Search Results (122)

Necrotizing fasciitis (NF) is a rare but aggressive soft tissue infection with high rates of mortality and amputation, making early identification of key prognostic biomarkers essential for clinical management. However, the rarity and heterogeneity of NF mean clinical datasets are often small and non-normally distributed, limiting the effectiveness of standard parametric statistical approaches. To address this, we retrospectively analyzed 66 NF patients using a robust, distribution-free framework that combines the Nonparametric Combination (NPC) methodology and bootstrap resampling. We specifically assessed glycated hemoglobin (HBA1C) and serum albumin (ALBUMINA) as potential predictors of two outcomes: mortality (MORTO) and major amputation (AMPUTAZIONE). NPC enabled exact multivariate hypothesis testing while rigorously controlling the family-wise error rate (FWER), and bootstrap resampling generated 95% confidence intervals (CI) for critical biomarkers. HBA1C was an exceptionally significant predictor compared to the 7.0% clinical threshold (p = 1.04 × 10⁻¹⁵⁴, CI: 0.0830–0.0957), while ALBUMINA showed greater biological variability but no significant association with outcomes (2.8 g/dL; p = 0.267, CI: 2.551–2.866). We also developed a global severity ranking, integrating multiple variables to improve clinical risk stratification. Our results demonstrate that permutation-based and resampling methods provide reliable, actionable insights from challenging small-sample clinical datasets. Based on a small-sample dataset from necrotizing fasciitis patients, this framework provides a replicable model for robust, nonparametric statistical analysis in similarly rare and high-risk medical conditions. This study introduces a Nonparametric Combination (NPC) framework for risk scoring in necrotizing fasciitis using bootstrap resampling and permutation tests. Key predictors like HBA1C and Albumin were assessed, achieving an AUC of 0.89 and a Youden Index of 0.71. The model offers a robust, interpretable tool for clinical risk stratification in small-sample rare disease settings. Full article

Life testing of products often requires extended observation periods. To shorten the duration of these tests, products can be subjected to more extreme conditions than those encountered in normal use; an approach known as accelerated life testing (ALT) is considered. This study investigates the estimation of unknown parameters and the acceleration factor for the modified Fréchet-Lomax exponential distribution (MFLED), utilizing Type II progressively first-failure censored (PFFC) samples obtained under the framework of constant-stress partially accelerated life testing (CSPALT). Maximum likelihood (ML) estimation is employed to obtain point estimates for the model parameters and the acceleration factor, while the Fisher information matrix is used to construct asymptotic confidence intervals (ACIs) for these estimates. To improve the precision of inference, two parametric bootstrap methods are also implemented. In the Bayesian context, a method for eliciting prior hyperparameters is proposed, and Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) method. These estimates are evaluated under both symmetric and asymmetric loss functions, and the corresponding credible intervals (CRIs) are computed. A comprehensive simulation study is conducted to compare the performance of ML, bootstrap, and Bayesian estimators in terms of mean squared error and coverage probabilities of confidence intervals. Finally, real-world failure time data of light-emitting diodes (LEDs) are analyzed to demonstrate the applicability and efficiency of the proposed methods in practical reliability studies, highlighting their value in modeling the lifetime behavior of electronic components. Full article

Background: Epidermal growth factor receptor (EGFR) mutations are presented in approximately 50% of East Asian populations with advanced non-small cell lung cancer (NSCLC). While EGFR-tyrosine kinase inhibitors (TKIs) are the standard treatment, patient outcomes are also influenced by host-related factors. This study aimed to investigate clinical and radiological factors associated with early mortality and develop a prognostic prediction model in advanced EGFR-mutated NSCLC. Methods: A retrospective cohort was conducted in patients with EGFR-mutated NSCLC treated with first line EGFR-TKIs from January 2012 to October 2022 at Chiang Mai University Hospital. Clinical data and radiologic findings at the initiation of treatment were analyzed. A multivariable flexible parametric survival model was used to determine the predictors of death at 18 months. The predicted survival probabilities at 6, 12, and 18 months were estimated, and the model performance was evaluated. Results: Among 189 patients, 84 (44.4%) died within 18 months. Significant predictors of mortality included body mass index <18.5 or ≥23, bone metastasis, neutrophil-to-lymphocyte ratio ≥ 5, albumin-to-globulin ratio < 1, and mean pulmonary artery diameter ≥ 29 mm. The model demonstrated good performance (Harrell’s C-statistic = 0.72; 95% CI: 0.66–0.78). Based on bootstrap internal validation, the optimism-corrected Harrell’s C-statistic was 0.71 (95% CI: 0.71–0.71), derived from an apparent C-statistic of 0.75 (95% CI: 0.74–0.75) and an estimated optimism of 0.04 (95% CI: 0.03–0.04). Estimated 18-month survival ranged from 87.1% in those without risk factors to 2.1% in those with all predictors. A web-based tool was developed for clinical use. Conclusions: The prognostic model developed from fundamental clinical and radiologic parameters demonstrated promising utility in predicting 18-month mortality in patients with advanced EGFR-mutated NSCLC receiving first-line EGFR-TKI therapy. Full article

