Search Results (42)

Background/Objectives: Temporomandibular disorders (TMDs) are a heterogeneous subset of orthodontic conditions with persistent diagnostic and reporting variability. This review compared transparency, reporting quality, and spin prevalence in TMD/TMJ (temporomandibular joint)-focused orthodontic randomized controlled trials (RCTs) versus general orthodontic RCTs. Methods: The review followed PRISMA 2020 and was registered in PROSPERO (4201024184). Searches were performed in PubMed/MEDLINE, Embase, CINAHL, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform from the earliest available records in each database up to 15 October 2025. Eligible studies were peer-reviewed human orthodontic RCTs. Five transparency indicators (funding disclosure, bias discussion, confounder consideration, protocol registration, reporting-guideline adherence) and five spin indicators (selective focus, unsupported efficacy claims, emphasis on benefits, recommendations despite nonsignificance, “trend toward significance” language) were coded dichotomously. Beta–binomial mixed-effects models compared composite scores between groups, adjusting for publication era, impact factor, and journal clustering. Results: Among 874 included trials (840 general, 34 TMD/TMJ-focused), TMD/TMJ-focused studies showed lower adjusted transparency (odds ratio (OR) = 0.58; 95% confidence interval (CI) 0.34–0.99; p = 0.047), mainly due to limited registration and incomplete guideline adherence. Predicted transparency proportions were 0.82 for general and 0.73 for TMD/TMJ-focused studies. Composite spin did not differ (OR = 1.05; 95% CI 0.68–1.62; p = 0.821), though TMD/TMJ-focused abstracts more often emphasized benefits (OR = 4.62) and recommended interventions despite nonsignificant primary outcomes (OR = 2.83). Conclusions: TMD-focused orthodontic trials exhibited lower transparency and a distinct pattern of interpretive spin, particularly a greater tendency to emphasize benefits or recommend interventions despite non-significant results, compared with general orthodontic research. Full article

Background/Objectives: Antimicrobial resistance (AMR) threatens global public health. This systematic review and meta-analysis, as part of the “ENVIRE” project (interventions to control the dynamics of antimicrobial resistance from chickens through the environment), assesses the prevalence of phenotypic and genotypic resistance, including extended-spectrum beta-lactamases (ESBLs), AmpC beta-lactamases, carbapenemases, colistin, and fluoroquinolone resistance, in broiler chickens and their environment. Methods: The analysis covers the years 2002–2022, focusing on Escherichia (E.) coli, Klebsiella spp., Enterobacter spp., and Citrobacter spp. in fecal, meat, environmental, and other-than-feces samples from observational studies published in PubMed and Web of Science. Quality assessment was performed using the Alberta Heritage Foundation criteria. Results: Data from 170 studies, conducted in Europe, North Africa, and North America, were included. The most frequently studied resistance was to beta-lactam, with focus on ESBL-producing and AmpC beta-lactamase isolates. The pooled prevalence of ESBL-resistant E. coli observed in meat samples at 41% and in fecal samples at 38% demonstrated significant heterogeneity between the studies. The negative binomial regression analysis of prevalence data revealed significantly higher ESBL-producing E. coli rates in European meat samples compared to North African samples. Conclusions: This systematic review revealed substantial variation in prevalence and emphasizes the need for standardized surveillance systems and robust study designs. Full article

Risk management is vital for financial institutions to evaluate and mitigate potential losses. Thunderstorm count modeling with risk analysis is used by various sectors, such as insurance and utility companies, to forecast storm recurrence, analyze risk, and estimate financial losses based on factors like wind speed, hail size, and tornado potential. This paper introduces a novel discrete distribution, the Beta-Gamma Discrete (BGD) distribution, designed for modeling count data that inherently excludes zero values. Developed through the compounding of a discrete gamma distribution with a beta distribution, the BGD offers significant flexibility in handling overdispersion and complex data characteristics. The study derives key statistical properties of the BGD, including its probability mass function, moments, hazard rate function, moment generating function, and mean residual life. A comprehensive characterization theorem is also established. The model’s practical utility is demonstrated through an application to thunderstorm event data from the Kennedy Space Center (KSC), where the frequency of thunderstorms per event is a critical operational concern. The performance of the BGD is thoroughly assessed against established zero-truncated models—namely, the Zero-Truncated Generalized Poisson (ZTGP), Size-Biased Negative Binomial (SBNB), and Zero-Truncated Generalized Negative Binomial (ZTGNB)—using evaluation criteria such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Chi-square goodness-of-fit, and the Vuong test. The results consistently show that the BGD provides a superior and more accurate fit for the thunderstorm data, thus help NASA and other space agencies for establishing it as a robust and effective tool for modeling positive count data in meteorological and other applied contexts with risk analysis. Full article

