A generalization of Passing-Bablok regression is proposed for comparing multiple measurement methods simultaneously. Possible applications include assay migration studies or interlaboratory trials. When comparing only two methods, the method boils down to the usual Passing-Bablok estimator. It is close in spirit to reduced major axis regression, which is, however, not robust. To obtain a robust estimator, the major axis is replaced by the (hyper-)spherical median axis. This technique has been applied to compare SARS-CoV-2 serological tests, bilirubin in neonates, and an in vitro diagnostic test using different instruments, sample preparations, and reagent lots. In addition, plots similar to the well-known Bland-Altman plots have been developed to represent the variance structure.

The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA\'s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson\'s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions.

OBJECTIVE: The objective of our study is to investigate the impacts of telemedicine technology and its specific tools on physicians\' overall satisfaction, quality of care, and percentage of patient visits in ambulatory care settings after the COVID-19 lockdowns.
METHODS: Data for our analysis was sourced from the 2021 annual National Electronic Health Records Survey (NEHRS), which included 1,875 complete questionnaire responses from physicians in the 2021 NEHRS. We used regression models to test the effects of telemedicine on physicians\' overall satisfaction, quality of care, and percentage of patients\' visits.
RESULTS: We report that telemedicine technology has significant positive effects on physicians\' satisfaction with telemedicine and quality of care evaluation, both at an aggregate level and at the disaggregate levels of individual telemedicine features, and partially significant effects on patients\' telemedicine visits.
CONCLUSIONS: Telemedicine features that contributed significantly to physician satisfaction and quality of care evaluation were telephone, videoconferencing, standalone telemedicine platform, and telemedicine platform integrated with EHR, while only telephone and integrated telemedicine platform contributed significantly to patients\' telemedicine visits.
CONCLUSIONS: For telemedicine research and practice, this study confirms that telemedicine improves physician satisfaction and quality of care perceptions and will therefore be preferred by physicians. However, telemedicine has a mixed impact on percentage of patient visits, which suggests that providers may have to work harder to regularize telemedicine acceptance among patients in the post-COVID era.

Generalized linear models (GLMs) are an integral tool in ecology. Like general linear models, GLMs assume linearity, which entails a linear relationship between independent and dependent variables. However, because this assumption acts on the link rather than the natural scale in GLMs, it is more easily overlooked. We reviewed recent ecological literature to quantify the use of linearity. We then used two case studies to confront the linearity assumption via two GLMs fit to empirical data. In the first case study we compared GLMs to generalized additive models (GAMs) fit to mammal relative abundance data. In the second case study we tested for linearity in occupancy models using passerine point-count data. We reviewed 162 studies published in the last 5 years in five leading ecology journals and found less than 15% reported testing for linearity. These studies used transformations and GAMs more often than they reported a linearity test. In the first case study, GAMs strongly out-performed GLMs as measured by AIC in modeling relative abundance, and GAMs helped uncover nonlinear responses of carnivore species to landscape development. In the second case study, 14% of species-specific models failed a formal statistical test for linearity. We also found that differences between linear and nonlinear (i.e., those with a transformed independent variable) model predictions were similar for some species but not for others, with implications for inference and conservation decision-making. Our review suggests that reporting tests for linearity are rare in recent studies employing GLMs. Our case studies show how formally comparing models that allow for nonlinear relationships between the dependent and independent variables has the potential to impact inference, generate new hypotheses, and alter conservation implications. We conclude by suggesting that ecological studies report tests for linearity and use formal methods to address linearity assumption violations in GLMs.

In clinical practice and biomedical research, measurements are often collected sparsely and irregularly in time, while the data acquisition is expensive and inconvenient. Examples include measurements of spine bone mineral density, cancer growth through mammography or biopsy, a progression of defective vision, or assessment of gait in patients with neurological disorders. Practitioners often need to infer the progression of diseases from such sparse observations. A classical tool for analyzing such data is a mixed-effect model where time is treated as both a fixed effect (population progression curve) and a random effect (individual variability). Alternatively, researchers use Gaussian processes or functional data analysis, assuming that observations are drawn from a certain distribution of processes. While these models are flexible, they rely on probabilistic assumptions, require very careful implementation, and tend to be slow in practice. In this study, we propose an alternative elementary framework for analyzing longitudinal data motivated by matrix completion. Our method yields estimates of progression curves by iterative application of the Singular Value Decomposition. Our framework covers multivariate longitudinal data, and regression and can be easily extended to other settings. As it relies on existing tools for matrix algebra, it is efficient and easy to implement. We apply our methods to understand trends of progression of motor impairment in children with Cerebral Palsy. Our model approximates individual progression curves and explains 30% of the variability. Low-rank representation of progression trends enables identification of different progression trends in subtypes of Cerebral Palsy.

UNASSIGNED: The addition of two-way interactions is a classic problem in statistics, and comes with the challenge of quadratically increasing dimension. We aim to a) devise an estimation method that can handle this challenge and b) to aid interpretation of the resulting model by developing computational tools for quantifying variable importance.
UNASSIGNED: Existing strategies typically overcome the dimensionality problem by only allowing interactions between relevant main effects. Building on this philosophy, and aiming for settings with moderate n to p ratio, we develop a local shrinkage model that links the shrinkage of interaction effects to the shrinkage of their corresponding main effects. In addition, we derive a new analytical formula for the Shapley value, which allows rapid assessment of individual-specific variable importance scores and their uncertainties.
UNASSIGNED: We empirically demonstrate that our approach provides accurate estimates of the model parameters and very competitive predictive accuracy. In our Bayesian framework, estimation inherently comes with inference, which facilitates variable selection. Comparisons with key competitors are provided. Large-scale cohort data are used to provide realistic illustrations and evaluations. The implementation of our method in RStan is relatively straightforward and flexible, allowing for adaptation to specific needs.
UNASSIGNED: Our method is an attractive alternative for existing strategies to handle interactions in epidemiological and/or clinical studies, as its linked local shrinkage can improve parameter accuracy, prediction and variable selection. Moreover, it provides appropriate inference and interpretation, and may compete well with less interpretable machine learners in terms of prediction.

