Adaptive design of experiments to fit surrogate Gaussian process regression models allows fast sensitivity analysis of the input waveform for patient-specific 3D CFD models of liver radioembolization.

BACKGROUND AND OBJECTIVE: Patient-specific 3D computational fluid dynamics (CFD) models are increasingly being used to understand and predict transarterial radioembolization procedures used for hepatocellular carcinoma treatment. While sensitivity analyses of these CFD models can help to determine the most impactful input parameters, such analyses are computationally costly. Therefore, we aim to use surrogate modelling to allow relatively cheap sensitivity analysis. As an example, we compute Sobol's sensitivity indices for three input waveform shape parameters. METHODS: We extracted three characteristic shape parameters from our input mass flow rate waveform (peak systolic mass flow rate, heart rate, systolic duration) and defined our 3D input parameter space by varying these parameters within 75 %-125 % of their nominal values. To fit our surrogate model with a minimal number of costly CFD simulations, we developed an adaptive design of experiments (ADOE) algorithm. The ADOE uses 100 Latin hypercube sampled points in 3D input space to define the initial design of experiments (DOE). Subsequently, we re-sample input space with 10,000 Latin Hypercube sampled points and cheaply estimate the outputs using the surrogate model. In each of 27 equivolume bins which divide our input space, we determine the most uncertain prediction of the 10,000 points, compute the true outputs using CFD, and add these points to the DOE. For each ADOE iteration, we calculate Sobol's sensitivity indices, and we continue to add batches of 27 samples to the DOE until the Sobol indices have stabilized. RESULTS: We tested our ADOE algorithm on the Ishigami function and showed that we can reliably obtain Sobol's indices with an absolute error <0.1. Applying ADOE to our waveform sensitivity problem, we found that the first-order sensitivity indices were 0.0550, 0.0191 and 0.407 for the peak systolic mass flow rate, heart rate, and the systolic duration, respectively. CONCLUSIONS: Although the current study was an illustrative case, the ADOE allows reliable sensitivity analysis with a limited number of complex model evaluations, and performs well even when the optimal DOE size is a priori unknown. This enables us to identify the highest-impact input parameters of our model, and other novel, costly models in the future.

Performance evaluation of different regression models: application in a breast cancer patient data.

This paper provides a comprehensive analysis of linear regression models, focusing on addressing multicollinearity challenges in breast cancer patient data. Linear regression methodologies, including GAM, Beta, GAM Beta, Ridge, and Beta Ridge, are compared using two statistical criteria. The study, conducted with R software, showcases the Beta regression model's exceptional performance, achieving a BIC of - 5520.416. Furthermore, the Ridge regression model demonstrates remarkable results with the best AIC at - 8002.647. The findings underscore the practical application of these models in real-world scenarios and emphasize the Beta regression model's superior ability to handle multicollinearity challenges. The preference for AIC over BIC in Generalized Additive Models (GAMs) is rooted in the AIC's calculation framework, highlighting its effectiveness in capturing the complexity and flexibility inherent in GAMs.

An empirical estimation of aggregate import demand under foreign exchange constraints: Evidence from Ethiopia.

This study employs the estimation of aggregate import demand under foreign exchange constraints in Ethiopia, utilizing annual time series data from 1985 to 2021. The regression analysis is conducted using the nonlinear autoregressive distributed lag (NARDL) approach to investigate the impact of the accumulation of foreign exchange reserves on aggregate import demand in Ethiopia. The estimation results indicate that, in the long run, all the variables, i.e., foreign exchange reserve, the relative price of imports, real income, volatility of the exchange rate, money supply, and policy dummy, significantly determine the behavior of aggregate imports over the reference period. The findings also show that, in the long run, foreign exchange reserve, real income, and the exchange rate positively affect the demand for imports in Ethiopia. Meanwhile, a positive shock in relative import price and money supply negatively affects import demand in Ethiopia. Thus, the price and income elasticity estimates have correct signs and are statistically significant. The variables included in the model strongly affect import demand in both the short and long run. Finally, policymakers aiming to significantly influence import demand should focus on effective management of these variables, as they strongly affect import volume.

Heterogeneous peer effects of college roommates on academic performance.

