Comparison of beta-binomial regression model approaches to analyze health related quality of life data

Health related quality of life (HRQoL) has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of HRQoL with patients' and disease's characteristics has become one of the primary aims of many HRQoL studies. HRQoL scores are usually assumed to be distributed as binomial random variables and often highly skewed. The use of the beta-binomial distribution in the regression context has been proposed to model such data, however, the beta-binomial regression has been performed by means of two di erent approaches in the literature: i) beta-binomial distribution with a logistic link; and ii) hierarchical generalized linear models (HGLMs). None of the existing literature in the analysis of HRQoL survey data has performed a comparison of both approaches in terms of adequacy and regression parameter interpretation context. This paper is motivated by the analysis of a real data application of HRQoL outcomes in patients with Chronic Obstructive Pulmonary Disease (COPD), where the use of both approaches yields to contradictory results in terms of covariate e ects signi cance and consequently the interpretation of the most relevant factors in HRQoL. We present an explanation of the results in both methodologies through a simulation study and address the need to apply the proper approach in the analysis of HRQoL survey data for practitioners, providing an R package.IT-620-13, MTM2013-40941-P, MTM2014-52184-P, MTM2016-74931-P, RD12/0001/0001 - REDISSE

Selecting the number of categories of the lymph node ratio in cancer research: A bootstrap-based hypothesis test

The high impact of the lymph node ratio as a prognostic factor is widely established in colorectal cancer, and is being used as a categorized predictor variable in several studies. However, the cut-off points as well as the number of categories considered differ considerably in the literature. Motivated by the need to obtain the best categorization of the lymph node ratio as a predictor of mortality in colorectal cancer patients, we propose a method to select the best number of categories for a continuous variable in a logistic regression framework. Thus, to this end, we propose a bootstrap-based hypothesis test, together with a new estimation algorithm for the optimal location of the cut-off points called BackAddFor, which is an updated version of the previously proposed AddFor algorithm. The performance of the hypothesis test was evaluated by means of a simulation study, under different scenarios, yielding type I errors close to the nominal errors and good power values whenever a meaningful difference in terms of prediction ability existed. Finally, the methodology proposed was applied to the CCR-CARESS study where the lymph node ratio was included as a predictor of five-year mortality, resulting in the selection of three categories.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Basque Government through the Consolidated Research Group MATHMODE (IT1294-19) from the Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco, the BERC 2018-2021 program and the SPRI Elkartek project 3KIA (KK-2020/00049); by the Spanish Government through the Ministerio de Ciencia, Innovación y Universidades: BCAM Severo Ochoa accreditation SEV-2017-0718 and by Ministerio de Economía y Competitividad and FEDER under research grants MTM2014-55966-P, MTM2016-74931-P and MTM2017-89422-P; and by Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019-2022) and the EU (ERDF), Ref. ED431G2019/06. Financial support for data collection was provided in part by grants from the Instituto de Salud Carlos III, (PS09/00314, PS09/00910, PS09/00746, PS09/00805, PI09/90460, PI09/90490, PI09/90453, PI09/90441, PI09/90397, and the thematic networks REDISSEC - Red de Investigación en Servicios de Salud en Enfermedades Crónicas), co-funded by European Regional Development Fund/European Social Fund (ERDF/ESF “Investing in your future”); and the Research Committee of the Hospital Galdakao

Estimation of cut-off points under complex-sampling design data

In the context of logistic regression models, a cut-off point is usually selected to dichotomize the estimated predicted probabilities based on the model. The techniques proposed to estimate optimal cut-off points in the literature, are commonly developed to be applied in simple random samples and their applicability to complex sampling designs could be limited. Therefore, in this work we propose a methodology to incorporate sampling weights in the estimation process of the optimal cut-off points, and we evaluate its performance using a real data-based simulation study. The results suggest the convenience of considering sampling weights for estimating optimal cut-off points.IT1294-19 BERC 2018-2021 KK-2020/00049 PIF18/21

La Formación de Utrillas en el borde sur de la cuenca Vasco-Cantábrica: aspectos estratigráficos, mineralógicos y genéticos