We propose the zero-inflated Polynomially Adjusted Poisson (zPAP) model. It extends the usual zero-inflated Poisson by multiplying the Poisson kernel with a nonnegative polynomial, enabling the model to handle extra zeros, overdispersion, skewness, and even multimodal counts. We derive the maximum-likelihood framework—including the log-likelihood and score equations under both general and regression settings—and fit zPAP to the zero-inflated, highly dispersed Fish Catch data as well as a synthetic bimodal mixture. In both cases, zPAP not only outperforms the standard zero-inflated Poisson model but also yields reliable inference via parametric bootstrap confidence intervals. Overall, zPAP is a clear and tractable tool for real-world count data with complex features. Full article

This study investigates the effects of transcutaneous electroacupuncture stimulation (TEAS) on eyeblink rate, EEG, and heart rate variability (HRV), emphasising whether eyeblink data—often dismissed as artefacts—can serve as useful physiological markers. Sixty-six participants underwent four TEAS sessions with different stimulation frequencies (2.5, 10, 80, and 160 pps, with 160 pps as a low-amplitude sham). EEG, ECG, PPG, and respiration data were recorded before, during, and after stimulation. Using non-parametric statistical analyses, including Friedman’s test, Wilcoxon, Conover–Iman, and bootstrapping, the study found significant changes across eyeblink, EEG, and HRV measures. Eyeblink laterality, particularly at 2.5 and 10 pps, showed strong frequency-specific effects. EEG power asymmetry and spectral centroids were associated with HRV indices, and 2.5 pps stimulation produced the strongest parasympathetic HRV response. Blink rate correlated with increased sympathetic and decreased parasympathetic activity. Baseline HRV measures, such as lower heart rate, predicted participant dropout. Eyeblinks were analysed using BLINKER software (v. 1.1.0), and additional complexity and entropy (‘CEPS-BLINKER’) metrics were derived. These measures were more predictive of adverse reactions than EEG-derived indices. Overall, TEAS modulates multiple physiological markers in a frequency-specific manner. Eyeblink characteristics, especially laterality, may offer valuable insights into autonomic function and TEAS efficacy in neuromodulation research. Full article

Background/Objectives: Amelogenesis imperfecta (AI) represents a heterogeneous group of inherited disorders affecting the quality and quantity of dental enamel, making clinical diagnosis challenging. This study aimed to identify genetic variants in Slovenian patients with non-syndromic AI and to evaluate enamel morphology using radiographic parameters. Methods: Whole exome sequencing (WES) was performed on 24 AI patients and their families. Panoramic radiographs (OPTs) were analyzed using Fiji ImageJ to assess crown dimensions, enamel angle (EA), dentine angle (DA), and enamel–dentine mineralization ratio (EDMR) in lower second molar buds, compared to matched controls (n = 24). Two observers independently assessed measurements, and non-parametric tests compared EA, DA, and EDMR in patients with and without disease-causing variants (DCVs). Statistical models, including bootstrap-validated random forest and logistic regression, assessed variable influences. Results: DCVs were identified in ENAM (40% of families), AMELX (15%), and MMP20 (10%), including four novel variants. AI patients showed significant enamel deviations with high reproducibility, particularly in hypomineralized and hypoplastic regions. DA and EDMR showed significant correlations with DCVs (p < 0.01). A bootstrap-validated random forest model yielded a 90% (84.0–98.0%) AUC-estimated predictive power. Conclusions: These findings highlight a novel and reproducible radiographic approach for detecting developmental enamel defects in AI and support its diagnostic potential. Full article