The coronavirus disease 2019 (COVID-19) pandemic highlighted the critical need for scalable, timely, and unbiased methods to monitor disease transmission at the population level. Wastewater-based epidemiology (WBE) provides an effective method for monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission by detecting viral RNA shed into the sewage system. Because it does not rely on individual testing, WBE can offer timely, cost-effective, and community-level insights into infection trends. In this study, we present a hierarchical Beta-Binomial model that integrates SARS-CoV-2 RNA concentration in wastewater with reported COVID-19 case counts to enhance the monitoring of community-level transmission dynamics. The model incorporates wastewater viral loads as a predictor and reported cases as the response, while adjusting for testing volume to account for biases introduced by fluctuations in testing practices. This approach enables reliable estimation of the effective reproduction number ($R_t$), even in the absence of consistent reporting of clinical data. Applied to twenty counties in California, our modeling framework demonstrates the potential of wastewater surveillance to inform public health decision making, particularly in locations with sparse clinical data. Full article

Progressive first-failure censoring is a flexible and cost-efficient strategy that captures real-world testing scenarios where only the first failure is observed at each stage while randomly removing remaining units, making it ideal for biomedical and reliability studies. By applying the $\alpha$-power transformation to the exponential baseline, the proposed model introduces an additional flexibility parameter that enriches the family of lifetime distributions, enabling it to better capture varying failure rates and diverse hazard rate behaviors commonly observed in biomedical data, thus extending the classical exponential model. This study develops a novel computational framework for analyzing an $\alpha$-powered exponential model under beta-binomial random removals within the proposed censoring test. To address the inherent complexity of the likelihood function arising from simultaneous random removals and progressive censoring, we derive closed-form expressions for the likelihood, survival, and hazard functions and propose efficient estimation strategies based on both maximum likelihood and Bayesian inference. For the Bayesian approach, gamma and beta priors are adopted, and a tailored Metropolis–Hastings algorithm is implemented to approximate posterior distributions under symmetric and asymmetric loss functions. To evaluate the empirical performance of the proposed estimators, extensive Monte Carlo simulations are conducted, examining bias, mean squared error, and credible interval coverage under varying censoring levels and removal probabilities. Furthermore, the practical utility of the model is illustrated through three oncological datasets, including multiple myeloma, lung cancer, and breast cancer patients, demonstrating superior goodness of fit and predictive reliability compared to traditional models. The results show that the proposed lifespan model, under the beta-binomial probability law and within the examined censoring mechanism, offers a flexible and computationally tractable framework for reliability and biomedical survival analysis, providing new insights into censored data structures with random withdrawals. Full article

Prenatal tobacco smoke exposure (TSE) has been associated with significant alterations in DNA methylation (DNAm), an epigenetic mechanism with potential functional consequences to child development. This pilot study aimed to investigate differential DNAm patterns in preterm children with and without prenatal TSE using reduced representation bisulfite sequencing (RRBS) to interrogate a wider array of sites than in more common approaches, namely microarrays. Buccal swabs were collected from 16 two-year-old children (7 with TSE, 9 without), and DNAm was quantified at over 1.3 million CpG sites. To identify differential DNAm, univariable analyses were first performed and followed by Bayesian beta-binomial hierarchical regression models for sequence count data including adjustment for potential confounders. False Discovery Rate correction was used to account for multiple comparisons. Significant differential methylation was observed at CpG sites within intronic regions of the CALN1 and LINGO1 genes and the distal intergenic region of the TBL1XR1 gene. These findings suggest that prenatal TSE may influence epigenetic regulation in genes involved in neurodevelopment. This study demonstrates the importance of RRBS in identifying novel DNAm changes associated with prenatal TSE and highlights the need for larger studies to validate and expand upon these preliminary findings. Full article

One of the main motivations of this paper is to construct generating functions for generalization of the Touchard polynomials (or generalization exponential functions) and certain special numbers. Many novel formulas and relations for these polynomials are found by using the Euler derivative operator and functional equations of these functions. Some novel relations among these polynomials, beta polynomials, Bernstein polynomials, related to Binomial distribution from discrete probability distribution classes, are given. Full article

