Log-likelihood Values Research Articles

Prognostic value of left atrial strain for outcome of catheter ablation in patients with atrial fibrillation and moderately enlarged left atrium

Abstract Background Atrial fibrillation catheter ablation (AFCA) is an effective rhythm control method for patients with AF, but AF recurrence is higher in patients with a large left atrium (LA). We explored whether pre-procedural LA strain has incremental prognostic value for the long-term rhythm outcomes of AFCA in patients with AF and moderately enlarged LA size (45≤LA diameter<50mm). Methods We included 2,269 patients who underwent de novo AF catheter ablation (men 72.2%, 59.1[±10.7] years, paroxysmal AF 64.0%). We divided grouped into 5 mm increments and determined the appropriate cut-off of LA diameter (45mm) predicting the difference in long-term rhythm outcome by the log-likelihood values of multivariate Cox proportional hazard models. Patients with moderately enlarged LA size (45≤LA diameter<50mm, n=413) with borderline rhythm outcomes were used for analysis. Results In the cohort of 413 patients who underwent AFCA with moderately enlarged LA size, AF was recurred in 208 patients (43.7%) after AFCA. We determined the appropriate cut-off of LA strain (12.5) predicting the difference in long-term rhythm outcome by the log-likelihood values of multivariate Cox proportional hazard models. During 24 months [10-50] follow-up, patients with low LA strain (<12.5) were showed worsen rhythm outcome than high LA strain (Log-rank p<0.001). Patients with AF recurrence after AFCA were independently associated with low LA strain (HR 1.539 [1.130-2.094], p=0.006), and paroxysmal AF (OR 0.651 [0.469-0.905], p=0.011). Furthermore, the likelihood ratio test demonstrated a significant influence of adding LA strain by TTE (χ2 = 13.90 [P < 0.001]). Conclusions LA strain using baseline echocardiography has a predictive power for AF recurrence after AFCA in patients with moderately enlarged LA size (45≤LA diameter<50mm).

Compliance with the 2016 WHO’s antenatal care recommendation and its determinants among women in Sub-Saharan Africa: a multilevel-analysis of population survey data

BackgroundDespite the positive impact of adhering to the new antenatal care model on pregnancy outcomes and maternal health service uptake, women in resource-limited settings exhibit low levels of compliance with this recommendation. Previous studies on women’s adherence to the new antenatal care recommendation have been limited to individual countries, with no evidence available at Sub-Saharan Africa (SSA) level. Therefore, this study sought to investigate compliance with the 2016 WHO’s recommendation of at least eight antenatal care contacts among women in SSA countries and identify its determinants.MethodsThe study utilized a weighted sample of 101,983 women who had received antenatal care during their index pregnancy, drawn from recent DHS data of sixteen SSA countries. A multilevel mixed-effect analysis was conducted to identify factors that influence compliance with new antenatal care recommendations. Model comparison was performed using deviance and log-likelihood values, and statistical significance was determined at a P-value of less than 0.05.ResultsThe level of compliance with the recommended antenatal care contacts among women in SSA was 9.9% (95% CI: 9.7-10.1%), with the highest rate in Sierra Leone (26.1%) and lowest in Rwanda (< 1%). A multivariable logistic regression analysis showed that age, education, employment status, household wealth, healthcare decisions, the timing of antenatal contacts, consumption of nutritional supplements, residence, community-level women illiteracy, and media exposure were the significant determinants of compliance.ConclusionOnly one in ten pregnant women in SSA countries had attended the recommended number of antenatal contacts, with Sierra Leone having the highest compliance rate and Rwanda and Senegal having the lowest. Therefore, policymakers should focus on improving access to education, especially for women and their partners, and providing exempted services for pregnant women from low-income households. Interventions that target communities with low levels of literacy and media exposure could also be effective in improving the uptake of the services.