Understanding how student peers influence learning outcomes is crucial for effective education management in complex social systems. The complexities of peer selection and evolving peer relationships, however, pose challenges for identifying peer effects using static observational data. Here we use both null-model and regression approaches to examine peer effects using longitudinal data from 5,272 undergraduates, where roommate assignments are plausibly random upon enrollment and roommate relationships persist until graduation. Specifically, we construct a roommate null model by randomly shuffling students among dorm rooms and introduce an assimilation metric to quantify similarities in roommate academic performance. We find significantly larger assimilation in actual data than in the roommate null model, suggesting roommate peer effects, whereby roommates have more similar performance than expected by chance alone. Moreover, assimilation exhibits an overall increasing trend over time, suggesting that peer effects become stronger the longer roommates live together. Our regression analysis further reveals the moderating role of peer heterogeneity. In particular, when roommates perform similarly, the positive relationship between a student's future performance and their roommates' average prior performance is more pronounced, and their ordinal rank in the dorm room has an independent effect. Our findings contribute to understanding the role of college roommates in influencing student academic performance.

Health-related quality of life and its predictors among hypertensive patients on follow up at public hospitals in Addis Ababa, Ethiopia: application of Tobit regression model.

BACKGROUND: Health-related quality of life and its associated factors among hypertensive patients living in Ethiopia are not well studied. Therefore, this study aims to assess the level of health-related quality of life and its associated factors in hypertensive patients on follow-up in Public Hospitals in Addis Ababa, Ethiopia. METHODS: A facility-based cross-sectional study was conducted among 339 hypertensive patients on follow-up at Yekatit 12 &Zewditu Hospitals. Data were collected through face-to-face interviews using Euro Quality of Life Groups 5 Dimensions 5 Levels (EQ-5D-5L) in combination with Euro Quality of Life Groups Visual Analog Scale (EQ-VAS). A multivariable Tobit regression model was employed to assess the association between EQ-5D-5L index, EQ-VAS, and potential predicting factors. RESULTS: The median index value and EQ-VAS Scales score was 0.86 (IQR = 0.74, 0.94) and 69 (IQR = 55, 80) respectively. The proportion of participants reporting anxiety/depression and pain/discomfort problems was highest, while the fewest patients reported problems in the self-care dimension. Older, rural residents, low income, higher stages of hypertension, increased use of antihypertensive medications, and patients with an increased hospitalization rate scored lower on health-related quality of life than others. CONCLUSION: Health-related quality of life among hypertensive patients attending public health hospitals in Addis Ababa is unacceptably poor. Emphasis should be given to patients with higher stages of hypertension, increased use of antihypertensive medications, and an increased hospitalization rate giving due focus to older, rural residents, and low-income patients to promote their health-related quality of life.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.

A new and unified method for regression analysis of interval-censored failure time data under semiparametric transformation models with missing covariates.

This paper discusses regression analysis of interval-censored failure time data arising from semiparametric transformation models in the presence of missing covariates. Although some methods have been developed for the problem, they either apply only to limited situations or may have some computational issues. Corresponding to these, we propose a new and unified two-step inference procedure that can be easily implemented using the existing or standard software. The proposed method makes use of a set of working models to extract partial information from incomplete observations and yields a consistent estimator of regression parameters assuming missing at random. An extensive simulation study is conducted and indicates that it performs well in practical situations. Finally, we apply the proposed approach to an Alzheimer's Disease study that motivated this study.

Factors associated with health-related quality of life among people living with HIV in South Korea: Tobit regression analysis.

This study investigated health-related quality of life and identified factors affecting it among people with the HIV in South Korea. A total of 243 people living with HIV participated in this cross-sectional survey. Data were collected from five hospitals between November 2021 and August 2022 using structured online questionnaires. Data were analyzed using descriptive statistics, Mann-Whitney U test, Kruskal-Wallis test, Spearman's rho analysis, and Tobit regression analysis because a significant ceiling effect was observed for the dependent variable. The mean score for the health-related quality of life was 75.74 ± 16.48. The significant factors that positively influence the health-related quality of life were "employment" (B = 4.57, p = .035), "not participating in the self-help group" (B = 6.10, p = .004), "higher self-efficacy for managing symptoms" (B = 1.32, p = .036), "higher self-efficacy for getting support/help" (B = 0.95, p = .035), and "higher self-efficacy for managing fatigue" (B = 2.80, p < .001) in the Tobit regression analysis. The results suggest that interventions to increase self-efficacy should involve developing programs and policies for people living with HIV. There is a need for efforts to provide healthcare services linked to employment support, as well as to establish a social environment in which they can work without stigma. Further, self-help groups could be utilized as intervention channels.

Factors associated with shame and stigma among head and neck cancer patients: a cross-sectional study.

