Design and properties of the predictive ratio cusum (PRC) control charts

In statistical process control/monitoring (SPC/M), memory-based control charts aim to detect small/medium persistent parameter shifts. When a phase I calibration is not feasible, self-starting methods have been proposed, with the predictive ratio cusum (PRC) being one of them. To apply such methods in practice, one needs to derive the decision limit threshold that will guarantee a preset false alarm tolerance, a very difficult task when the process parameters are unknown and their estimate is sequentially updated. Utilizing the Bayesian framework in PRC, we will provide the theoretic framework that will allow to derive a decision-making threshold, based on false alarm tolerance, which along with the PRC closed-form monitoring scheme will permit its straightforward application in real-life practice. An enhancement of PRC is proposed, and a simulation study evaluates its robustness against competitors for various model type misspecifications. Finally, three real data sets (normal, Poisson, and binomial) illustrate its implementation in practice. Technical details, algorithms, and R-codes reproducing the illustrations are provided as supplementary material

Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs

The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a persistent shift. Self-starting methods, both in the frequentist and the Bayesian domain aim to offer a solution. Adopting the latter perspective, we propose a general closed-form Bayesian scheme, where the testing procedure is built on a memory-based control chart that relies on the cumulative ratios of sequentially updated predictive distributions. The theoretic framework can accommodate any likelihood from the regular exponential family and the use of conjugate analysis allows closed form modeling. Power priors will offer the axiomatic framework to incorporate into the model different sources of information, when available. A simulation study evaluates the performance against competitors and examines aspects of prior sensitivity. Technical details and algorithms are provided as supplementary material

Sympathetic Activation in Deadlines of Deskbound Research - A Study in the Wild

Paper and proposal deadlines are important milestones, conjuring up emotional memories to researchers. The question is if in the daily challenging world of scholarly research, deadlines truly incur higher sympathetic loading than the alternative. Here we report results from a longitudinal, in the wild study of n = 10 researchers working in the presence and absence of impeding deadlines. Unlike the retrospective, questionnaire-based studies of research deadlines in the past, our study is real-time and multimodal, including physiological, observational, and psychometric measurements. The results suggest that deadlines do not significantly add to the sympathetic loading of researchers. Irrespective of deadlines, the researchers' sympathetic activation is strongly associated with the amount of reading and writing they do, the extent of smartphone use, and the frequency of physical breaks they take. The latter likely indicates a natural mechanism for regulating sympathetic overactivity in deskbound research, which can inform the design of future break interfaces

Scope actuation system for articulated laparoscopes

Background: An articulated laparoscope comprises a rigid shaft with an articulated distal end to change the viewing direction. The articulation provides improved navigation of the operating field in confined spaces. Furthermore, incorporation of an actuation system tends to enhance the control of an articulated laparoscope. Methods: A preliminary prototype of a scope actuation system to maneuver an off-the-shelf articulated laparoscope (EndoCAMaleon by Karl Storz, Germany) was developed. A user study was conducted to evaluate this prototype for the surgical paradigm of video-assisted thoracic surgery. In the study, the subjects maneuvered an articulated scope under two modes of operation: (a) actuated mode where an operating surgeon maneuvers the scope using the developed prototype and (b) manual mode where a surgical assistant directly maneuvers the scope. The actuated mode was further assessed for multiple configurations based on the orientation of the articulated scope at the incision. Results: The data show the actuated mode scored better than the manual mode on all the measured performance parameters including (a) total duration to visualize a marked region, (a) duration for which scope focus shifts outside a predefined visualization region, and (c) number of times for which scope focus shifts outside a predefined visualization region. Among the different configurations tested using the actuated mode, no significant difference was observed. Conclusions: The proposed articulated scope actuation system facilitates better navigation of an operative field as compared to a human assistant. Secondly, irrespective of the orientation in which an articulated scope’s shaft is inserted through an incision, the proposed actuation system can navigate and visualize the operative field

A generic scope actuation system for flexible endoscopes

Background: A scope actuation system assists a surgeon in steering a scope for navigating an operative field during an interventional or diagnostic procedure. Each system is tailored for a specific surgical procedure. The development of a generic scope actuation system could assist various laparoscopic and endoscopic procedures. This has the potential to reduce the deployment and maintenance costs for a hospital, making it more accessible for clinical usage. Methods: A modular actuation system (for maneuvering rigid laparoscopes) was adapted to enable incorporation of flexible endoscopes. The design simplifies the installation and disassembly processes. User studies were conducted to assess the ability of the system to focus onto a diagnostic area, and to navigate during a simulated esophagogastroduodenoscopy procedure. During the studies, the endoscope was maneuvered with (robotic mode) and without (manual mode) the actuation system to navigate the endoscope’s focus on a predefined track. Results: Results show that the robotic mode performed better than the manual mode on all the measured performance parameters including (a) the total duration to traverse a track, (b) the percentage of time spent outside a track while traversing, and (c) the number of times the scope focus shifts outside the track. Additionally, robotic mode also reduced the perceived workload based on the NASA-TLX scale. Conclusions: The proposed scope actuation system enhances the maneuverability of flexible endoscopes. It also lays the groundwork for future development of modular and generic scope assistant systems that can be used in both laparoscopic and endoscopic procedures

