject level. Findings Using data from completed RBM studies carried out in the last 4 years, we demonstrate that our implementation of RBM improves the efficiency and effectiveness of the clinical oversight process as measured on various quality, timeline, and cost dimensions. Implications These results provide strong evidence that our RBM methodology can significantly improve the clinical oversight process and do so at a lower cost through more intelligent deployment of monitoring resources to the sites that need the most attention. [ABSTRACT FROM AUTHOR]

Monitoring vehicle traffic flow plays a central role in enhancing traffic management, transportation safety and cost savings. In this paper, we propose an innovative approach for detection of traffic congestion. Specifically, we combine the flexibility and simplicity of a piecewise switched linear (PWSL) macroscopic traffic model and the greater capacity of the exponentially-weighted moving average (EWMA) monitoring chart. Macroscopic models, which have few, easily calibrated parameters, are employed to describe a free traffic flow at the macroscopic level. Then, we apply the EWMA monitoring chart to the uncorrelated residuals obtained from the constructed PWSL model to detect congested situations. In this strategy, wavelet-based multiscale filtering of data has been used before the application of the EWMA scheme to improve further the robustness of this method to measurement noise and reduce the false alarms due to modeling errors. The performance of the PWSL-EWMA approach is successfully tested on traffic data from the three lane highway portion of the Interstate 210 (I-210) highway of the west of California and the four lane highway portion of the State Route 60 (SR60) highway from the east of California, provided by the Caltrans Performance Measurement System (PeMS). Results show the ability of the PWSL-EWMA approach to monitor vehicle traffic, confirming the promising application of this statistical tool to the supervision of traffic flow congestion. [ABSTRACT FROM AUTHOR]

Monitoring medical errors has a proven positive impact on improving health-care quality. This study is designed to discuss the statistical surveillance methods and propose useful procedures to medical practitioners for monitoring time between medical errors. Variable time between events (TBE) control charts are constructed in order to monitor time betwe en dosing errors. Results indicate that the variable TBE control charts can easily be integrated to the medical error monitoring processes. Several methods are considered to analyze the dozing error data and discussion is provided for medical decision make rs to apply a better medical error monitoring program.

There was held the assessment of the local (domestic) experience of the investment actions of information supply at the level of regions. The role of information, information supply and connection between subjects of the investment market was defined, and the ways of improvement were suggested.Проведена оценка отечественного опыта системы информационного обеспечения инвестиционной деятельности на региональном уровне. Определена роль информации, информационного обеспечения и связи между субъектами инвестиционного рынка и предложены пути улучшения ситуации.

Purpose: Monitoring is a costly requirement when conducting clinical trials. New regulatory guidance encourages the industry to consider alternative monitoring methods to the traditional 100 % source data verification (SDV) approach. The purpose of this literature review is to provide an overview of publications on different monitoring methods and their impact on subject safety data, data integrity, and monitoring cost. Methods: The literature search was performed by keyword searches in MEDLINE and hand search of key journals. All publications were reviewed for details on how a monitoring approach impacted subject safety data, data integrity, or monitoring costs. Results: Twenty-two publications were identified. Three publications showed that SDV has some value for detection of not initially reported adverse events and centralized statistical monitoring (CSM) captures atypical trends. Fourteen publications showed little objective evidence of improved data integrity with traditional monitoring such as 100 % SDV and sponsor queries as compared to reduced SDV, CSM, and remote monitoring. Eight publications proposed a potential for significant cost reductions of monitoring by reducing SDV without compromising the validity of the trial results. Conclusions: One hundred percent SDV is not a rational method of ensuring data integrity and subject safety based on the high cost, and this literature review indicates that reduced SDV is a viable monitoring method. Alternative methods of monitoring such as centralized monitoring utilizing statistical tests are promising alternatives but have limitations as stand-alone tools. Reduced SDV combined with a centralized, risk-based approach may be the ideal solution to reduce monitoring costs while improving essential data quality. [ABSTRACT FROM AUTHOR]

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]

One of the conditions of accession of Slovakia into the European Union is the existence of a developed land market. At present, the official land price is mostly used in the evaluation of agricultural land. The price was calculated on the basis of normative production and normative costs required to achieve such production. As the land market gradually develops, market prices come into being; reflecting the effect of real market forces. With the full-fledged market prices, the task of official prices will be just an informative one. The land market prices will gradually assume all the tasks of the current official prices of the agricultural land.