PURPOSE: Head and neck cancer (HNC) patients often suffer from shame and stigma due to treatment limitations or due to societal factors. The purpose of this study was to assess perceived body image, depression, physical and psychosocial function, and self-stigma, as well as to identify factors that predicted shame and stigma in patients with HNC. METHODS: This cross-sectional study recruited 178 HNC patients from the outpatient radiation department of a medical center in Northern Taiwan. Patients were assessed for patient reported outcomes using the Body Image Scale (BIS), the Hospital Anxiety and Depression Scale-Depression Subscale (HADS-Depression Subscale), the University of Washington Quality of Life Scale (UW-QOL) version 4.0, and the Shame and Stigma Scale (SSS). Data were analyzed by descriptive analysis, Pearson's product-moment correlation, and multiple regression. RESULTS: The two top-ranked subscales of shame and stigma were: "speech and social concerns" and "regret". Shame and stigma were positively correlated with a longer time since completion of treatment, more body image concerns, and higher levels of depression. They were negatively correlated with being male and having lower physical function. Multiple regression analysis showed that female gender, a longer time since completing treatment, higher levels of body image concern, greater depression, and less physical function predicted greater shame and stigma. These factors explained 74.7% of the variance in shame and stigma. CONCLUSION: Patients' body image concerns, depression, time since completing treatment, and physical function are associated with shame and stigma. Oncology nurses should assess and record psychological status, provide available resources, and refer appropriate HNC patients to counselling.

ERegPose: An explicit regression based 6D pose estimation for snake-like wrist-type surgical instruments.

BACKGROUND: Accurately estimating the 6D pose of snake-like wrist-type surgical instruments is challenging due to their complex kinematics and flexible design. METHODS: We propose ERegPose, a comprehensive strategy for precise 6D pose estimation. The strategy consists of two components: ERegPoseNet, an original deep neural network model designed for explicit regression of the instrument's 6D pose, and an annotated in-house dataset of simulated surgical operations. To capture rotational features, we employ an Single Shot multibox Detector (SSD)-like detector to generate bounding boxes of the instrument tip. RESULTS: ERegPoseNet achieves an error of 1.056 mm in 3D translation, 0.073 rad in 3D rotation, and an average distance (ADD) metric of 3.974 mm, indicating an overall spatial transformation error. The necessity of the SSD-like detector and L1 loss is validated through experiments. CONCLUSIONS: ERegPose outperforms existing approaches, providing accurate 6D pose estimation for snake-like wrist-type surgical instruments. Its practical applications in various surgical tasks hold great promise.

A regressive analysis of the main environmental risk factors of human echinococcosis in 370 counties in China.

BACKGROUND: Echinococcosis is a natural focal, highly prevalent disease in China. Factors influencing the spread of echinococcosis are not only related to personal exposure but also closely related to the environment itself. The purpose of this study was to explore the influence of environmental factors on the prevalence of human echinococcosis and to provide a reference for prevention and control of echinococcosis in the future. METHODS: Data were collected from 370 endemic counties in China in 2018. By downloading Modis, DEM and other remote-sensing images in 2018. Data on environmental factors, i.e., elevation, land surface temperature (LST) and normalized difference vegetation index (NDVI) were collected. Rank correlation analysis was conducted between each environmental factor and the prevalence of echinococcosis at the county level. Negative binomial regression was used to analyze the impact of environmental factors on the prevalence of human echinococcosis at the county level. RESULTS: According to rank correlation analysis, the prevalence of human echinococcosis in each county was positively correlated with elevation, negatively correlated with LST, and negatively correlated with NDVI in May, June and July. Negative binomial regression showed that the prevalence of human echinococcosis was negatively correlated with annual LST and summer NDVI, and positively correlated with average elevation and dog infection rate. The prevalence of human cystic echinococcosis was inversely correlated with the annual average LST, and positively correlated with both the average elevation and the prevalence rate of domestic animals. The prevalence of human alveolar echinococcosis was positively correlated with both NDVI in autumn and average elevation, and negatively correlated with NDVI in winter. CONCLUSION: The prevalence of echinococcosis in the population is affected by environmental factors. Environmental risk assessment and prediction can be conducted in order to rationally allocate health resources and improve both prevention and control efficiency of echinococcosis.

Genito-urinary infectious adverse events related to sodium glucose cotransporter-2 inhibitors: a network meta-analysis and meta-regression.