Advancing oncological practice with innovative cancer data analytics: A new exponential-generated class of distributions

Traditional predictive modeling techniques have significantly influenced the analysis of survival times and predictive markers in oncology. However, these models often do not fully address the complexities of cancer data, leading to a noticeable gap in accurately modeling both cancer survival and mortality. The aim of this study is to show a model that offers the best fit for cancer mortality and survival data, thereby improving treatment strategies and patient prognoses. I propose the a New Exponential-Weibull (NEWE) distribution within the New Exponential-Generated (NE-G) class of distributions to enhance oncological analytics with greater accuracy and comprehensiveness. An empirical analysis of Brazilian cancer mortality data is conducted., This data set covers children, head and neck, and cervical cancer. Concurrently, survival time-to-event data for bladder and advanced lung cancers patients are assessed to evaluate the model’s effectiveness. Based on standard metrics, extensive simulation experiments show the Maximum Product of Spacing Estimator as the best of seven-point estimation techniques. The NEWE distribution shows superior modeling capabilities, surpassing traditional models with lower values of Log-likelihood, Cramer-von Mises, and Anderson-Darling and higher Kolmogorov-Smirnov (KS) p-values. The study also discerns the most fitting estimators for distinct types of cancer mortality and survival data. This includes the Right Tail Anderson-Darling for child cancer deaths, the Maximum Product Spacing for head and neck cancer deaths, the Least Squares for cervical cancer deaths, the Weighted Least Squares for bladder cancer survival, and the Anderson-Darling for advanced lung cancer deaths. This shows that the NEWE distribution can be used in a number of different cancers settings. The development and implementation of the NEWE distribution marks a significant advancement in oncological modeling. By analyzing both cancer mortality and survival data with enhanced accuracy and flexibility, this novel approach surpasses traditional models, offering deeper insights into cancer progression and treatment outcomes. As a result, the NEWE distribution equips healthcare professionals with a powerful tool for improving clinical decisions, leading to better prognostic assessments and patient care in cancer treatment.

A research on resistance spot welding quality judgment of stainless steel sheets based on revised quantum genetic algorithm and hidden markov model

In view of the following problems existed in the data-driven resistance spot welding (RSW) quality judgment methods: relying on expert experience, resulting in a high misjudgment rate; affected by the initial value, it is easy to fall into the local extreme point; multiple parameters needed to be modulated to increase the training time of the model. Therefore, this paper proposed a RSW quality judgment method based on revised quantum genetic algorithm (RQGA) and hidden markov model (HMM). Which used quantum rotation gate to replace the evolution process of genetic algorithm (GA), reducing the number of parameters that needed to be modulated in the model, and optimizing the initial model of HMM by RQGA to avoid falling into local extreme point. At the same time, combining expert experience with HMM to make quality judgment to ensure the accuracy of judgment.Firstly, by RSW test to obtain the main influencing factors of quality, and combining with expert experience to realize the preliminary judgment of it, on this basis, HMM was established. Next, to address the susceptibility of HMM to initial model, quantum genetic algorithm (QGA) was utilized to search for the optimal initial model. Moreover, in view of the limitations of QGA (“coding length disaster” and fixed rotation angle), a corresponding improvement was also presented. At last, the established model was applied to the RSW process of stainless steel sheets railway carriage roof. Compared with the other five models, it took the least time to complete parameter training of HMM in four quality states, less than 15s; when judging the quality, its log-likelihood probability value was −47.8418, which was close to the maximum value of this state; during the classification test, the average classification accuracy of the four quality states were 90.8 %, 92.2 %, 90.8 % and 92.0 %, respectively, which was higher than the other five models. Consequently, the model established in this paper was effective.

