OBJECTIVE: Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decreasing the mortality rate and improving the prognosis. So, this study.
METHODS: In this retrospective study, we used 654 alive and dead PC cases to establish the prediction model for PC. The six chosen machine learning algorithms and prognostic factors were utilized to build the prediction models. The importance of the predictive factors was assessed using the relative importance of a high-performing algorithm.
RESULTS: The XG-Boost with AU-ROC of 0.933 (95% CI= [0.906-0.958]) and AU-ROC of 0.836 (95% CI= [0.789-0.865] in internal and external validation modes were considered as the best-performing model for predicting the mortality risk of PC. The factors, including tumor size, smoking, and chemotherapy, were considered the most influential for prediction.
CONCLUSIONS: The XG-Boost gained more performance efficiency in predicting the mortality risk of PC patients, so this model can promote the clinical solutions that doctors can achieve in healthcare environments to decrease the mortality risk of these patients.

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors\' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance.

In almost every country, patents need to be renewed multiple times after they are granted. A patentee assesses the value of the patent and then pays a renewal fee to keep it active for another stipulated period. The factors that characterize the value of a patent is subjective. This paper aims to address the research gap of building an accurate model for predicting the renewal life (often considered as a substitute for the patent value) of Indian patents, and identification of significant factors that influence the renewal life. This study uses an extensive data set collected from the Indian Patent Office for all granted patents filed between 1995 and 2005. The popular statistical and machine learning algorithms do not result in accurate predictive models, because the patent renewal life distribution (at least for the Indian patents) shows unusual spikes at the two extreme values, which makes the modeling task more challenging. We propose a new two-stage hybrid model by combining an efficient multi-class classifier and a binomial regression model for predicting the complex renewal data distribution. We conducted a comparative analysis of the proposed model with several state-of-the-art machine learning and statistical models. The results show that the proposed hybrid model gives 90% accuracy as compared to the best competitor which gives only 40% accuracy.

Risk prediction models fitted using maximum likelihood estimation (MLE) are often overfitted resulting in predictions that are too extreme and a calibration slope (CS) less than 1. Penalized methods, such as Ridge and Lasso, have been suggested as a solution to this problem as they tend to shrink regression coefficients toward zero, resulting in predictions closer to the average. The amount of shrinkage is regulated by a tuning parameter, λ , $\\lambda ,$ commonly selected via cross-validation (\"standard tuning\"). Though penalized methods have been found to improve calibration on average, they often over-shrink and exhibit large variability in the selected λ $\\lambda $ and hence the CS. This is a problem, particularly for small sample sizes, but also when using sample sizes recommended to control overfitting. We consider whether these problems are partly due to selecting λ $\\lambda $ using cross-validation with \"training\" datasets of reduced size compared to the original development sample, resulting in an over-estimation of λ $\\lambda $ and, hence, excessive shrinkage. We propose a modified cross-validation tuning method (\"modified tuning\"), which estimates λ $\\lambda $ from a pseudo-development dataset obtained via bootstrapping from the original dataset, albeit of larger size, such that the resulting cross-validation training datasets are of the same size as the original dataset. Modified tuning can be easily implemented in standard software and is closely related to bootstrap selection of the tuning parameter (\"bootstrap tuning\"). We evaluated modified and bootstrap tuning for Ridge and Lasso in simulated and real data using recommended sample sizes, and sizes slightly lower and higher. They substantially improved the selection of λ $\\lambda $ , resulting in improved CS compared to the standard tuning method. They also improved predictions compared to MLE.

Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to measure the exposure accurately. A common type of exposure misclassification is recall bias, which occurs in retrospective cohort studies when study subjects may inaccurately recall their past exposure. Particularly challenging is differential recall bias in the context of self-reported binary exposures, where the bias may be directional rather than random and its extent varies according to the outcomes experienced. This paper makes several contributions: (1) it establishes bounds for the average treatment effect even when a validation study is not available; (2) it proposes multiple estimation methods across various strategies predicated on different assumptions; and (3) it suggests a sensitivity analysis technique to assess the robustness of the causal conclusion, incorporating insights from prior research. The effectiveness of these methods is demonstrated through simulation studies that explore various model misspecification scenarios. These approaches are then applied to investigate the effect of childhood physical abuse on mental health in adulthood.