INTRODUCTION: Sodium glucose cotransporter-2 inhibitors (SGLT2is) are an emerging class of drugs with wide indications. Controversial evidence exists regarding the risk of urinary tract infection (UTI) and genital infections (GI) with SGLT2is paving way for undertaking this network meta-analysis and meta-regression study. METHODS: Data from randomized trials evaluating SGLT2is reporting the number of patients with UTI and GI were included. Odds ratios (OR) with 95% confidence intervals (95% CI) were the effect estimates. Meta-regression analysis identified risk factors. Number needed to harm (NNH) was estimated. RESULTS: Two hundred and sixty-four articles were included [UTI (213 studies; 150,140 participants) and GI (188 studies; 121,275 participants)]. An increased risk of UTI (OR: 1.11; 95% CI: 1.06, 1.16) and GI (OR: 3.5, 95% CI: 3.1, 3.9) was observed. Men showed a lower risk of UTI (OR: 0.2; 95% CI: 0.2, 0.3) and GI (OR: 0.4; 95% CI: 0.4, 0.5). Meta-regression analyses revealed BMI ≥ 30 kg/m2 and duration of SGLT2i treatment for ≥6 months as risk factors. NNH was 16 for UTI and 25 for GI. CONCLUSION: SGLT2is increase the risk of UTI and GI that needs to be incorporated in the treatment guidelines with precautions in high-risk patients. PROSPECTIVE PROTOCOL REGISTRATION: https://osf.io/5fwyk.

Analysis of multi-level barriers to physical activity among nursing students using regularized regression.

Physical inactivity is a growing societal concern with significant impact on public health. Identifying barriers to engaging in physical activity (PA) is a critical step to recognize populations who disproportionately experience these barriers. Understanding barriers to PA holds significant importance within patient-facing healthcare professions like nursing. While determinants of PA have been widely studied, connecting individual and social factors to barriers to PA remains an understudied area among nurses. The objectives of this study are to categorize and model factors related to barriers to PA using the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework. The study population includes nursing students at the study institution (N = 163). Methods include a scoring system to quantify the barriers to PA, and regularized regression models that predict this score. Key findings identify intrinsic motivation, social and emotional support, education, and the use of health technologies for tracking and decision-making purposes as significant predictors. Results can help identify future nursing workforce populations at risk of experiencing barriers to PA. Encouraging the development and employment of health-informatics solutions for monitoring, data sharing, and communication is critical to prevent barriers to PA before they become a powerful hindrance to engaging in PA.

Study on the influence of rock bridge angle on the size effect of rock uniaxial compression.

As a basic parameter of rock, the rock bridge angle plays an important role in maintaining the stability of rock masses. To study the size effect of rock bridge angle on the uniaxial compressive strength of rocks, this paper adopts the principle of regression analysis and combines numerical simulation to carry out relevant research. The research results indicate that: (1) the uniaxial compressive strength decreases with the increase of the rock bridge angle, showing a power function relationship; (2) The uniaxial compressive strength decreases with the increase of rock size and tends to stabilize when the rock size is greater than 350 mm, showing a significant size effect. (3) The fluctuation coefficient of compressive strength increases with the increase of rock bridge angle and decreases with the increase of rock size; When the rock size is 350 mm, the fluctuation coefficient is less than 5%; (4) The characteristic compressive strength and characteristic size both increase with the increase of the rock bridge angle.

Integrating classification and regression learners with bioimpedance methods for estimating weight status in infants and juveniles from the southern Cuba region.

OBJECTIVE: The search for other indicators to assess the weight and nutritional status of individuals is important as it may provide more accurate information and assist in personalized medicine. This work is aimed to develop a machine learning predictions of weigh status derived from bioimpedance measurements and other physical parameters of healthy younger volunteers from Southern Cuba Region. METHODS: A pilot random study at the Pediatrics Hospital was conducted. The volunteers were selected between 2002 and 2008, ranging in age between 2 and 18 years old. In total, 776 female and male volunteers are studied. Along the age and sex in the cohort, volunteers with class I obesity, overweight, underweight and with normal weight are considered. The bioimpedance parameters are obtained by measuring standard tetrapolar whole-body configuration. The bioimpedance analyser is used, collecting fundamental bioelectrical and other parameters of interest. A classification model are performed, followed by a prediction of the body mass index. RESULTS: The results derived from the classification leaner reveal that the size, body density, phase angle, body mass index, fat-free mass, total body water volume according to Kotler, body surface area, extracellular water according to Kotler and sex largely govern the weight status of this population. In particular, the regression model shows that other bioparameters derived from impedance measurements can be associated with weight status estimation with high accuracy. CONCLUSION: The classification and regression predictive models developed in this work are of the great importance to assist the diagnosis of weigh status with high accuracy. These models can be used for prompt weight status evaluation of younger individuals at the Pediatrics Hospital in Santiago de Cuba, Cuba.