VALUE AT RISK ESTIMATION FOR STOCK PORTFOLIO USING THE ARCHIMEDEAN COPULA APPROACH

Investment is one of the many ways to achieve future profits. One form of investment that is widely made is stocks. The return obtained in investing in stocks is potentially higher than other investment alternatives, but the risks borne are amplified, so it is necessary to analyze these risks that may occur. In this study, the Archimedean copula method is used to estimate the Value at Risk on shares of PT Bank Rakyat Indonesia Tbk (BBRI) and PT Telekomunikasi Indonesia Tbk (TLKM) for the period September 1, 2021, to August 31, 2023. The stock data is used to determine the Archimedean copula model and calculate the estimated value of Value at Risk (VaR) on the stock return portfolio using the Archimedean copula approach. The Archimedean copula models used are the Clayton copula model, Gumbel copula, and Frank copula. Of the three Archimedean copula models, the best model was selected by looking at the largest Maximum Likelihood Estimation (MLE) value. In this study, the log-likelihood value of Clayton copula is 7.958, Gumbel copula is 6.663, and Frank copula is 8.398. Therefore, Frank copula is the best Archimedean copula model with the largest log-likelihood value of 8.398 for the said data. Then the VaR estimation is done with the Frank copula model. The Value at Risk estimation results based on the Frank copula model show maximum loss rates of -0.0277 at the 90% confidence level, -0.0363 at the 95% confidence level, and -0.0516 at the 99% confidence level.

Incorporating Honey Badger Algorithm in Estimating Gamma Distribution With Application to Stock Price Modelling

This study evaluates the performance of various estimation methods in stock price analysis across diverse parameters, focusing on the Honey Badger Algorithm (HBA). The purpose is to determine the most accurate and reliable method for parameter estimation. Methodologically, we analyze data spanning eight years from publicly traded Malaysian property companies, employing financial metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Our findings highlight HBA’s consistent precision in parameter estimation, with values closely aligning with initial parameters across different stock sizes. For example, HBA-Gamma model achieves an MAE of 0.0592 and an RMSE of 0.8458 for 13 stocks, demonstrating its proficiency in capturing stock price distributions in dynamic markets. In contrast, the Artificial Immune System (AIS) provides reasonable estimates but with higher variability. The Regression Method exhibits mixed outcomes, displaying accuracy in some cases but notable variability and reduced precision, especially with larger datasets. The Moment Method, while adequate, shows slightly higher variance compared to both HBA and AIS. Further analysis using Log Likelihood values confirms HBA’s superior fit to the data, consistently surpassing AIS, Regression Method, and Moment Method in likelihood maximization across various stock numbers. Specifically, HBA exhibits lower MAE and RMSE values of 0.1034 and 0.06723, respectively, for 26 stocks, further validating its effectiveness in parameter estimation and stock price prediction. These findings underscore the importance of integrated approaches that account for market nuances rather than relying solely on individual model forecasts. The results affirm HBA’s potential for informed investment decision-making, emphasizing its robust performance and enhanced predictive capabilities compared to alternative methodologies. However, further research is needed to assess the generalizability of these findings to other markets and contexts.

Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&amp;P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&amp;P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets.

Patient-level and Physician-level Predictors of Discharge Against Medical Advice: A Multilevel Modeling Approach

Background: Discharge Against Medical Advice (DAMA) is a complex and multifaceted issue in healthcare, often challenging the continuity of care and affecting patient outcomes. Objectives: This study aimed to investigate the predictors of Discharge Against Medical Advice (DAMA) by simultaneously examining patient- and physician-level variables within a unified analytical framework. Methods: This cross-sectional study was conducted in 2023 at one of the largest private hospitals in northwest Iran. The study included all 16,071 patients admitted in 2022 and 137 attending physicians. A multilevel analysis model was employed to examine the influence of variables related to patients and physicians in predicting DAMA. Results: The study involved patients with a mean age of 45.28 ± 19.59 years, with 67.7% being women and 14.8% not having health insurance. Among the physicians studied, the mean age was 56.27 ± 12.29 years, with 67.2% being male and 70.1% being hospital shareholders. Patients with DAMA comprised 6.8% (n = 1094). The null model had a log-likelihood value of -3304.90. When patient-level predictors were added, the value increased to -3041.76, a statistically significant improvement (P &lt; 0.001) based on the chi-square test. Subsequently, incorporating physician-level predictors further increased the log-likelihood value to -2996.16, and this increase was also statistically significant compared to the model with only patient-level predictors (P &lt; 0.001). Physician-level factors, including specialization, sex, and experience, were associated with DAMA. Significant patient-level variables included age, type of insurance, and type of disease (P &lt; 0.05). Conclusions: Utilizing multilevel modeling enables the assessment of the significance of both physician-level and patient-level factors. To avoid conflicting results, it is recommended to evaluate hospital performance based on DAMA by considering both levels.

