Statistical Inference Techniques Research Articles

Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials

Numerous studies have shown that older women with endometrial cancer have a higher risk of recurrence and cancer-related death. However, it remains unclear whether older age is a causal prognostic factor, or whether other risk factors become increasingly common with age. We aimed to address this question with a unique multimethod study design using state-of-the-art statistical and causal inference techniques on datasets of three large, randomised trials. In this multimethod analysis, data from 1801 women participating in the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials were used for statistical analyses and causal inference. The cohort included 714 patients with intermediate-risk endometrial cancer, 427 patients with high-intermediate risk endometrial cancer, and 660 patients with high-risk endometrial cancer. Associations of age with clinicopathological and molecular features were analysed using non-parametric tests. Multivariable competing risk analyses were performed to determine the independent prognostic value of age. To analyse age as a causal prognostic variable, a deep learning causal inference model called AutoCI was used. Median follow-up as estimated using the reversed Kaplan-Meier method was 12·3 years (95% CI 11·9-12·6) for PORTEC-1, 10·5 years (10·2-10·7) for PORTEC-2, and 6·1 years (5·9-6·3) for PORTEC-3. Both overall recurrence and endometrial cancer-specific death significantly increased with age. Moreover, older women had a higher frequency of deep myometrial invasion, serous tumour histology, and p53-abnormal tumours. Age was an independent risk factor for both overall recurrence (hazard ratio [HR] 1·02 per year, 95% CI 1·01-1·04; p=0·0012) and endometrial cancer-specific death (HR 1·03 per year, 1·01-1·05; p=0·0012) and was identified as a significant causal variable. This study showed that advanced age was associated with more aggressive tumour features in women with endometrial cancer, and was independently and causally related to worse oncological outcomes. Therefore, our findings suggest that older women with endometrial cancer should not be excluded from diagnostic assessments, molecular testing, and adjuvant therapy based on their age alone. None.

Recent advances on mechanisms of network generation: Community, exchangeability, and scale‐free properties

AbstractThe mechanisms of network generation have undergone extensive analysis and found broad applications in various real‐world scenarios. Among the fruitful literature on network models, numerous studies seek to explore and interpret fundamental graph structure properties, including the clustering effect, exchangeability, and scale‐free properties. In this paper, we present a comprehensive review of the statistical modeling methods for the mechanisms of network generation. We specifically focus on three representative classes of models, namely the stochastic block models, the exchangeable network models, and the preferential attachment models. For each model type, our approach begins by reviewing existing methods and model setups, followed by an exploration of the core modeling principles behind them. We also summarize relevant statistical inference techniques and provide a unified understanding of theoretical analyses. Furthermore, we emphasize several challenges and open problems that could shed light on future research. We conclude this review with the identification of some possible directions for future study.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms Algorithms and Computational Methods > Networks and Security

“Determining the efficacy of a machine learning model for measuring periodontal bone loss”

BackgroundConsidering the prevalence of Periodontitis, new tools to help improve its diagnostic workflow could be beneficial. Machine Learning (ML) models have already been used in dentistry to automate radiographic analysis.AimsTo determine the efficacy of an ML model for automatically measuring Periodontal Bone Loss (PBL) in panoramic radiographs by comparing it to dentists.MethodsA dataset of 2010 images with and without PBL was segmented using Label Studio. The dataset was split into n = 1970 images for building a training dataset and n = 40 images for building a testing dataset. We propose a model composed of three components. Firstly, statistical inference techniques find probability functions that best describe the segmented dataset. Secondly, Convolutional Neural Networks extract visual information from the training dataset. Thirdly, an algorithm calculates PBL as a percentage and classifies it in stages. Afterwards, a standardized test compared the model to two radiologists, two periodontists and one general dentist. The test was built using the testing dataset, 40 questions long, done in controlled conditions, with radiologists considered as ground truth. Presence or absence, percentage, and stage of PBL were asked, and time to answer the test was measured in seconds. Diagnostic indices, performance metrics and performance averages were calculated for each participant.ResultsThe model had an acceptable performance for diagnosing light to moderate PBL (weighted sensitivity 0.23, weighted F1-score 0.29) and was able to achieve real-time diagnosis. However, it proved incapable of diagnosing severe PBL (sensitivity, precision, and F1-score = 0).ConclusionsWe propose a Machine Learning model that automates the diagnosis of Periodontal Bone Loss in panoramic radiographs with acceptable performance.