The outcomes of mental health care for depression over time: A meta-regression analysis of response rates in usual care.

BACKGROUND: Over the past decades dozens of randomized trials have shown that psychological treatments are more effective than care-as-usual (CAU). It could be expected that these treatments are implemented in routine care and that the response rates in usual care improve over time. The aim of the current meta-analysis is to examine if response and remission rates in usual care have improved over time. METHODS: We used an existing meta-analytic database of randomized controlled trials examining the effects of psychological treatments of depression and selected CAU control groups from these trials. We only included CAU conditions in primary care, specialized mental health care, perinatal care and general medical care. The response rate (50 % symptom reduction) was the primary outcome. RESULTS: We included 125 CAU control groups (8542 participants). The response rate for all CAU control groups was 0.22 (95 % CI: 0.19; 0.24) with high heterogeneity (I2 = 83; 95 % CI: 80; 85), with somewhat higher rates in primary care (0.27; 95 % CI: 0.23; 0.31). We found hardly any indications that the outcomes have improved over the years. The meta-regression analysis with publication year as predictor in the full dataset resulted in a coefficient of 0.1 (SE = 0.01; p = 0.0.35). A series of sensitivity analyses supported the main findings. Remission rates and pre-post effect sizes also did not significantly improve over time. CONCLUSIONS: Response and remission rates in usual care are low, with the large majority of patients not responding or remitting, and the outcomes have probably not improved over time.

Regression discontinuity design for the study of health effects of exposures acting early in life.

Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effect of an exposure on later outcomes by exploiting the discontinuity in the exposure probability at an assignment variable cut-off. With the intent of facilitating the use of RDD in the Developmental Origins of Health and Disease (DOHaD) research, we describe the main aspects of the study design and review the studies, assignment variables and exposures that have been investigated to identify short- and long-term health effects of early life exposures. We also provide a brief overview of some of the methodological considerations for the RDD identification using an example of a DOHaD study. An increasing number of studies investigating the effects of early life environmental stressors on health outcomes use RDD, mostly in the context of education, social and welfare policies, healthcare organization and insurance, and clinical management. Age and calendar time are the mostly used assignment variables to study the effects of various early life policies and programs, shock events and guidelines. Maternal and newborn characteristics, such as age, birth weight and gestational age are frequently used assignment variables to study the effects of the type of neonatal care, health insurance, and newborn benefits, while socioeconomic measures have been used to study the effects of social and welfare programs. RDD has advantages, including intuitive interpretation, and transparent and simple graphical representation. It provides valid causal estimates if the assumptions, relatively weak compared to other non-experimental study designs, are met. Its use to study health effects of exposures acting early in life has been limited to studies based on registries and administrative databases, while birth cohort data has not been exploited so far using this design. Local causal effect around the cut-off, difficulty in reaching high statistical power compared to other study designs, and the rarity of settings outside of policy and program evaluations hamper the widespread use of RDD in the DOHaD research. Still, the assignment variables' cut-offs for exposures applied in previous studies can be used, if appropriate, in other settings and with additional outcomes to address different research questions.

Regression models for average hazard.

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.

No Change in Complications Following Thyroidectomy Despite Increase in Thyroid Cancer Surgeries: A Meta-Regression Analysis.

PURPOSE: The increase in thyroid cancer incidence has inevitably led to an increase in thyroid cancer surgeries. This meta-regression analysis aimed to determine if the rate of post-thyroidectomy complications changes by year. MATERIALS AND METHODS: PubMed and Embase databases were used to perform a systematic literature search of studies published from January 1, 2005, using the keywords "thyroidectomy" and "complication." A meta-regression was performed for post-thyroidectomy hypocalcemia and bleeding. RESULTS: This meta-analysis included 25 studies involving 927751 individuals. Through the years of publications in this study, there was no significant difference in the proportion of post-thyroidectomy hypocalcemia and bleeding (p=0.9978, 0.6393). CONCLUSION: Although the number of thyroid surgeries has recently increased, the incidence of post-thyroidectomy hypocalcemia and bleeding did not significantly increase.