Background: Statistical monitoring involves the review of prospective study data collected in participating sites to detect intra/inter patients and sites inconsistencies. We report methods and results of statistical monitoring in a phase IV clinical trial., Method: PRO-MSACTIVE is a study evaluating ocrelizumab in active relapsing multiple sclerosis (RMS) patients in France. Specific statistical methods (volcano plots, mahalanobis distance, funnel plot …) have been applied to a SDTM database to detect potential issues. R-Shiny application was developed to generate an interactive web application in order to ease site and/or patients identification during statistical data review meetings., Results: The PRO-MSACTIVE study enrolled 422 patients in 46 centers between July 2018 and August 2019. Three data review meetings were held between April and October 2019 and 14 standard and planned tests were run on study data, with a total of 15 (32.6%) sites identified as needing review or investigation. Overall 36 findings were identified during the meetings: duplicate records, outliers, inconsistent delays between dates., Conclusion: Statistical monitoring is useful to identify unusual or clustered data patterns that might be revealing issues that could impact the data integrity and/or may potentially impact patients' safety. With anticipated and appropriate interactive data visualization, early signals can easily be identified or reviewed by the study team and appropriate actions be set up and assigned to the most appropriate function for a close follow-up and resolution. Interactive statistical monitoring is time consuming to initiate using R-Shiny, but is time saving after the 1st data review meeting (DRV).(ClinicalTrials.gov identifier: NCT03589105; EudraCT identifier: 2018-000780-91)., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Pau, Marie Lotz, Gaelle Grandclaude and Alexandre Civet are employees of Roche SAS. Romain Jegou is an employee of Keyrus Life Science., (© 2023 The Authors.)

Background: Data quality issues in clinical trials can be caused by a variety of behaviors including fraud, misconduct, intentional or unintentional noncompliance, and significant carelessness. Regardless of how these behaviors are defined, they may compromise the validity of the study results. Reliable study results and quality data are needed to evaluate products for marketing approval and for decisions that are made on the use of medicine. This article focuses on detecting data quality issues, irrespective of origin or motive. Early detection of data quality issues are important so that corrective actions taken can be implemented during the conduct of the trial, recurrence can be prevented, and data quality can be preserved. Methods: A survey was distributed to TransCelerate member companies to assess current strategies for detecting and mitigating risks involving fraud and misconduct in clinical trials. A review of literature across many industries from 1985 to 2014 was conducted using multiple platforms. Results: Eighteen TransCelerate member companies anonymously responded to the survey. All of the respondents had one or more existing strategies for fraud and misconduct detection. The literature search identified current practices and methodologies across many industries. Conclusions: TransCelerate recommends the creation of an integrated, multifaceted approach to proactively detect data quality issues. Detection methods should include a strategy tailored to the characteristics of the study. Some sponsors are taking advantage of more advanced methods and integrated processes and systems to proactively detect and address issues, relying on advances in technology to more efficiently review data in real time. Further research is underway to assess statistical data quality detection methodology in clinical trials. [ABSTRACT FROM AUTHOR]