Settlement analyses of foundation soils are very important for the investigation, design, and construction of buildings. However, due to complex natural sedimentary processes, soil-forming environments, and geological tectonic stress histories, settlement properties show obvious spatial variability and autocorrelation. Moreover, measurement data on the physical and mechanical parameters of building foundation soils are limited. This limits the accuracy of formation stability analyses and safety evaluations. In this study, a series of field tests of building foundation soils were carried out, and the statistical physical and mechanical properties of the clay strata were obtained. A random field method and copula functions of uncertain geotechnical properties with limited survey data are proposed. A dual-yield surface constitutive model of the soil properties and a stability analysis method for uncertain deformation were developed. The detailed analytical procedures for soil deformation and stratum settlement are presented. The reliability functions and failure probabilities of variable settlement processes are calculated and analyzed. The impact of the spatial variation and cross-correlation of geotechnical properties on the probabilistic stability of variable land subsidence is discussed. This work presents an innovative analysis approach for evaluating the variable settlement properties of building foundation soils. The results show that the four different mechanical parameters can be regressed to linear equations. The horizontal fluctuation scale is significantly larger than the vertical scale. Copula theory provides a powerful framework for modeling limited geotechnical parameters. The bootstrap approach avoids parametric assumptions, leveraging empirical data to enhance the reliability analysis of variable settlement. The variability parameter exerts a greater influence on land subsidence processes than the correlation structure. The failure probabilities of variable stratum settlement for different cross-correlations of building foundation soils are different. These results provide an important reference for the safety of building engineering. Full article

Coastal cities and low-lying areas are increasingly vulnerable, and accurate data is needed to identify where interventions are most required. We compared 53 cities affected by a 1 m increase in land levels and a 2 m rise in sea levels. The geographical scope of this study covered selected coastal cities in Europe and northern Africa. Data were sourced from the European Environment Agency (EEA) in the form of prepared datasets, which were further processed for analysis. Statistical methods were applied to compare the extent of urban flooding under two sea level rise scenarios—1 m and 2 m—by calculating the percentage of affected urban areas. To assess social vulnerability, the analysis included several variables: MAPF65 (Mean Area Potentially Flooded for people aged 65 and older, indicating elderly exposure), Age (the percentage of the population aged 65+ in each city), MAPF (Mean Area Potentially Flooded, representing the average share of urban area at risk of flooding), and Unemployment Ratio (the percentage of unemployed individuals living in the areas potentially affected by sea level rise). We utilized t-tests to analyze the means of two datasets, yielding a mean difference of 2.9536. Both parametric and bootstrap confidence intervals included zero, and the p-values from the t-tests (0.289 and 0.289) indicated no statistically significant difference between the means. The Bayes factor (0.178) provided substantial evidence supporting equal means, while Cohen’s D (0.099) indicated a very small effect size. Ceuta’s flooding value (502.8) was identified as a significant outlier (p < 0.05), indicating high flood risk. A Grubbs’ test confirmed Ceuta as a significant outlier. A Wilcoxon test highlighted significant deviations between the medians, with a p << 0.001, demonstrating systematic discrepancies tied to flood frequency and sea level anomalies. These findings illuminated critical disparities in flooding trends across specific locations, offering essential insights for urban planning and mitigation strategies in cities vulnerable to rising sea levels and extreme weather patterns. Information on coastal flooding provides awareness of how rising sea levels affect at-risk areas. Examining factors such as MAPF and population data enables the detection of the most threatened zones and supports targeted action. These perceptions are essential for strengthening climate resilience, improving emergency planning, and directing resources where they are needed most. Full article