In complex reliability applications, it is common for the failure of an individual or an item to be attributed to multiple causes known as competing risks. This paper explores the estimation of the Hjorth competing risks model based on an adaptive progressive Type II censoring scheme via a binomial removal mechanism. For parameter and reliability metric estimation, both frequentist and Bayesian methodologies are developed. Maximum likelihood estimates for the Hjorth parameters are computed numerically due to their intricate form, while the binomial removal parameter is derived explicitly. Confidence intervals are constructed using asymptotic approximations. Within the Bayesian paradigm, gamma priors are assigned to the Hjorth parameters and a beta prior for the binomial parameter, facilitating posterior analysis. Markov Chain Monte Carlo techniques yield Bayesian estimates and credible intervals for parameters and reliability measures. The performance of the proposed methods is compared using Monte Carlo simulations. Finally, to illustrate the practical applicability of the proposed methodology, two real-world competing risk data sets are analyzed: one representing the breaking strength of jute fibers and the other representing the failure modes of electrical appliances. Full article

Write $\mathrm{pbeta}(·,\alpha ,\beta )$ for the distribution function of the Beta distribution with parameters $\alpha$ and $\beta$. We show that $\alpha \mapsto pbeta(\alpha /(\alpha +\beta ),\alpha ,\beta )$ is decreasing and $\alpha \mapsto pbeta(\alpha /(\alpha +\beta ),\alpha +1,\beta )$ is increasing over the positive reals, with the common limit for $\alpha \to \infty$ expressible in terms of the Gamma distribution functions, and discuss implications for the distribution functions of the Gamma, Poisson and Binomial distributions. Full article

Empirical Bayes-based Methods (EBM) is an increasingly popular form of Objective Bayesianism (OB). It is identified in particular with the statistician Bradley Efron. The main aims of this paper are, first, to describe and illustrate its main features and, second, to locate its role by comparing it with two other statistical paradigms, Subjective Bayesianism (SB) and Evidentialism. EBM’s main formal features are illustrated in some detail by schematic examples. The comparison between what Efron calls their underlying “philosophies” is by way of a distinction made between confirmation and evidence. Although this distinction is sometimes made in the statistical literature, it is relatively rare and never to the same point as here. That is, the distinction is invariably spelled out intra- and not inter-paradigmatically solely in terms of one or the other accounts. The distinction made in this paper between confirmation and evidence is illustrated by two well-known statistical paradoxes: the base-rate fallacy and Popper’s paradox of ideal evidence. The general conclusion reached is that each of the paradigms has a basic role to play and all are required by an adequate account of statistical inference from a technically informed and fine-grained philosophical perspective. Full article

In this paper, we propose an alternative distribution to model count data exhibiting uni/bimodality. It arises as a weighted version of the beta-binomial distribution, which is defined by a parametric weight function that admits up to two modes for the resulting probability mass function. Like the baseline beta-binomial distribution, the proposed distribution performs well in modeling overdispersed binomial data. Structural properties of the new distribution are studied. Raw moments are derived, which are used to describe the dispersion behavior relative to the mean and the skewness behavior. Parameter estimation is carried out using the maximum likelihood method. A simulation study is conducted in order to illustrate the behavior of the estimators. Finally, two applications illustrating the usefulness of the proposal are presented. Full article

Purpose: This study aimed to investigate the association between social jetlag (SJL) and obesity-related outcomes among adolescents from Valle de Ricote (Region of Murcia, Spain). We explored the relationship between SJL and body mass index (BMI) z-score, waist circumference, and body fat percentage, as well as the odds of having excess weight, obesity, and abdominal obesity in a sample of Spanish adolescents. Methods: A cross-sectional study was conducted using data from the Eating Healthy and Daily Life Activities (EHDLA) project, which included 847 Spanish adolescents aged 12–17 years. SJL was assessed based on the differences in sleep patterns between weekdays and weekends. Obesity-related indicators such as BMI z-score, waist circumference, body fat percentage, excess weight, obesity, and abdominal obesity were measured. Generalized linear models with a Gaussian or binomial distribution were used to analyze the associations between SJL and obesity-related outcomes, adjusting for potential confounders. Results: The analysis revealed significant associations between SJL and BMI z-score (unstandardized beta coefficient [B] = 0.15, 95% CI: 0.05 to 0.25, p = 0.003), waist circumference (B = 1.03, 95% CI: 0.39 to 1.67, p = 0.002), and body fat percentage (B = 0.83, 95% CI: 0.31 to 1.43, p = 0.008). Additionally, the odds ratios (ORs) for excess weight (OR = 1.35, 95% CI: 1.16 to 1.57; p < 0.001), obesity (OR = 1.59, 95% CI: 1.26 to 2.00; p < 0.001), and abdominal obesity (OR = 1.46, 95% CI: 1.23 to 1.72; p < 0.001) increased significantly with each 60 min increment in SJL. Conclusions: This study pointed out that the misalignment of sleeping times during weekdays and weekends (SJL) is significantly associated with higher BMI z-scores, waist circumference, body fat percentage, and higher odds of excess weight, obesity, and abdominal obesity among adolescents, being more significant in boys than in girls. These findings highlight the importance of addressing circadian misalignment in the prevention and management of obesity and its related metabolic disorders in this population. Full article