The Utrillas formation, located in the southem border of the Basque-Cantabrian basin, is mainly composed of sandy materials deposited in a fluvial environment. Two informal units have been distinguished due to field data: a lower coarse-grained unit, interpreted as braider river channel-fills, and a upper fine-grained unit which suggests a meandering river environment. Mineralogy consists of quartz and phyllosilicates, with minor amounts of feldspars. The analysis of tourmalines has pointed two possible sources for these sediments: granitoids and low grade-metasediments. The identified clay minerals are mica and kaolinite. Texturals observations have pointed out an inherited origin for mica, while kaolinite is partly inherited and partly authigenic. This authigenic origin seems to be associated with the alteration of potassic feldspars during the stage of late diagenesis (telodiagenesis).La Formación de Utrillas, aflorante en el borde sur de la cuenca Vasco-Cantábrica, está formada por materiales mayoritariamente areniscosos depositados en un ambiente fluvial. Los datos de campo han permitido distinguir de una manera informal dos unidades: una inferior de granulometría gruesa, representativa de un relleno de canal de tipo trenzado, y una superior más fina que sugiere un entorno de río meandriforme. La mineralogía está compuesta por cuarzo y filosilicatos, con cantidades menores de feldespatos. Como mineral accesorio aparece la turmalina, cuyo análisis ha permitido identificar dos posibles fuentes para los sedimentos: granitoides y metasedimentos de bajo grado. Los minerales de la arcilla presentes son exclusivamente la mica y la caolinita. A partir de criterios texturales, se ha constatado que la mica es de origen heredado, mientras que la caolinita es en parte heredada o bien autigénica, estando asociada a la alteración de feldespato potásico en una etapa de diagénesis tardía (telodiagénesis)

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks

Background:A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients’ morphology. Objective:To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology. Methods:We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists. We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x, y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models. Results:With annotators’ consensus as the ‘gold standard’, our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10-3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982]. In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10-3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans. The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view. Conclusions:We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.BERC.2022-2025 BCAM Severo Ochoa accreditation CEX2021-001142-S / MICIN / AEI / 10.13039/50110001103

Localización de usuarios con coordenadas polares.

Currently, the increase of location aware services and network management has driven the demand for user location estimation schemes, although it is not usually available to operators. Moreover, commercial networks have limited access to specific user related metrics. In general, solutions with Machine Learning (ML) have reached high precisions, but only in a trained scenario, and with difficulties in predicting unseen areas. The approach proposed here solves the above limitation by a reference coordinate conversion, to obtain relative polar positions which create scenario agnostic models, and whose performance is demonstrated using a dataset recollected from a commercial mobile network.Ministerio de Asuntos Económicos y Transformación Digital y la Unión Europea - NextGenerationEU, en el marco del Plan de Recuperación, Transformación y Resiliencia y el Mecanismo de Recuperación y Resiliencia bajo el proyecto MAORI. Además, también está parcialmente financiado por la Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech., a través de II Plan Propio de Investigación y Transferencia y por el proyecto “Desarrollo de casos de uso para el diseño, optimización y dimensionado de redes móviles – Líneas B1 y D1” (Ref. 8.06/5.59.5705-3 IDEA)

A Computer Application to Predict Adverse Events in the Short-Term Evolution of Patients With Exacerbation of Chronic Obstructive Pulmonary Disease

Background: Chronic obstructive pulmonary disease (COPD) is a common chronic disease. Exacerbations of COPD (eCOPD) contribute to the worsening of the disease and the patient’s evolution. There are some clinical prediction rules that may help to stratify patients with eCOPD by their risk of poor evolution or adverse events. The translation of these clinical prediction rules into computer applications would allow their implementation in clinical practice. Objective: The goal of this study was to create a computer application to predict various outcomes related to adverse events of short-term evolution in eCOPD patients attending an emergency department (ED) based on valid and reliable clinical prediction rules. Methods: A computer application, Prediction of Evolution of patients with eCOPD (PrEveCOPD), was created to predict 2 outcomes related to adverse events: (1) mortality during hospital admission or within a week after an ED visit and (2) admission to an intensive care unit (ICU) or an intermediate respiratory care unit (IRCU) during the eCOPD episode. The algorithms included in the computer tool were based on clinical prediction rules previously developed and validated within the Investigación en Resultados y Servicios de Salud COPD study. The app was developed for Windows and Android systems, using Visual Studio 2008 and Eclipse, respectively. Results: The PrEveCOPD computer application implements the prediction models previously developed and validated for 2 relevant adverse events in the short-term evolution of patients with eCOPD. The application runs under Windows and Android systems and it can be used locally or remotely as a Web application. Full description of the clinical prediction rules as well as the original references is included on the screen. Input of the predictive variables is controlled for out-of-range and missing values. Language can be switched between English and Spanish. The application is available for downloading and installing on a computer, as a mobile app, or to be used remotely via internet. Conclusions: The PrEveCOPD app shows how clinical prediction rules can be summarized into simple and easy to use tools, which allow for the estimation of the risk of short-term mortality and ICU or IRCU admission for patients with eCOPD. The app can be used on any computer device, including mobile phones or tablets, and it can guide the clinicians to a valid stratification of patients attending the ED with eCOPD.Fondo de Investigación Sanitaria (PI 06\1010, PI06\1017, PI06\714, PI06\0326, PI06\0664) Departamento de Salud del Gobierno Vasco (2012111008) Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco (IT620-13) Ministerio de Economía y Competitividad del Gobierno Español and FEDER (MTM2013-40941-P and MTM2016-74931-P) the Research Committee of the Hospital Galdakao the thematic networks -REDISSEC (Red de Investigación en Servicios de Salud en Enfermedades Crónicas) - of the Instituto de Salud Carlos III