Cardiovascular diseases continue to pose a major global health burden, contributing significantly to mortality rates worldwide. This study aimed to explore the association between myocardial infarction and basic hematological parameters—red blood cells (RBCs), white blood cells (WBCs), and platelets (PLTs)—which are routinely assessed in clinical diagnostics. The analysis was conducted on a cohort of 743 adults hospitalized with diagnosed cardiovascular conditions. To identify blood parameters that distinguish patients with a history of first-time myocardial infarction from those who had never experienced such an event, we employed a dual analytic approach. Standard parametric methods were complemented with bootstrap resampling to strengthen inference and mitigate the impact of sampling variability. Patients with myocardial infarction showed decreased RBC and elevated WBC counts relative to those without infarction. These associations were non-linear, with the most pronounced group differences observed within the second and third quartiles of RBC and WBC distributions, while minimal differences appeared at the distributional extremes. No significant differences were found in platelet count (PLT) between the groups. Bootstrap validation not only corroborated findings obtained through traditional statistics, but also enhanced the robustness of the results, providing improved estimates under data conditions prone to skewness or small sample artifacts. This approach enabled the detection of nuanced patterns that might elude classical inference. Our findings emphasize the utility of resampling techniques in clinical research settings, particularly where inference stability is critical. Incorporating such methods in future investigations may advance statistical rigor, increase reproducibility, and better capture complex biological relationships in medical datasets. Full article

Environmental transmission is a critical driver of cholera dynamics and a key factor influencing model-based inference and forecasting. This study focuses on stable parameter estimation and forecasting of cholera outbreaks using a compartmental SIRB model informed by three formulations of the environmental transmission rate: (1) a pre-parameterized periodic function, (2) a temperature-driven function, and (3) a flexible, data-driven time-dependent function. We apply these methods to the 1991–1997 cholera epidemic in Peru, estimating key parameters; these include the case reporting rate and human-to-human transmission rate. We assess practical identifiability via parametric bootstrapping and compare the performance of each transmission formulation in fitting epidemic data and forecasting short-term incidence. Our results demonstrate that while the data-driven approach achieves superior in-sample fit, the temperature-dependent model offers better forecasting performance due to its ability to incorporate seasonal trends. The study highlights trade-offs between model flexibility and parameter identifiability and provides a framework for evaluating cholera transmission models under data limitations. These insights can inform public health strategies for outbreak preparedness and response. Full article

Estimating confidence intervals in small or noisy datasets is a recurring challenge in biomolecular research, particularly when data contain outliers or exhibit high variability. This study introduces a robust statistical method that combines a hybrid bootstrap procedure with Steiner’s most frequent value (MFV) approach to estimate confidence intervals without removing outliers or altering the original dataset. The MFV technique identifies the most representative value while minimizing information loss, making it well suited for datasets with limited sample sizes or non-Gaussian distributions. To demonstrate the method’s robustness, we intentionally selected a dataset from outside the biomolecular domain: a fast-neutron activation cross-section of the ¹⁰⁹Ag(n, 2n)^108mAg reaction from nuclear physics. This dataset presents large uncertainties, inconsistencies, and known evaluation difficulties. Confidence intervals for the cross-section were determined using a method called the MFV–hybrid parametric bootstrapping (MFV-HPB) framework. In this approach, the original data points were repeatedly resampled, and new values were simulated based on their uncertainties before the MFV was calculated. Despite the dataset’s complexity, the method yielded a stable MFV estimate of 709 mb with a 68.27% confidence interval of [691, 744] mb, illustrating the method’s ability to provide interpretable results in challenging scenarios. Although the example is from nuclear science, the same statistical issues commonly arise in biomolecular fields, such as enzymatic kinetics, molecular assays, and diagnostic biomarker studies. The MFV-HPB framework provides a reliable and generalizable approach for extracting central estimates and confidence intervals in situations where data are difficult to collect, replicate, or interpret. Its resilience to outliers, independence from distributional assumptions, and compatibility with small-sample scenarios make it particularly valuable in molecular medicine, bioengineering, and biophysics. Full article