THE EFFECT OF TGT-PUZZLE COOPERATIVE LEARNING TO IMPROVE LEARNING ACHIEVEMENT ON MOLECULAR GEOMETRIC MATERIAL

Chemistry can be divided into two concepts: concrete concepts and defined concepts. The geometris of a molecule is one of the defining concepts, because ideas are at the molecular level and are invisible to the naked eye. Therefore, this study was conducted to determine the effect of the TGT Puzzle cooperative learning strategy on the success of learning the material of molecular form. The purpose of this study was to determine whether learning achievement increased after learning the TGT Puzzle cooperative learning strategy in class X IPA MA Hidayatul Ummah Pringgoboyo. This study uses statistical inference technique using paired sample t-test to determine whether learning performance increases after learning with the TGT Puzzle cooperative learning strategy. The method of analysis used paired sample t-test with a significant difference (sig) <0.05. The results of this study indicate an increase in learning achievement after learning with the TGT Puzzle cooperative learning strategy.

Understanding Capacity Fade in Organic Redox-Flow Batteries by Combining Spectroscopy with Statistical Inference Techniques

Aqueous-soluble organic and organometallic redox-active molecules show considerable promise as redox-flow battery (RFB) reactants because their costs at scale are projected to be low. However, most aqueous organic RFB undergo rapid degradation resulting in high rates of capacity fade. Considerable research has been devoted towards discovering chemically stable charge carriers for RFBs. However, the decomposition mechanisms of underperforming chemistries during flow cell cycling are often poorly understood, and in some instances, competing or incompatible hypotheses about the relationship between reactant decay and capacity fade have often been proposed.In this talk, we will demonstrate that combining spectroscopic and statistical inference techniques can be used to shed light on relationship between RFB reactant degradation and capacity fade. Specifically, we employ in operando ultraviolet-visible spectrophotometry along with Bayesian inference, Bayesian model selection, and multivariate curve regression in order to quantify the relative probabilities of different decay mechanisms given the data at hand, and to estimate rate constants for specific reactions within the most probable decay scheme. In principle, rate constants thus obtained can be fed into a zero-dimensional electrochemical flow cell model, recently developed by our group, that can simulate capacity fade given a reactant decay mechanism, associated rates, and the cycling protocol. This work has the potential to shed light on the relationship between capacity fade and reactant decay in various organic RFB chemistries, and to enable prediction of the lifetimes of commercial-scale RFBs.

A gender-oriented analysis of digital skills and ICT use intensity in tourism companies in Extremadura (Spain)

PurposeThis study aims to use a novel approach, focusing on the manager’s gender, to explore whether it acts as a differentiator in the following aspects of tourist accommodation companies in Extremadura (Spain): the level of information and communication technology (ICT) specialisation of employees, managers’ knowledge of ICTs and the social media and online tourism platform use intensity of managers.Design/methodology/approachA questionnaire was sent to 238 accommodation companies. The data collected from the questionnaire were analysed using statistical inference techniques and linear and logistic regression.FindingsIn general, ICT specialist profiles are more common amongst the employees of male-led companies. Male managers also use Booking and analyse online feedback more intensively. There appear to be no gender-based differences in terms of the ICT knowledge of managers.Practical implicationsThese results highlight issues of major practical interest for the sector’s managers and decision makers, especially in Extremadura. They reveal the digital divide in certain aspects between men- and women-led firms in Extremadura. This finding has important consequences for the sector in terms of competitiveness. It highlights the need to continue working to eradicate gender gaps in digital settings.Originality/valueThe study shows the role of the manager’s gender as a differentiating factor in terms of the existence of specialist ICT profiles and ICT use intensity in tourism companies. To the best of the authors’ knowledge, this study provides the first evidence of such a finding for the tourism sector in general, as well as for the specific case of a rural destination such as Extremadura.

Granger causality-based cluster sequence mining for spatio-temporal causal relation mining

We proposed a method to extract causal relations of spatial clusters from multi-dimensional event sequence data, also known as a spatio-temporal point process. The proposed Granger cluster sequence mining algorithm identifies the pairs of spatial data clusters that have causality over time with each other. It extended the cluster sequence mining algorithm, which utilized a statistical inference technique to identify the occurrence relation, with a causality inference based on the Granger causality. In addition, the proposed method utilizes a false discovery rate procedure to control the significance of the causality. Based on experiments on both synthetic and semi-real data, we confirmed that the algorithm is able to extract the synthetic causal relations from multiple different sets of data, even when disturbed with high level of spatial noise. False discovery rate procedure also helps to increase the accuracy even more under such case and also make the algorithm less-sensitive to the hyperparameters.

Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques

Organic redox-active molecules are attractive as redox-flow battery (RFB) reactants because of their low anticipated costs and widely tunable properties. Unfortunately, many lab-scale flow cells experience rapid material degradation (from chemical and electrochemical decay mechanisms) and capacity fade during cycling (>0.1%/day) hindering their commercial deployment. In this work, we combine ultraviolet-visible spectrophotometry and statistical inference techniques to elucidate the Michael attack decay mechanism for 4,5-dihydroxy-1,3-benzenedisulfonic acid (BQDS), a once-promising positive electrolyte reactant for aqueous organic redox-flow batteries. We use Bayesian inference and multivariate curve resolution on the spectroscopic data to derive uncertainty-quantified reaction orders and rates for Michael attack, estimate the spectra of intermediate species and establish a quantitative connection between molecular decay and capacity fade. Our work illustrates the promise of using statistical inference to elucidate chemical and electrochemical mechanisms of capacity fade in organic redox-flow battery together with uncertainty quantification, in flow cell-based electrochemical systems.

Reconciling art and science in the era of personalised medicine: the legacy of Georges Canguilhem

BackgroundBiomedicine, i.e. the application of basic sciences to medicine, has become the cornerstone for the study of etiopathogenesis and treatment of diseases. Biomedicine has enormously contributed to the progress of medicine and healthcare and has become the preferred approach to medical problems in the West. The developments in statistical inference and machine learning techniques have provided the foundation for personalised medicine where clinical management can be fully informed by biomedicine. The deployment of precision medicine may impact the autonomy and self-normativity of the patients. Understanding the relationship between biomedicine and medical practice can help navigate the benefits and challenges offered by precision medicine.MethodsConventional content analysis was applied to “Le Normal and le Pathologique” (Canguilhem G. The Normal and the Pathological. Princeton: Princeton University Press; 1991) and further investigated with respect to its relationship with techne and precision medicine using PubMed and Google Scholar and the Standford Encyclopedia of Philosophy to search for the following keywords singularly or in combination: “Canguilhem”, “techne”, “episteme”, “precision medicine”, “machine learning AND medicine”.ResultsThe Hippocratic concept of techne accounts for many characteristics of medical knowledge and practice. The advances of biomedicine, experimental medicine and, more recently, machine learning offer, in contrast, the model of a medicine based purely on episteme. I argue that Canguilhem medical epistemology establishes a framework where episteme and data-driven medicine is compatible with the promotion of patient’s autonomy and self-normativity.ConclusionsCanguilhem’s medical epistemology orders the relationship of applied medicine with experimental sciences, ethics and social sciences. It provides guidance to define the scope of medicine and the boundaries of medicalization of healthy life. Finally, it sets an agenda for a safe implementation of machine learning in medicine.

Architecture of Deep Learning and Its Applications

Recently, Deep Learning (DL) has accomplished enormous prosperity in various areas, like natural language processing (NLP), image processing, different medical issues and computer vision. Both Machine Learning (ML) and DL as compared to traditional methods, can learn and make better and enhanced use of datasets for feature extraction. This paper is divided into three parts. The first part introduces a detailed information about different characteristics and learning types in terms of learning problems, hybrid learning problems, statistical inference and learning techniques; besides to an exhausted historical background about feature learning and DL. The second part is about the major architectures of DL with mathematical equations and clarified examples. These architectures include Autoencoders (AEs), Generative Adversarial Networks (GANs), Deep Belief Networks (DBNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Recursive Neural Networks. The third part of this work represents an overview with detailed explanation about different applications and use-cases. Finally, the fourth part is about hardware/ software tools used with DL. Index Terms— Deep learning, Machine Learning, Neural Network, Network architecture.

Quantifying changes in the T cell receptor repertoire during thymic development.

One of the feats of adaptive immunity is its ability to recognize foreign pathogens while sparing the self. During maturation in the thymus, T cells are selected through the binding properties of their antigen-specific T-cell receptor (TCR), through the elimination of both weakly (positive selection) and strongly (negative selection) self-reactive receptors. However, the impact of thymic selection on the TCR repertoire is poorly understood. Here, we use transgenic Nur77-mice expressing a T-cell activation reporter to study the repertoires of thymic T cells at various stages of their development, including cells that do not pass selection. We combine high-throughput repertoire sequencing with statistical inference techniques to characterize the selection of the TCR in these distinct subsets. We find small but significant differences in the TCR repertoire parameters between the maturation stages, which recapitulate known differentiation pathways leading to the CD4+ and CD8+ subtypes. These differences can be simulated by simple models of selection acting linearly on the sequence features. We find no evidence of specific sequences or sequence motifs or features that are suppressed by negative selection. These results favour a collective or statistical model for T-cell self non-self discrimination, where negative selection biases the repertoire away from self recognition, rather than ensuring lack of self-reactivity at the single-cell level.

