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In 1925, the American entomologist Walter Sidney Abbott proposed an equation for assessing efficacy, and it is still widely used today for analysing controlled experiments in crop protection and phytomedicine. Typically, this equation is applied to each experimental unit and the efficacy estimates thus obtained are then used in analysis of variance and least squares regression procedures. However, particularly regarding the common assumptions of homogeneity of variance and normality, this approach is often inaccurate. In this tutorial paper, we therefore revisit Abbott's equation and outline an alternative route to analysis via generalized linear mixed models that can satisfactorily deal with these distributional issues. Nine examples from entomology, weed science and phytopathology, each with a different focus and methodological peculiarity, are used to illustrate the framework. [ABSTRACT FROM AUTHOR]

AbstractWe present a random effects meta-analytic approach for analyzing exchangeable 2×2×2 tables of COVID-19 deaths classified by two comorbidities, for example, diabetes and hypertension. We take the marginal tables of comorbidities as multinomial with table-specific cell probabilities drawn from a Dirichlet distribution. Conditionally hereon, we model the death counts for cell in the 2×2 table of possible comorbidity combinations as independent binomial random variables. We allow for a randomly drawn normally distributed frailty to model the correlation of the death counts within the same study. For complete tables, this model can be fitted by standard statistical software. We propose an approximate maximum likelihood (ML) imputation procedure by which tables with missing entries can be completed enabling the use of the same standard procedures and giving a better estimate of model parameters than one would get by leaving partial tables out. The properties of the method are illustrated by simulations. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, the potential of using a head-up display (HUD) in the connected environment to improve a vehicle’s running comfort and pedestrian safety is tested, by providing warning information to drivers in advance. To achieve this objective, driving simulation technology is used to construct the connected environment and develop the HUD, and the effectiveness of the system is then tested. Specifically, thirty-four participants were recruited to conduct driving simulation experiments in six scenarios: three warning display types (Baseline/Head-down display/Head-up display) combined with two weather conditions (clear weather/foggy weather). The effects of the three different warning display types on braking risk-avoidance strategy were studied by comparing the drivers’ performance during the perception and decision stage (position of accelerator-pedal release, position of first braking), the risk-avoidance manipulation stage (maximum deceleration, braking distance) and the risk-avoidance result stage (minimum collision distance, position of minimum speed). The influences of weather conditions and driver attributes were also considered. When the HUD warnings were activated, drivers started to decelerate further away from pedestrians, with a more stable and moderate deceleration process and a greater safety margin between the vehicle and the pedestrians. Using HUD warnings in foggy conditions improved drivers’ perception and decision abilities, this study confirmed the great benefits that HUD warnings in the connected environment can bring to traffic safety, especially under risky situations and inclement weathers. The research results provide a reference for the more humanized and rationalized optimization design of these warning systems. [ABSTRACT FROM AUTHOR]

Structural habitats support high biodiversity by providing refuge, forage resources, and recruitment habitat that upwardly influence the broader faunal community structure. However, there are few system-wide studies that empirically measure communities before and after major shifts in habitat structure or availability, limiting our ability to predict the consequences of changes in structural habitat above local scales. We used the collapse of the Apalachicola Bay, Florida oyster population as a natural experiment to assess the impacts of estuary-wide structural habitat loss on the nekton community through long-term faunal monitoring data. Habitat losses of this magnitude are expected to decrease diversity and alter composition, so we expected to observe these changes in Apalachicola Bay after the oyster population collapse. We assessed changes in Gini-Simpson diversity and composition over a 21-year period encompassing the 2012 oyster collapse using generalized linear mixed models to account for confounding drivers. We surprisingly found no evidence that Gini-Simpson diversity or composition differed immediately before or following the collapse, which may be explained if estuarine fauna were able to effectively use other habitats. This suggests that proportional changes in diversity or composition from loss or gain of structural habitats cannot be assumed at the system-wide scale. [ABSTRACT FROM AUTHOR]

Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal–normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analyses of sparse data, which may arise when the event rate is low for binary or count outcomes, pose a challenge to the normal–normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal–normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the $t$ -statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analyses and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal–normal model. The proposed method would give useful guidance to address publication bias in the meta-analysis of sparse data. [ABSTRACT FROM AUTHOR]

Background Problematic drinking frequently co-occurs with depression among young adults, but often remains unaddressed in depression treatment. Evidence is insufficient on whether digital alcohol interventions can be effective in this young comorbid population. In a randomized controlled trial, we examined the effectiveness of Beating the Booze (BtB), an add-on digital alcohol intervention to complement depression treatment for young adults. Methods Participants were randomized to BtB + depression treatment as usual (BTB + TAU, n = 81) or TAU (n = 82). The primary outcome was treatment response, a combined measure for alcohol and depression after 6-month follow-up. Secondary outcomes were number of weekly drinks (Timeline Follow-back) and depressive symptoms (Center for Epidemiologic Studies Depression scale). Treatment response was analyzed using generalized linear modeling and secondary outcomes using robust linear mixed modeling. Results Low treatment response was found due to lower than expected depression remission rates. No statistically significant between-group effect was found for treatment response after 6-month follow-up (odds ratio 2.86, p = 0.089, 95% confidence interval [CI] 0.85–9.63). For our secondary outcomes, statistically significant larger reductions in weekly drinks were found in the intervention group after 3-month (B = −4.00, p = 0.009, 95% CI −6.97 to −1.02, d = 0.27) and 6-month follow-up (B = −3.20, p = 0.032, 95% CI −6.13 to −0.27, d = 0.23). We found no statistically significant between-group differences on depressive symptoms after 3-month (B = −0.57, p = 0.732, 95% CI −3.83 to 2.69) nor after 6-month follow-up (B = −0.44, p = 0.793, 95% CI −3.69 to 2.82). Conclusions The add-on digital alcohol intervention was effective in reducing alcohol use, but not in reducing depressive symptoms and treatment response among young adults with co-occurring depressive disorders and problematic alcohol use. Trial registration: Pre-registered on October 29, 2019 in the Overview of Medical Research in the Netherlands (OMON), formerly the Dutch Trial Register(https://onderzoekmetmensen.nl/en/trial/49219). [ABSTRACT FROM AUTHOR]

This article focuses on rater severity and consistency and their relation to major changes in the rating system in a high-stakes testing context. The study is based on longitudinal data collected from 2009 to 2019 from the second language (L2) Finnish speaking subtest in the National Certificates of Language Proficiency in Finland. We investigated whether rater severity and consistency changed over that period and whether the changes could be explained by major changes in the rating system, such as the change of lead examiner, the modus of rating and training (on-site or remote), and the composition of the rater group. The data consisted of 45 rating sessions with 104 raters and 59,899 examinees and were analysed using the many-facet Rasch model and generalized linear mixed models. The analyses indicated that raters as a group became somewhat more lenient over time. In addition, the results showed that the rater community and its practices, the lead examiners, and the modus of rating and training can influence rating behaviour. Finally, we elaborate on implications for both research and practice. [ABSTRACT FROM AUTHOR]

Lingonberry (Vaccinium vitis-idaea L.) grows in a range of nature types in the boreal zone, and understanding factors affecting the abundance of the plant, as well as mapping its spatial distribution, is important. The abundance of the species can be an indicator of ecosystem changes, and lingonberry can also be a source for commercial utilisation of berry resources. Using country-wide data from 6404 field plots of the Norwegian national forest inventory (NFI), we modelled the relationship between lingonberry cover and airborne laser scanning (ALS) and satellite metrics and bioclimatic variables describing the forest structure, terrain, soil properties and climate using a generalised mixed-effects model with a quasipoisson distribution. The validation carried out with an independent set of 2124 NFI plots indicated no obvious bias in predictions. The most important predictors were found to be interactions between dominant tree species, stand basal area and latitude, as well as the reflectance in the near-infrared band from Sentinel-2 satellite imagery, the dominant height based on the ALS variable and the long-term mean summer (June–August) temperature. The results provide an indicator of the effects of global warming, as well as the possibility of giving forest management prescriptions that favour lingonberry and locating the most abundant lingonberry sites in Norwegian forests.