Background/Objectives: Proper confidence interval estimation of the area under the receiver operating characteristic curve (AUC), the net reclassification index (NRI), and the integrated discrimination improvement (IDI) is an area of ongoing research. The most common confidence interval estimation methods employ asymptotic theory. However, developments demonstrate that degeneration of the normal distribution assumption under the null hypothesis exists for measures such as the change in AUC (ΔAUC) and IDI, and confidence intervals estimated under the normal distribution assumption may be invalid. We aim to study the performance of confidence intervals derived assuming asymptotic theory and those derived with non-parametric bootstrapping methods. Methods: We examine the performance of ΔAUC, NRI, and IDI in both the logistic and survival regression context. We explore empirical distributions and compare coverage probabilities of asymptotic confidence intervals with those produced from bootstrapping methods through simulation. Results: The primary finding in both the logistic framework and the survival analysis framework is that the percentile CIs performed well regarding coverage, without compromise to their width; this finding was robust in most scenarios. Conclusions: Our results suggest that the asymptotic intervals are only appropriate when a strong effect size of the added parameter exists, and that the percentile bootstrap interval exhibits at least a reasonable coverage while maintaining the shortest width in nearly all simulated scenarios, making this interval the most reliable choice. The intent is that these recommendations improve the accuracy in the estimation and the overall assessment of discrimination improvement. Full article

The methodology carried out in this work is based on non-parametric inference. The problem is framed as a regression analysis, and the solution is derived using the permutation approach. The proposed test does not rely on the assumption that the distribution of the response follows a specific family of probability laws, unlike other parametric approaches. This makes the test powerful, particularly when the typical assumptions of parametric approaches, such as the normality of data, are not satisfied and parametric tests are not reliable. Furthermore, this method is more flexible and robust with respect to parametric tests. A permutation test on the goodness-of-fit of a multiple regression model is applied. Hence, proposed solution consists of the application of permutation tests on the significance of the single coefficients and then a combined permutation test (CPT) to solve the overall goodness-of-fit testing problem. Furthermore, a Monte Carlo simulation study was performed to evaluate the power of the previously mentioned permutation approach, comparing it with the conventional parametric F-test for ANOVA and the bootstrap combined test, both commonly discussed in the literature on this statistical problem. Finally, the proposed non-parametric test was applied to real-world data to investigate the impact of age and smoking habits on medical insurance costs in the USA. The findings suggest that smoking and being at least 50 years old significantly contribute to increased medical insurance costs. Full article

The inverse Gaussian (IG) distribution exhibits asymmetry and right skewness. This distribution presents values uniformly, encompassing wait length, stochastic processes, and rates of accident occurrences. The delta-inverse Gaussian (delta-IG) distribution is suitable for modeling traffic accident data as a mortality count, especially in cases when accidents may not occur. The confidence interval (CI) for the variance and standard deviation of the delta-IG distribution for the accident count is crucial for evaluating risk, allocating resources, and formulating enhancement protocols for transportation safety. We aim to construct confidence intervals for variance and standard deviation in the delta-IG population using several approaches: Adjusted GCI (AGCI), Parametric Bootstrap Percentile CI (PBPCI), fiducial CI (FCI), and Bayesian credible interval (BCI). The AGCI, PBPCI, and FCI will be utilized with estimation methods for proportions which are VST, Wilson’s score, and Hannig approaches. Monte Carlo simulations were evaluated, and the suggested confidence interval approach was employed for the average width (AW) and coverage probability (CP). The findings demonstrated that the AGCI based on the VST method employed successful approaches, as seen in their CP and AW. We employed these approaches to produce CIs for the variance and S.D. of the mortality count in Bangkok. Full article

The Chalk River Laboratories (CRL) site in Ontario, Canada, has long been a hub for nuclear research, which has resulted in the accumulation of legacy nuclear waste, including radioactive materials such as uranium, plutonium, and other radionuclides. Effective management of this legacy requires precise contamination and risk assessments, with a particular focus on the concentration levels of fissile materials such as ${}^{235}\mathrm{U}$. These assessments are essential for maintaining nuclear criticality safety. This study estimates the upper bounds of ${}^{235}\mathrm{U}$ concentrations. We investigated the use of a hybrid parametric bootstrapping method and robust statistical techniques to analyze datasets with outliers, then compared these outcomes with those derived from nonparametric bootstrapping. This study underscores the significance of measuring ${}^{235}\mathrm{U}$ for ensuring safety, conducting environmental monitoring, and adhering to regulatory compliance requirements at nuclear legacy sites. We used publicly accessible ${}^{235}\mathrm{U}$ data from the Eastern Desert of Egypt to demonstrate the application of these statistical methods to small datasets, providing reliable upper limit estimates that are vital for remediation and decommissioning efforts. This method seeks to enhance the interpretation of sensor data, ultimately supporting safer nuclear waste management practices at legacy sites such as CRL. Full article