Multicenter studies are widely used to meet accrual targets in clinical trials. Clinical data monitoring is required to ensure the quality and validity of the data gathered across centers. One approach to this end is central statistical monitoring, which aims at detecting atypical patterns in the data by means of statistical methods. In this context, we consider the simple case of a continuous variable, and we propose a detection procedure based on a linear mixed-effects model to detect location differences between each center and all other centers. We describe the performance of the procedure as a function of contamination rate and signal-to-noise ratio. We investigate the effect of center size and variance structure and illustrate the use of the procedure using data from two multicenter clinical trials. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Monitoring medical errors has a proven positive impact on improving health-care quality. This study is designed to discuss the statistical surveillance methods and propose useful procedures to medical practitioners for monitoring time between medical errors. Variable time between events (TBE) control charts are constructed in order to monitor time between dosing errors. Results indicate that the variable TBE control charts can easily be integrated to the medical error monitoring processes. Several methods are considered to analyze the dozing error data and discussion is provided for medical decision makers to apply a better medical error monitoring program. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis for dynamic processes is an intensively investigated area. However, the problem of determining whether or not system faults can be successfully detected based on the output measurements for a given dynamic process remains an open research topic. An intrinsic definition of fault detectability in multivariate dynamic processes is proposed in this paper. It defines the detectability in an intrinsic manner as a system property, without any reference to any specific fault detection algorithm. Furthermore, the relationship between system structure and the detectability for mean change faults and variability change faults are investigated. Analytical criteria for checking the system detectability are established. The results presented in this paper can provide guidelines on system design improvement for process monitoring and control. A case study is presented that illustrates the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The current generation of vehicle models are increasingly being equipped with on-board diagnostic (OBD) systems aimed at assessing the ‘state of health’ of important anti-pollution subsystems and components. In order to promptly diagnose and fix quality and reliability problems that may potentially affect such complex diagnostic systems, even during advanced development prior to mass production, some vehicle prototypes undergo a testing phase under realistic conditions of use (a mileage accumulation campaign). The aim of this work is to set up a statistical tool for improving the reliability of the OBD system by monitoring its operation during the mileage accumulation campaign of a new vehicle model. A dedicated software program was developed by the authors to filter the large experimental database recorded during the mileage accumulation campaign and to extract the time series of the diagnostic indices to be analysed. A model-based monitoring approach, using continuous time autoregressive (CAR) models for the time-series structure and traditional control charts for the estimated residuals, is adopted. A Kalman recursion procedure for the estimation of the unknown CAR model parameters is described. An application of the proposed approach is presented for a diagnostic index related to the state of health of the oxygen sensor. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Automotive on-board diagnostic (OBD) systems are designed to keep critical components under control during vehicle functioning, and to alert the driver in case of severe malfunctions. OBD systems aimed at reducing polluting emissions are mandatory on new motor vehicles. Some research projects conducted in cooperation between universities and the automotive industry have been quite successful in terms of knowledge advancement and industrial gain. An updated overview of the adopted methodologies and results obtained are given in this article. Such results can be valuable for both theorists and practitioners, since they witness the use of statistics as a powerful catalyst of technical progress, and give a possible line of action for further application in several scientific fields. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Monitoring the performance of a photovoltaic (PV) system when environmental parameters are not available is very difficult. Comparing the energy datasets of the arrays belonging to the same PV plant is one strategy. If the extension of a PV plant is limited, all the arrays are subjected to the same environmental conditions. Therefore, identical arrays produce the same energy amount, whatever the solar radiation and cell temperature. This is valid for small- to medium-rated power PV plants (3–50 kWp) and, moreover, this typology of PV plants sometimes is not equipped with a meteorological sensor system. This paper presents a supervision methodology based on comparing the average energy of each array and the average energy of the whole PV plant. To detect low-intensity anomalies before they become failures, the variability of the energy produced by each array is monitored by using the Bollinger Bands (BB) method. This is a statistical tool developed in the financial field to evaluate the stock price volatility. This paper introduces two modifications in the standard BB method: the exponential moving average (EMA) instead of the simple moving average (SMA), and the size of the width of BB, set to three times the standard deviation instead of four times. Until the produced energy of each array is contained in the BB, a serious anomaly is not present. A case study based on a real operating 19.8 kWp PV plant is discussed. [ABSTRACT FROM AUTHOR]