Profiling analysis aims to evaluate health care providers, including hospitals, nursing homes, or dialysis facilities among others with respect to a patient outcome, such as 30-day unplanned hospital readmission or mortality. Fixed effects (FE) profiling models have been developed over the last decade, motivated by the overall need to (a) improve accurate identification or flagging of under-performing providers, (b) relax assumptions inherent in random effects (RE) profiling models, and (c) take into consideration the unique disease characteristics and care/treatment processes of end-stage kidney disease (ESKD) patients on dialysis. In this paper, we review the current state of FE methodologies and their rationale in the ESKD population and illustrate applications in four key areas: profiling dialysis facilities for (1) patient hospitalizations over time (longitudinally) using standardized dynamic readmission ratio (SDRR), (2) identification of dialysis facility characteristics (e.g., staffing level) that contribute to hospital readmission, and (3) adverse recurrent events using standardized event ratio (SER). Also, we examine the operating characteristics with a focus on FE profiling models. Throughout these areas of applications to the ESKD population, we identify challenges for future research in both methodology and clinical studies.

Knowledge of interspecific and spatiotemporal variation in demography–environment relationships is key for understanding the population dynamics of sympatric species and developing multispecies conservation strategies. We used hierarchical random‐effects models to examine interspecific and spatial variation in annual productivity in six migratory ducks (i.e., American wigeon [Mareca americana], blue‐winged teal [Spatula discors], gadwall [Mareca strepera], green‐winged teal [Anas crecca], mallard [Anas platyrhynchos] and northern pintail [Anas acuta]) across six distinct ecostrata in the Prairie Pothole Region of North America. We tested whether breeding habitat conditions (seasonal pond counts, agricultural intensification, and grassland acreage) or cross‐seasonal effects (indexed by flooded rice acreage in primary wintering areas) better explained variation in the proportion of juveniles captured during late summer banding. The proportion of juveniles (i.e., productivity) was highly variable within species and ecostrata throughout 1961–2019 and generally declined through time in blue‐winged teal, gadwall, mallard, pintail, and wigeon, but there was no support for a trend in green‐winged teal. Productivity in Canadian ecostrata declined with increasing agricultural intensification and increased with increasing pond counts. We also found a strong cross‐seasonal effect, whereby more flooded rice hectares during winter resulted in higher subsequent productivity. Our results suggest highly consistent environmental and anthropogenic effects on waterfowl productivity across species and space. Our study advances our understanding of current year and cross‐seasonal effects on duck productivity across a suite of species and at finer spatial scales, which could help managers better target working‐lands conservation programs on both breeding and wintering areas. We encourage other researchers to evaluate environmental drivers of population dynamics among species in a single modeling framework for a deeper understanding of whether conservation plans should be generalized or customized given limited financial resources. [ABSTRACT FROM AUTHOR]