Multi-modality trajectory prediction with the dynamic spatial interaction among vehicles under connected vehicle environment

Compared to non-connected vehicle environments, the connected vehicle environment establishes vehicle interconnection through communication technologies, resulting in more complex interaction, network topologies, and large-scale inputs. This complexity renders traditional trajectory prediction models, which rely primarily on inputting historical information of the target vehicle, inadequate for handling the complex and dynamic interactive lane-changing scenarios in connected vehicle environments. In a connected vehicle environment, it is necessary to propose a more targeted and stable lane-changing behavior prediction method based on vehicle traveling characteristics. Taking into account dynamic spatial interaction among vehicles, this study proposes a multi-modality trajectory prediction model called STA-LSTM to perform analysis on the potential interactive behaviors among vehicles under connected vehicle lane-changing scenarios, and specifically to expand the multi-modality feature input of the vehicle trajectory prediction model. The spatial grid occupancy method is used to model the interactions between vehicles. A space-dimensional attention mechanism is introduced to adaptively match the influencing weights of the surrounding vehicles with the target vehicle and to improve the interactive information extraction method. In addition, the attention module is incorporated into the LSTM decoder from the time dimension so that the established model can identify significant historical hidden features during each trajectory decoding process. To account for the uncertainty of trajectory prediction, the vectors of vehicle interactions are incorporated into contextual information to improve the reliability of prediction results and the robustness of the established model. Compared with conventional baseline models, the proposed model exhibited lower root mean square error (RMSE) and negative log-likelihood (NLL) values, and the RMSE values at different prediction times of 1s, 2s, 3s, 4s, and 5s are 0.46m, 1.15m, 1.89m, 2.84m, and 4.05m, respectively. This indicates that the proposed model can accurately predict the interactions between vehicles and the travel paths of surrounding target vehicles.

TODDLER FECES MANAGEMENT AND BASIC IMMUNIZATION ON THE INCIDENCE OF DIARRHEA: A SPATIAL ANALYSIS

Background: Diarrhea is one of the leading causes of child morbidity and mortality in low and middle-income countries. 23.8% prevalence of diarrhea was found among children under five years of age. Area-based management is required to address and provide specific interventions. Purpose: The study aims to analyze the effect of unsafe toddler feces management and incomplete primary immunization on the incidence of diarrhea using a spatial analysis in East Java, Indonesia. Methods: The data used is the 2018 Basic Health Research data. The measurement of the relationship between region and diarrhea was carried out by studying Moran's I index. Lisa Cluster Map and the Lisa Significance Map were also studied to understand distribution and significance. The regression used is OLS regression, spatial lag, and spatial error. The best model is assessed by comparing various parameters. Data were analyzed using GeoDA. Results: Moran's I result shows an index value of 0.489 with a pseudo-value of 0.001 (p &lt;0.05) and a z-value of 3.7515. Lisa Cluster Map shows seven high-high category areas, five low-low category areas, and one high-low category area. Lisa's Significance Map shows six areas p=0.05, three areas p=0.01, and four areas p=0.001. The unsafe management of toddler feces and incomplete primary immunization related to diarrhea shows a significance of p&lt;0.05. By comparing the R square, log-likelihood, p(sign), and sigma square values, the better model is the spatial lag model. Conclusion: The unsafe toddler feces management and incomplete primary immunization related to diarrhea.