Sports records play a crucial role in understanding the limits of human achievement in sports. However, a thorough exploration of a comprehensive analysis of various sports records utilizing the existing statistical model has been lacking. This study introduces a framework for analyzing the integrated features and evolutionary trends of 23 sports records for men and women. It includes world records and intercontinental records from six continents, covering 6440 athletes from 2001 to 2020. Our findings indicate that human beings have not yet reached sports limits in athletic performance, suggesting a continuous improvement over time. Furthermore, we have investigated the contributions of our model\'s parameters to the integrated features, emphasizing their robustness and convergence in handling data flow and information entropy. Additionally, our model underscores the significance of integrating various sports for ongoing advancement, in line with the Olympic motto \"Together,\" thereby promoting coordinated development.

The Latent Dirichlet Allocation (LDA) model is used to extract the text themes of newspaper news and construct the Chinese Economic Policy Uncertainty (EPU) Index. On this basis, based on the relevant data of Chinese A-share listed companies from 2008 to 2020, this paper empirically analyzes the impact of EPU on peer effects of firms R&D investment, and finds that EPU will aggravate the peer effects of firms R&D investment. Furthermore, the moderating effect of manager\'s motivation to maintain reputation on the process of EPU influencing the peer effects of firms R&D investment was tested, and the mechanism of EPU influencing the peer effects of firms R&D investment through financial frictions was verified.

BACKGROUND: HALE is now a regular strategic planning indicator for all levels of the Chinese government. However, HALE measurements necessitate comprehensive data collection and intricate technology. Therefore, effectively converting numerous diseases into the years lived with disability (YLD) rate is a significant challenge for HALE measurements. Our study aimed to construct a simple YLD rate measurement model with high applicability based on the current situation of actual data resources within China to address challenges in measuring HALE target values during planning.
METHODS: First, based on the Chinese YLD rate in the Global Burden of Disease (GBD) 2019, Pearson correlation analysis, the global optimum method, etc., was utilized to screen the best predictor variables from the current Chinese data resources. Missing data for predictor variables were filled in via spline interpolation. Then, multiple linear regression models were fitted to construct the YLD rate measurement model. The Sullivan method was used to measure HALE. The Monte Carlo method was employed to generate 95% uncertainty intervals. Finally, model performances were assessed using the mean absolute error (MAE) and mean absolute percentage error (MAPE).
RESULTS: A three-input-parameter model was constructed to measure the age-specific YLD rates by sex in China, directly using the incidence of infectious diseases, the incidence of chronic diseases among persons aged 15 and older, and the addition of an under-five mortality rate covariate. The total MAE and MAPE for the combined YLD rate were 0.0007 and 0.5949%, respectively. The MAE and MAPE of the combined HALE in the 0-year-old group were 0.0341 and 0.0526%, respectively. There were slightly fewer males (0.0197, 0.0311%) than females (0.0501, 0.0755%).
CONCLUSIONS: We constructed a high-accuracy model to measure the YLD rate in China by using three monitoring indicators from the Chinese national routine as predictor variables. The model provides a realistic and feasible solution for measuring HALE at the national and especially regional levels, considering limited data.

Well-known continuous distributions such as Beta and Kumaraswamy distribution are useful for modeling the datasets which are based on unit interval [0,1]. But every distribution is not always useful for all types of data sets, rather it depends on the shapes of data as well. In this research, a three-parameter new distribution named bounded exponentiated Weibull (BEW) distribution is defined to model the data set with the support of unit interval [0,1]. Some fundamental distributional properties for the BEW distribution have been investigated. For modeling dependence between measures in a dataset, a bivariate extension of the BEW distribution is developed, and graphical shapes for the bivariate BEW distribution have been shown. Several estimation methods have been discussed to estimate the parameters of the BEW distribution and to check the performance of the estimator, a Monte Carlo simulation study has been done. Afterward, the applications of the BEW distribution are illustrated using COVID-19 data sets. The proposed distribution shows a better fit than many well-known distributions. Lastly, a quantile regression model from bounded exponentiated Weibull distribution is developed, and its graphical shapes for the probability density function (PDF) and hazard function have been shown.

Haseman-Elston regression (HE-reg) has been known as a classic tool for detecting an additive genetic variance component. However, in this study we find that HE-reg can capture GxE under certain conditions, so we derive and reinterpret the analytical solution of HE-reg. In the presence of GxE, it leads to a natural discrepancy between linkage and association results, the latter of which is not able to capture GxE if the environment is unknown. Considering linkage and association as symmetric designs, we investigate how the symmetry can and cannot hold in the absence and presence of GxE, and consequently we propose a pair of statistical tests, Symmetry Test I and Symmetry Test II, both of which can be tested using summary statistics. Test statistics, and their statistical power issues are also investigated for Symmetry Tests I and II. Increasing the number of sib pairs is important to improve statistical power for detecting GxE.