Design and Conceptual Development of a Novel Hybrid Intelligent Decision Support System Applied towards the Prevention and Early Detection of Forest Fires

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas.

Evaluation of the Problems Militating Against Effective Implementation of the Introduced Cultural and Creative Arts Curriculum of the Upper Basic Education

Obviously, the introduced Cultural and Creative Arts (CCA) subject curriculum for upper basic education has not yielded the laudable objectives as expected. This study therefore has x-rayed the problems militating against its effective implementation. A sample of sixty (60) music teachers of Universal Basic Education (UBE) 1-3, derived from the six (6) educational zones in Anambra State was used for the study. A structured questionnaire containing twenty seven (27) test items arranged in clusters was used for data gathering. The data analysis was done using frequencies, percentages and Chi Square (χ2) statistical inference techniques under competence, material, attitude, managerial and methodology clusters. It was found that when all the test items were collated, 81.5% of them negatively affected the introduction of CCA. A paltry 7.4% positively affected the introduction while 11.1% were insignificant test items. The problems adversely affecting the introduced CCA curriculum include insufficient and unqualified music teachers, non uniformity of standards, reduction of scope, insufficient time allocation, irrelevant contents in achieving objectives, inequality in distribution, scarcity of indigenous contents, insufficient practical, improper coordination, diffusion of sense of responsibility, inefficient harmonization, lack of evaluation details, lack of skills in playing musical instruments, etc. The study proffered possible solutions with a view towards achieving the basic education needs that resulted in the curriculum.

Bayesian Inference for Stochastic Cusp Catastrophe Model with Partially Observed Data

The purpose of this paper is to develop a data augmentation technique for statistical inference concerning stochastic cusp catastrophe model subject to missing data and partially observed observations. We propose a Bayesian inference solution that naturally treats missing observations as parameters and we validate this novel approach by conducting a series of Monte Carlo simulation studies assuming the cusp catastrophe model as the underlying model. We demonstrate that this Bayesian data augmentation technique can recover and estimate the underlying parameters from the stochastic cusp catastrophe model.

Mixed reality or LEGO game play? Fostering social interaction in children with Autism

This study extends the previous research in which it has been shown that a mixed reality (MR) system fosters social interaction behaviours (SIBs) in children with Autism Spectrum Condition (ASC). When comparing this system to a LEGO-based non-digital intervention, it has been observed that an MR system effectively mediates a face-to-face play session between a child with ASC and a child without ASC providing new specific advantageous properties (e.g. not being a passive tool, not needing to be guided by the therapist). Considering the newly collected multimodal data totaling to 72 children (36 trials of dyads, child with ASC/child without ASC), a first goal of the present study is to apply detailed statistical inference and machine learning techniques to extensively evaluate the overall effect of this MR system, when compared to the LEGO condition. This goal also includes the analysis of psychophysiological data and allows the context-driven triangulation of the multimodal data which is operationalized by (i) video-coding of SIBs, (ii) psychophysiological data, and (iii) system logs of user-system events. A second goal is to show how SIBs, taking place in these experiences, are influenced by the internal states of the users and the system. SIBs were measured by video-coding overt behaviours (Initiation, Response and Externalization) and with self-reports. Internal states were measured using a wearable device designed by the FuBIntLab (Full-Body Interaction Lab) to acquire: Electrocardiogram (ECG) and Electrodermal Activity (EDA) data. Affective sliders and State Trait Anxiety Scale questionnaires were used as self-reports. Repeated-measures design was chosen with two conditions, the MR environment and the traditional therapy LEGO. The results show that the MR system has a positive effect on SIBs when compared to the LEGO condition, with an added advantage of being more flexible.

Bayesian network approach to process data reconciliation with state uncertainties and recycle streams

Data reconciliation is a widely utilised technique in process industries to obtain consistent estimates of the process variables from measurements corrupted with random error and gross error, taking process models as constraint. In the existing formulations for data reconciliation, process models are assumed to be error free. However, in practice, process models can suffer from model inaccuracies, leading to uncertainties in states. This paper introduces a new method for data reconciliation developed in the framework of Bayesian network, accounting for the state uncertainties. The solution is obtained by utilising a Bayesian network model translated from the process model and using statistical inference techniques to estimate the reconciled values of the states. A novel method to construct acyclic Bayesian network for process networks with recycle streams is proposed. This method is also extended for data reconciliation of partially measured systems. The proposed data reconciliation schemes is demonstrated on two case studies.