Multivariate Generalized Linear Mixed Models for Count Data

Univariate regression models have rich literature for counting data. However, this is not the case for multivariate count data. Therefore, we present the Multivariate Generalized Linear Mixed Models framework that deals with a multivariate set of responses, measuring the correlation between them through random effects that follows a multivariate normal distribution. This model is based on a GLMM with a random intercept and the estimation process remains the same as a standard GLMM with random effects integrated out via Laplace approximation. We efficiently implemented this model through the TMB package available in R. We used Poisson, negative binomial (NB), and COM-Poisson distributions. To assess the estimator properties, we conducted a simulation study considering four different sample sizes and three different correlation values for each distribution. We achieved unbiased and consistent estimators for Poisson and NB distributions; for COM-Poisson estimators were consistent, but biased, especially for dispersion, variance, and correlation parameter estimators. These models were applied to two datasets. The first concerns a sample from 30 different sites collected in Australia where the number of times each one of the 41 different ant species was registered; which results in an impressive 820 variance-covariance and 41 dispersion parameters are estimated simultaneously, let alone the regression parameters. The second is from the Australia Health Survey with 5 response variables and 5190 respondents. These datasets can be considered overdispersed by the generalized dispersion index. The COM-Poisson model overcame the other two competitors considering three goodness-of-fit indexes, AIC, BIC, and maximized log-likelihood values. As a result, it estimated parameters with smaller standard errors and a greater number of significant correlation coefficients. Therefore, the proposed model is capable of dealing with multivariate count data, either under- equi- or overdispersed responses, and measuring any kind of correlation between them taking into account the effects of the covariates.

High-risk fertility behaviors and associated factors among married reproductive-age women in sub-Saharan Africa: A multilevel mixed-effect analysis of nationally representative data from 35 countries.

Although high-risk fertility behaviors are linked with poor maternal and child health outcomes, their prevalence remains higher in resource-limited countries and varies significantly by context. Evidence on the recent estimates of these fertility risks at the sub-Saharan Africa level is limited. Therefore, this study aimed to examine the pooled prevalence of high-risk fertility behaviors and associated factors among married women in this region. Data from DHS of 35 sub-Saharan African countries were used and a weighted sample of 243,657 married reproductive-age women were included in the analysis. A multilevel binary logistic regression models were fitted and the final model was selected based on the log-likelihood and deviance values. A p-value less than 0.05 and an adjusted odds ratio with a corresponding 95% confidence interval were used to identify the factors associated with high-risk fertility behaviors. The pooled prevalence of high-risk fertility behaviors among women in sub-Saharan Africa was 77.7% [95% CI = 77.6%-77.9], where 43.1% [95% CI: 42.9%-43.3%], and 31.4% [95% CI = 31.2%-31.6%] had a single risk and combination of two or three fertility risks, respectively. The highest level of single-risk fertility pattern was observed in Burundi (53.4%) and Chad had the highest prevalence of both at least one (89.9%) and multiple (53.6%) fertility risks. Early and polygamous marriages, low maternal and husband education, poor wealth index, unmet need for contraception, couple's fertility discordance, rural residence, high community-level early marriage practice, and low community-level women empowerment were associated with risky fertility behaviors. More than three-quarters of married women in SSA were engaged in high-risk fertility behaviors, with significant variations across the included countries. Therefore, addressing the modifiable risk factors like improving access to need-based contraceptive methods and empowering couples through education for a better understanding of their reproductive health with particular attention to rural settings are important in reducing these fertility risks. The results also suggest the need to strengthen the policies regulating the prohibition of early and polygamous marriages.

Modelling Mixed Traffic Time Headway Distribution Induced by Bus Rapid Transit (BRT) in Cape Town, South Africa