A measurement performed on a quantum system is an act of gaining information about its state. However, in the foundations of quantum theory, the concept of information is multiply defined, particularly in the area of quantum reconstruction, and its conceptual foundations remain surprisingly under-explored. In this paper, we investigate the gain of information in quantum measurements from an operational viewpoint in the special case of a two-outcome probabilistic source. We show that the continuous extension of the Shannon entropy naturally admits two distinct measures of information gain, differential information gain and relative information gain, and that these have radically different characteristics. In particular, while differential information gain can increase or decrease as additional data are acquired, relative information gain consistently grows and, moreover, exhibits asymptotic indifference to the data or choice of Bayesian prior. In order to make a principled choice between these measures, we articulate a Principle of Information Increase, which incorporates a proposal due to Summhammer that more data from measurements leads to more knowledge about the system, and also takes into consideration black swan events. This principle favours differential information gain as the more relevant metric and guides the selection of priors for these information measures. Finally, we show that, of the symmetric beta distribution priors, the Jeffreys binomial prior is the prior that ensures maximal robustness of information gain for the particular data sequence obtained in a run of experiments. Full article

DNA methylation is a key epigenetic modification involved in gene regulation, contributing to both physiological and pathological conditions. For a more profound comprehension, it is essential to conduct a precise comparison of DNA methylation patterns between sample groups that represent distinct statuses. Analysis of differentially methylated regions (DMRs) using computational approaches can help uncover the precise relationships between these phenomena. This paper describes a hybrid model that combines the beta-binomial Bayesian hierarchical model with a combination of ranking methods known as HBCR_DMR. During the initial phase, we model the actual methylation proportions of the CpG sites (CpGs) within the replicates. This modeling is achieved through beta-binomial distribution, with parameters set by a group mean and a dispersion parameter. During the second stage, we establish the selection of distinguishing CpG sites based on their methylation status, employing multiple ranking techniques. Finally, we combine the ranking lists of differentially methylated CpG sites through a voting system. Our analyses, encompassing simulations and real data, reveal outstanding performance metrics, including a sensitivity of 0.72, specificity of 0.89, and an F1 score of 0.76, yielding an overall accuracy of 0.82 and an AUC of 0.94. These findings underscore HBCR_DMR’s robust capacity to distinguish methylated regions, confirming its utility as a valuable tool for DNA methylation analysis. Full article

For decades, conventional wisdom maintained that binary 0–1 Bernoulli random variables cannot contain extra-binomial variation. Taking an unorthodox stance, Hilbe actively disagreed, especially for correlated observation instances, arguing that the universally adopted diagnostic Pearson or deviance dispersion statistics are insensitive to a variance anomaly in a binary context, and hence simply fail to detect it. However, having the intuition and insight to sense the existence of this departure from standard mathematical statistical theory, but being unable to effectively isolate it, he classified this particular over-/under-dispersion phenomenon as implicit. This paper explicitly exposes his hidden quantity by demonstrating that the variance in/deflation it represents occurs in an underlying predicted beta random variable whose real number values are rounded to their nearest integers to convert to a Bernoulli random variable, with this discretization masking any materialized extra-Bernoulli variation. In doing so, asymptotics linking the beta-binomial and Bernoulli distributions show another conventional wisdom misconception, namely a mislabeling substitution involving the quasi-Bernoulli random variable; this undeniably is not a quasi-likelihood situation. A public bell pepper disease dataset exhibiting conspicuous spatial autocorrelation furnishes empirical examples illustrating various features of this advocated proposition. Full article