On multivariate copula modeling of dependent degradation processes

Multivariate degradation processes have been observed in many engineering systems. Most existing multivariate degradation modeling techniques, such as multivariate general path models or multivariate Wiener process models, assume an underlying Gaussian dependence structure. Unfortunately, in reality, the dependencies among degradation processes are often nonlinear, asymmetric and greatly tail-skewed, and thus limit the usefulness of the conventional modeling techniques in practice. To overcome these limitations, in this paper, we develop a copula-based multivariate modeling framework. Three fundamental copula classes are applied to model the complex dependence structure among correlated degradation processes. Statistical inference and model selection techniques, including two graphical diagnostic tools, a test of independence and a goodness-of-fit test, are employed to identify the best model. The advantages of the proposed modeling framework are demonstrated through simulation studies. And we also discuss the effect of ignoring tail dependence on system failure probability assessment. Finally, the applications of the copula-based multivariate degradation models on both system reliability evaluation and remaining useful life prediction are provided. The proposed methodology is illustrated using a numerical example.

Estudio descriptivo del síndrome confusional agudo en Urgencias

ObjectiveTo better understand the clinical, functional and analytical variables associated with delirium in emergencies and their evolution in order to obtain an improvement in the therapeutic approach of the elderly patient, thus preventing morbidity and mortality in this type of patient. DesignThis is a prospective descriptive study of acute confusional syndrome in the emergency department. SiteGeneral University Hospital of Ciudad Real. ParticipantsAll patients from the emergency department with a diagnosis of delirium were included in the 24-h interval following admission to the geriatric service. Main measurementsAn analysis of the variables of the data set (sociodemographic and clinical variables) was performed, calculating frequency tables for qualitative variables and descriptive statistics for quantitative variables. Subsequently, statistical inference techniques have been used. ResultsThe most frequent medical antecedent were neurological pathology and hypertension, followed by rheumatologic diseases. The main reasons for consultation were deterioration in general condition, dyspnea, decreased level of consciousness, and fever. Highlight the incidence of polypharmacy, especially drugs such as diuretics, benzodiazepines or hypnotics. In relation to the main etiology, the role of urinary and respiratory infections is noteworthy. ConclusionsHighlight the fundamental role of neurological diseases (especially dementia), hypertension, polypharmacy (inappropriate use of benzodiazepines and hypnotics) and urinary and respiratory infections as treatable and/or preventable factors of delirium in Primary Care patients in our setting.

New probabilistic S-N curves modeling method with small-sample test data of composite materials

Considering the large dispersion when processing small-sample fatigue test data of composite materials, a new method for modeling probabilistic S- N curves is proposed in terms of the equivalent fatigue lives. An equivalent fatigue life conversion model is first established based on two fundamental assumptions to improve the small-sample information utilized. Subsequently, a backward statistical inference technique improved by particle swarm optimization is used to determine probabilistic S- N curves through the equivalent fatigue lives. Finally, the proposed method is verified in terms of precision and stability by the fatigue test data of carbon eight-harness-satin/epoxy laminate. The results indicate that the proposed method can offer an accurate description of the probabilistic behavior of composite materials with small-sample test data.

Automated Analysis Using a Bayesian Functional Mixed-Effects Model With Gaussian Process Responses for Wavelet Spectra of Spatiotemporal Colonic Manometry Signals.

Manual analysis of human high-resolution colonic manometry data is time consuming, non-standardized and subject to laboratory bias. In this article we present a technique for spectral analysis and statistical inference of quasiperiodic spatiotemporal signals recorded during colonic manometry procedures. Spectral analysis is achieved by computing the continuous wavelet transform and cross-wavelet transform of these signals. Statistical inference is achieved by modeling the resulting time-averaged amplitudes in the frequency and frequency-phase domains as Gaussian processes over a regular grid, under the influence of categorical and numerical predictors specified by the experimental design as a functional mixed-effects model. Parameters of the model are inferred with Hamiltonian Monte Carlo. Using this method, we re-analyzed our previously published colonic manometry data, comparing healthy controls and patients with slow transit constipation. The output from our automated method, supports and adds to our previous manual analysis. To obtain these results took less than two days. In comparison the manual analysis took 5 weeks. The proposed mixed-effects model approach described here can also be used to gain an appreciation of cyclical activity in individual subjects during control periods and in response to any form of intervention.