Background: The safety and capacity of roadways are immensely influenced by time headways, a major traffic flow characteristic. Time headways are frequently utilized in various aspects of traffic and transportation engineering studies, including capacity analyses, studies of safety, modelling of lane-changing and car-following behaviour, and level of service assessment. Time headway, measured in seconds, refers to the distance between two subsequent passing cars moving over a single spot on the road. This paper reports the statistical modelling of the time headway distribution of a mixed traffic flow scenario caused by BRT via statistical headway distribution models. The paper posits that the introduction of BRT dedicated lane and its adopted design configuration induces anomalous traffic flows on its adjoining lanes with attendant time headway differentials, irrespective of the type of adopted design configuration for the corridor. The objectives of this study were to fit the BRT-induced time headway distribution to probability distribution models and determine the most appropriate distribution model that fits the data derived using the goodness of fit tests. Consequently, this contribution, therefore, fills the gap in research with respect to determining the precise model that fits the time headway distribution of mixed traffic flow scenarios induced by BRT. Methods: The time headway distribution data collected at four designated road segments on a multilane provincial route R27 caused by BRT dedicated lane in Cape Town, South Africa, were fitted to five probability distribution models viz, Lognormal, Inverse Gaussian, Log-logistic, Generalized Extreme Value (GEV), and Burr using MATLAB software. The fitted headway data were with respect to the traffic flows on the adjoining lanes to BRT or traffic flow ‘without BRT’. Precisely, the empirical data were collected over a three-month period using an Automatic Traffic Counter (ATC). Using ModelRisk software, the goodness of fit of the probability models was assessed by the Akaike Information Criterion (AIC), the Schwartz or Bayesian Information Criterion (SIC or BIC), the Hannan Quinn Information Criterion (HQIC), and the loglikelihood (LLH) model performance criteria respectively. The distribution model with the lowest AIC criterion and largest loglikelihood (LLH) values describe the model that best fits the headway data across the four sites. Results: Results showed that the models fitted the headway data well at each site by visual inspection of the attendant Probability Density Function (PDF) and the Probability plots (P-P). However, the Burr distribution provided the overall best fit based on the AIC and LLH values at 95% confidence and 0.05 significance levels across the four sites. At sites 02, 03, and 04, it ranked first with the lowest AIC values of 4025.99, 2595.56, 3815.36, and corresponding largest LLH values of -2008.98, -1293.76, and -1903.66, respectively, while the lognormal distribution performed best at site 01, with AIC value of 4445.14 and LLH value of -2220.57, closely followed by the Burr distribution with AIC and LLH values of 4453.05 and -2222.51, respectively. The P-values, which ranged between 0.65 and 0.80, showed the likelihood of the occurrence of the data sets under the null hypothesis. Hence the null hypothesis was accepted. Conclusion: The study concluded that the introduction of BRT dedicated lanes affects the adjoining lanes’ mixed traffic time headways’ distribution, and the headways are continuously distributed, hence fitting continuous probability distributions.

Genetic diversity, Population Structure and their Association with Body Weight in Egyptian Chicken Strains

A Genetic characteristics and population structure within and among Egyptian indigenous chicken strains are important for identifying some genetic resources. The present study aimed to use microsatellite markers to determine similarity and genetic distance among different genotypes and their association with growth and production traits in Egyptian indigenous chicken strains. The current study included 800 chickens and 100 genomic DNA samples obtained from four Egyptian local chicken strains of four different areas (Dokki-4, Mandarah, Anshas, and Al-Salam) in Egypt. Their genetic characteristics, population structure, phylogenetic relationships, and their association with body weight were analyzed using seven microsatellite markers. The performance of 200 chicks from each strain was assessed in terms of individual body weight and growth rate. Al-Salam strain had a significantly higher body weight than the other strains up to 12 weeks of age among the four lines of Egyptian local chickens. Additionally, male chickens across all strains indicated significantly higher body weight than females from 2 weeks of age until the end of the experiment. The study revealed a total of 68 alleles from the 7 loci across 4 chicken strains, with an average of 9.71. The average of observed heterozygosity, expected heterozygosity, and polymorphism information content were 0.799, 0.358, and 0.707, respectively. The Mandarah strain had the highest observed allele number of 5.37; however, the lowest observed allele number was 3.12 for the Dokki strain. Analysis of population structure revealed that the four chicken strains should be divided into three clusters based on the highest log-likelihood values (ΔK value, 56.3). The results showed a degree of heterozygosity in the Mandara strain with 66.7% individual memberships, indicating a level of admixture. On the other hand, the Al-Salam strain indicated a high genetic diversity with 99% individual membership. The current study provides valuable insights for future genetic polymorphism studies, the advancement of breeding programs, and strategies for the conservation of the Egyptian local chicken strains.