There is a plethora of research attempting to contrast high- and low-velocity pitchers to identify traits to target for increasing velocity. However, pitch velocity exists on a continuum. Therefore, our purpose is to display the analytical discrepancies between creating velocity subgroups and leaving velocity as a continuous variable by examining the influence of ball velocity on elbow valgus torque. Motion capture data for 1315 actively competing pitchers were retrospectively extracted from a private database. We compared three analytic methods: (1) linear regression of valgus torque on ball velocity, (2) t-test between low- and high-velocity groups formed by a median split, and (3) t-test between very low- and very high-velocity groups formed by upper and lower velocity quartiles. Linear regression indicates ball velocity influenced valgus torque (p < 0.001, R2 = 0.280). Median splitting reduced the predictability of ball velocity on valgus torque (p < 0.001, R2 = 0.180). Conversely, extreme group splitting artificially inflated the effect size (p < 0.001, R2 = 0.347). We recommend sports biomechanics researchers not discretise a continuous variable to form subgroups for analysis because (1) it distorts the relationship between the variables of interest and (2) a regression equation can be used to estimate the dependent variable at any value of the independent variable, not just the group means.

The increasing prevalence of Diabetes Mellitus (DM) as a global health concern highlights the paramount importance of accurately predicting its progression. This necessity has propelled the use of deep learning\'s advanced analytical and predictive capabilities to the forefront of current research. However, this approach is confronted with significant challenges, notably the prevalence of incomplete data and the need for more robust predictive models. Our research aims to address these critical issues, leveraging deep learning to enhance the precision and reliability of diabetes progression predictions. We address the issue of missing data by first locating individuals with data gaps within specific patient clusters, and then applying targeted imputation strategies for effective data imputation. To enhance the robustness of our model, we implement strategies such as data augmentation and the development of advanced group-level feature analysis. A cornerstone of our approach is the implementation of a deep attentive transformer that is sensitive to group characteristics. This framework excels in processing a wide array of data, including clinical and physical examination information, to accurately predict the progression of DM. Beyond its predictive capabilities, our model is engineered to perform advanced feature selection and reasoning. This is crucial for understanding the impact of both individual and group-level factors on deep models\' predictions, providing invaluable insights into the dynamics of DM progression. Our approach not only marks a significant advancement in the prediction of diabetes progression but also contributes to a deeper understanding of the multifaceted factors influencing this chronic disease, thereby aiding in more effective diabetes management and research.

Analyzing crash data is a complex and labor-intensive process that requires careful consideration of multiple interdependent modeling aspects, such as functional forms, transformations, likely contributing factors, correlations, and unobserved heterogeneity. Limited time, knowledge, and experience may lead to over-simplified, over-fitted, or misspecified models overlooking important insights. This paper proposes an extensive hypothesis testing framework including a multi-objective mathematical programming formulation and solution algorithms to estimate crash frequency models considering simultaneously likely contributing factors, transformations, non-linearities, and correlated random parameters. The mathematical programming formulation minimizes both in-sample fit and out-of-sample prediction. To address the complexity and non-convexity of the mathematical program, the proposed solution framework utilizes a variety of metaheuristic solution algorithms. Specifically, Harmony Search demonstrated minimal sensitivity to hyperparameters, enabling an efficient search for solutions without being influenced by the choice of hyperparameters. The effectiveness of the framework was evaluated using two real-world datasets and one synthetic dataset. Comparative analyses were performed using the two real-world datasets and the corresponding models published in literature by independent teams. The proposed framework showed its capability to pinpoint efficient model specifications, produce accurate estimates, and provide valuable insights for both researchers and practitioners. The proposed approach allows for the discovery of numerous insights while minimizing the time spent on model development. By considering a broader set of contributing factors, models with varied qualities can be generated. For instance, when applied to crash data from Queensland, the proposed approach revealed that the inclusion of medians on sharp curved roads can effectively reduce the occurrence of crashes, when applied to crash data from Washington, the simultaneous consideration of traffic volume and road curvature resulted in a notable reduction in crash variances but an increase in crash means.

UNASSIGNED: The current study aims to develop an algorithm for mapping the WHODAS 2.0 to the EQ-5D-5 L for patients with mental disorders.
UNASSIGNED: This cross-sectional study was conducted at the Institute of Mental Health and Community Wellness Clinics in Singapore between June 2019 and November 2022. We included four regression methods including the Ordinary Least Square (OLS) regression, the Tobit regression model (Tobit), the robust regression with MM estimator (MM), and the adjusted limited dependent variable mixture model (ALDVMM) to map EQ-5D-5 L utility scores from the WHODAS 2.0.
UNASSIGNED: A total of 797 participants were included. The mean EQ-5D-5 L utility and WHODAS 2.0 total scores were 0.615 (SD = 0.342) and 11.957 (SD = 8.969), respectively. We found that the EQ-5D-5 L utility score was best predicted by the robust regression model with the MM estimator. Our findings suggest that the WHODAS 2.0 total scores were significantly and inversely associated with the EQ-5D-5 L utility scores.
UNASSIGNED: This study provides a mapping algorithm for converting the WHODAS 2.0 scores into EQ-5D-5 L utility scores which can be implemented using a simple online calculator in the following web application: https://eastats.shinyapps.io/whodas_eq5d/.