Dissecting Driver Behaviors Under Cognitive, Emotional, Sensorimotor, and Mixed Stressors

In a simulation experiment we studied the effects of cognitive, emotional, sensorimotor, and mixed stressors on driver arousal and performance with respect to (wrt) baseline. In a sample of n = 59 drivers, balanced in terms of age and gender, we found that all stressors incurred significant increases in mean sympathetic arousal accompanied by significant increases in mean absolute steering. The latter, translated to significantly larger range of lane departures only in the case of sensorimotor and mixed stressors, indicating more dangerous driving wrt baseline. In the case of cognitive or emotional stressors, often a smaller range of lane departures was observed, indicating safer driving wrt baseline. This paradox suggests an effective coping mechanism at work, which compensates erroneous reactions precipitated by cognitive or emotional conflict. This mechanisms’ grip slips, however, when the feedback loop is intermittently severed by sensorimotor distractions. Interestingly, mixed stressors did not affect crash rates in startling events, suggesting that the coping mechanism’s compensation time scale is above the range of neurophysiological latency

Introduction to bayesian inference and its application in medical biology

In statistics the data become available via a random number generator mechanism, known as distribution, that depends on some unknown parameter(s). Two schools of thought exist in statistics that handle the unknown parameter(s) with a different philosophy: the Frequentist and the Bayesian. In the frequentist-based approach the unknown parameter is treated as a fixed unknown constant. The probability assessment is then based on the long-term frequency of an infinite number of replications of the experiment. On the contrary, the bayesian approach uses probability theory to quantify the unknown parameter. Precisely, adopting a subjective bayesian approach, we can incorporate into the prior distribution for the unknown parameter(s) any relevant information that we have (from our experience, from expert’s opinion etc.). In absence of prior knowledge, a non-informative (objective) prior could be employed. Once the data will arrive, the Bayes theorem will update the prior to posterior distribution and provide via decision theory a more user-friendly environment for statistical inference. Furthermore, one can derive the predictive distribution where we can talk about the uncertainty of future observable(s), given the available data only, i.e. the unknown parameter has been integrated out of the problem. Apart from better interpretability, the bayesian methods are also sequentially updated, offering the option to build methods that are up and running with even very few data (i.e. they are considered as self-starting methods). Furthermore, as more data become available the effect of the prior distribution decreases. In the field of Statistical Process Control and Monitoring the bayesian perspective provides unique tools that allow to perform (internal or external) quality control in an online fashion. The bayesian tools are capable of being self-starting, that is they break free from the necessity of a startup (calibration) phase, which is needed from the basic frequentist-based control charts. Within the bayesian framework two major tools for quality monitoring that are based on the predictive distribution have been developed: PCC: Predictive Control Charts [1], for detecting transient shifts of large magnitude (outliers) and the PRC: Predictive Ratio Cusum [2, 3]), for detection of persistent shifts of medium/small size (Predictive Residual Cusum is the special case of PRC, when we handle normal data). Jointly PCC and PRC provide a framework that is capable to provide efficient quality monitoring, outperforming standard frequentist-based alternatives

Sequential detection framework for real-time biosurveillance based on Shiryaev-Roberts procedure with illustrations using COVID-19 incidence data

This article develops a detection framework using Bayesian philosophy by adaptation of Shiryaev's and Roberts' methodology. We propose two unifying versions directly applicable in industrial process control and easily extendable to public health infectious disease surveillance via some data detrending and/or demodulation. The root idea uses the sum of likelihood ratios upon which an optimal stopping criterion is based. It sets a prior on the epoch of a change, allows the flexibility to elicit a prior distribution on other process parameters, and attempts to minimize an expected loss function. A sensitivity analysis is conducted for validation and performance assessment and analytical formulas are derived. The methods are successfully applied to the European Union Centre for Disease Control (ECDC) open-source global COVID-19 incidence data. We further lay out scenarios where interest may switch to the detection of separate outbreaks with similar syndromes during an already evolving epidemic. We view our approach as a toolkit with a potential to augment early reports to sentinels in syndromic surveillance and in biosurveillance

Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs

Performing online monitoring for short horizon data is a challenging, though cost effective benefit. Self-starting methods attempt to address this issue adopting a hybrid scheme that executes calibration and monitoring simultaneously. In this work, we propose a Bayesian alternative that will utilize prior information and possible historical data (via power priors), offering a head-start in online monitoring, putting emphasis on outlier detection. For cases of complete prior ignorance, the objective Bayesian version will be provided. Charting will be based on the predictive distribution and the methodological framework will be derived in a general way, to facilitate discrete and continuous data from any distribution that belongs to the regular exponential family (with Normal, Poisson and Binomial being the most representative). Being in the Bayesian arena, we will be able to not only perform process monitoring, but also draw online inference regarding the unknown process parameter(s). An extended simulation study will evaluate the proposed methodology against frequentist based competitors and it will cover topics regarding prior sensitivity and model misspecification robustness. A continuous and a discrete real data set will illustrate its use in practice. Technical details, algorithms, guidelines on prior elicitation and R-codes are provided in appendices and supplementary material. Short production runs and online phase I monitoring are among the best candidates to benefit from the developed methodology