Marginal Distribution Fitting Method for Modelling Flood Extremes on a River Network

This study utilized a max-stable process (MSP) model with a dependence structure defined via a non-Euclidean distance metric, with the goal of modelling extreme flood data on a river network. The dataset was composed of mean daily discharge observations from 22 United States Geological Survey streamflow gaging stations for river basins in Missouri and Arkansas. The analysis included the application of the elastic-net penalty to automatically build spatially varying trend surfaces to model the marginal distributions. The dependence model accounted for the river distance between hydrologically connected gaging sites and the hydrologic distance, defined as the Euclidean distance between the centers of site’s associated drainage areas, for all stations. Modelling the marginal distributions and spatial dependence among the extremes are two key components for spatially modelling extremes. Among the 16 covariates evaluated for marginal fitting, 7 were selected to spatially model the generalized extreme value (GEV) location parameter (for each gaging station’s contributing drainage basin, its outlet elevation, centroid x coordinate, centroid elevation, area, average basin width, elevation range, and median land surface slope). The three covariates selected for the GEV scale parameter included the area, average basin width, and median land surface slope. The GEV shape parameter was assumed to be constant throughout the entire study area. Comparisons of estimates obtained from the spatial covariate model with their corresponding “at-site” estimates resulted in computed values of 0.95, 0.95, 0.94 and 0.85, 0.84, 0.90 for the coefficient of determination, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency for the GEV location and scale parameters, respectively. Brown–Resnick MSP models were fit to independent multivariate events extracted from a set of common discharge data, transformed to unit Fréchet margins while considering different permutations of the non-Euclidean dependence model. Each of the fitted model’s log-likelihood values indicated improved fits when using hydrologic distance rather than Euclidean distance. They also demonstrated that accounting for flow-connected dependence and anisotropy further improved model fit. In this study, the results from both parts were illustrative; however, further research with larger datasets and more heterogeneous systems is recommended.

Abstract C077: Effects of treatment delays on colorectal cancer survival

Abstract Introduction: Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Understanding the influence of patient characteristics and treatment modalities on delays in care and related outcomes is crucial for optimizing treatment strategies and improving patient survival. Methods: A retrospective analysis of 2,359 CRC patients from the PLCO trial was conducted to assess the relationships between treatment initiation delays and survival outcomes. Descriptive statistics, Spearman's correlation coefficients, and Cox proportional hazards regression analyses were employed to analyze the data. Results: Among other factors, patients with disabilities and those using ibuprofen exhibited a higher hazard ratio and a lower hazard ratio, respectively, indicating the influence of these factors on patient outcomes. Despite examining multiple waiting time predictors, no significant associations were found between these predictors and mortality risk. The model's non-significant likelihood ratio chi-square and log-likelihood value suggested that it did not provide an adequate fit to the data. Discussion: This study highlights the importance of understanding the relationships between patient characteristics, treatment modalities, and their impact on patient outcomes in CRC patients. While curative resection was significantly associated with improved survival, neoadjuvant chemotherapy did not demonstrate a significant effect on mortality. The lack of association between waiting time predictors and mortality risk suggests the need to explore additional factors influencing patient outcomes. Conclusion: In conclusion, the study provides valuable insights into the relationships between patient characteristics, treatment modalities, and their impact on patient outcomes in CRC patients. Further research is needed to identify additional factors that may influence mortality risk and to optimize treatment strategies for this patient population. Citation Format: Percy Guzman Montero. Effects of treatment delays on colorectal cancer survival [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C077.

Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints

Road traffic accident (RTA) is a critical global public health concern, particularly in developing countries. Analyzing past fatalities and predicting future trends is vital for the development of road safety policies and regulations. The main objective of this study is to assess the effectiveness of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook (FB) Prophet models, with potential change points, in handling time-series road accident data involving seasonal patterns in contrast to other statistical methods employed by key governmental agencies such as Ghana's Motor Transport and Traffic Unit (MTTU). The aforementioned models underwent training with monthly RTA data spanning from 2013 to 2018. Their predictive accuracies were then evaluated using the test set, comprising monthly RTA data from 2019. The study employed the Box-Jenkins method on the training set, yielding the development of various tentative time series models to effectively capture the patterns in the monthly RTA data. SARIMA(0,1,1)×(1,0,0)12 was found to be the suitable model for forecasting RTAs with a log-likelihood value of −266.28, AIC value of 538.56, AICc value of 538.92, BIC value of 545.35. The findings disclosed that the SARIMA(0,1,1)×(1,0,0)12 model developed outperforms FB-Prophet with a forecast accuracy of 93.1025% as clearly depicted by the model's MAPE of 6.8975% and a Theil U1 statistic of 0.0376 compared to the FB-Prophet model's respective forecasted accuracy and Theil U1 statistic of 84.3569% and 0.1071. A Ljung-Box test on the residuals of the estimated SARIMA(0,1,1)×(1,0,0)12 model revealed that they are independent and free from auto/serial correlation. A Box-Pierce test for larger lags also revealed that the proposed model is adequate for forecasting. Due to the high forecast accuracy of the proposed SARIMA model, the study recommends the use of the proposed SARIMA model in the analysis of road traffic accidents in Ghana.

Design and application of an online teaching platform for public sports tennis in colleges and universities based on discrete choice regression model

Abstract At present, online teaching is becoming an increasingly popular teaching activity, and while it is gaining more and more popularity, its quality issues are becoming more and more prominent. This paper firstly analyzes the discrete regression model, and after comparing the logit model under random utility selection and its three derivative models, this paper selects the most widely used hybrid logit model to evaluate and analyze the online teaching platform of university public sports tennis with the MNL model of the maximum likelihood method for error analysis. Secondly, the overall architecture of the online teaching platform system is proposed, and the online teaching platform student satisfaction is studied and analyzed. Finally, based on the hybrid logit model, the AIC, BIC, and log-likelihood value indexes of the online teaching platform for college public education tennis are analyzed for the goodness of fit. The results of the study showed that students were more concerned about three aspects of the online teaching platform: operational difficulty, course comprehensiveness, and learning efficiency, which were 65%, 69%, and 74%, respectively. The AIC and BIC indicators of the hybrid logit model were 4753.656 and 4901.321, which were 192.142 and 117.848 lower than those of the conditional logit model, respectively, indicating that the model fits well and provides a better evaluation and analysis of the platform.

Comparative Analysis of Move-Specific Lexical Bundles in Linguistics Dissertation Abstracts: A Study of Students from China and the United States

This article compared three-word move-specific lexical bundles (MLBs) in dissertation abstracts authored by linguistics doctoral students from China and the United States. Two separate corpora were constructed for analysis: (1) The China Linguistics PhD Abstracts Corpus (CLC), consisting of 700 abstracts totaling 613,713 words generated by doctoral students from China specializing in linguistics, and (2) the America Linguistics PhD Abstracts Corpus (ALC), comprising 700 abstracts totaling 247,359 words written by American doctoral students in linguistics. The identification of MLBs was based on log-likelihood values, indicating a notably higher number of instances within a specific move in contrast to the overall corpus. The study then proceeded to compare the structures, functions, and communicative purposes of MLBs in the two corpora. The findings of the analysis are as follows: (1) Chinese students employed a higher percentage of NP- and PP-based MLBs but a lower proportion of VP-based MLBs, and both groups used different words within similar structures; (2) Chinese students employed a higher percentage of research- and participant-oriented MLBs but a lower proportion of text-oriented MLBs, and the two groups used dissimilar MLBs to fulfill similar functions; and (3) the two groups employed distinct MLBs to achieve identical communicative purposes, with Chinese students utilizing MLBs specific to the CLC corpus. These findings highlight the pedagogical significance of MLBs and underscore the importance of corpus linguistics in identifying and teaching essential move-specific vocabulary to second/foreign language learners.

Robust gradient boosting for generalized additive models for location, scale and shape

AbstractDue to the increasing complexity and dimensionality of data sources, it is favorable that methodological approaches yield robust results so that corrupted observations do not jeopardize overall conclusions. We propose a modelling approach which is robust towards outliers in the response variable for generalized additive models for location, scale and shape (GAMLSS). We extend a recently proposed robustification of the log-likelihood to gradient boosting for GAMLSS, which is based on trimming low log-likelihood values via a log-logistic function to a boundary depending on a robustness constant. We recommend a data-driven choice for the involved robustness constant based on a quantile of the unconditioned response variable and investigate the choice in a simulation study for low- and high-dimensional data situations. The versatile application possibilities of robust gradient boosting for GAMLSS are illustrated via three biomedical examples—including the modelling of thyroid hormone levels, spatial effects for functional magnetic resonance brain imaging and a high-dimensional application with gene expression levels for cancer cell lines.