Pre-hypertension (Pre-HT) is a significantly risk progress factor to hypertension which contributes to the development of cardiovascular disease that leaded cause of death. The purpose of this research was to identify work environments, health promotion practices, and other characteristics associated with Pre-HT among adults aged 35 - 59 years. This cross-sectional research was performed from January to March of 2023 with 1,202 participants aged between 35 and 59 years chosen by a multistage random sampling. Data was through interviews utilizing a reliable structured questionnaire. The data analysis encompassed the use of descriptive statistics, and a generalized linear mixed model (GLMM) was executed to ascertain the adjusted odds ratio (Adj. OR) and 95% CI for the presented findings. The participants, 26.29% were between the ages of 40 and 41, and 66.97% were female. The overall prevalence of Pre-HT was 73.55% (95% CI: 70.98 - 75.97). Results of the multivariable analysis revealed a strong association between Pre-HT and health promotion behavior (HPB) (Adj. OR = 1.84, 95% CI: 1.31 - 2.57, P-value < 0.001) work environment (Adj. OR = 2.07, 95% CI: 1.32 - 3.26, P-value = 0.006), gender (Adj. OR = 2.60, 95% CI: 1.89 - 3.57, P-value < 0.001), Age (Adj. OR = 1.70, 95% CI: 1.08 - 2.65, P-value = 0.050), history of smoking (Adj. OR = 2.60, 95% CI: 1.89 - 3.57, P-value < 0.001), Body mass index (Adj. OR = 2.09, 95% CI: 1.54 - 2.83, P-value < 0.001). Poor health promotion increased Pre-HT risk. Relevant authorities must evaluate group context to build a suitable health promotion model. [ABSTRACT FROM AUTHOR]

The objective of this study was to identify the relationship between hospitalization treatment strategies leading to change in symptoms during 12-week follow-up among hospitalized patients during the COVID-19 outbreak. In this article, data from a prospective cohort study on COVID-19 patients admitted to Khorshid Hospital, Isfahan, Iran, from February 2020 to February 2021, were analyzed and reported. Patient characteristics, including socio-demographics, comorbidities, signs and symptoms, and treatments during hospitalization, were investigated. Also, to investigate the treatment effects adjusted by other confounding factors that lead to symptom change during follow-up, the binary classification trees, generalized linear mixed model, machine learning, and joint generalized estimating equation methods were applied. This research scrutinized the effects of various medications on COVID-19 patients in a prospective hospital-based cohort study, and found that heparin, methylprednisolone, ceftriaxone, and hydroxychloroquine were the most frequently prescribed medications. The results indicate that of patients under 65 years of age, 76% had a cough at the time of admission, while of patients with Cr levels of 1.1 or more, 80% had not lost weight at the time of admission. The results of fitted models showed that, during the follow-up, women are more likely to have shortness of breath (OR = 1.25; P-value: 0.039), fatigue (OR = 1.31; P-value: 0.013) and cough (OR = 1.29; P-value: 0.019) compared to men. Additionally, patients with symptoms of chest pain, fatigue and decreased appetite during admission are at a higher risk of experiencing fatigue during follow-up. Each day increase in the duration of ceftriaxone multiplies the odds of shortness of breath by 1.15 (P-value: 0.012). With each passing week, the odds of losing weight increase by 1.41 (P-value: 0.038), while the odds of shortness of breath and cough decrease by 0.84 (P-value: 0.005) and 0.56 (P-value: 0.000), respectively. In addition, each day increase in the duration of meropenem or methylprednisolone decreased the odds of weight loss at follow-up by 0.88 (P-value: 0.026) and 0.91 (P-value: 0.023), respectively (among those who took these medications). Identified prognostic factors can help clinicians and policymakers adapt management strategies for patients in any pandemic like COVID-19, which ultimately leads to better hospital decision-making and improved patient quality of life outcomes. [ABSTRACT FROM AUTHOR]

In this article, we extend Liao's test for across-group comparisons of the fixed effects from the generalized linear model to the fixed and random effects of the generalized linear mixed model (GLMM). Using as our basis the Wald statistic, we developed an asymptotic test statistic for across-group comparisons of these effects. The test can be applied when the fixed and random effects are multivariate normally distributed, and it works well for any link function and conditional distribution of the dependent variable of the GLMM. We also derived the asymptotic properties of this test, and because power information does not exist for either our new test statistic or Liao's test, we implemented a power study to demonstrate the superiority of these tests over the alternatively proposed F test. Using an example, we show the application of the test and then discuss its possible restrictions with respect to the distribution of the random effects. [ABSTRACT FROM AUTHOR]

Various privacy-preserving frameworks that respect the individual’s privacy in the analysis of data have been developed in recent years. However, available model classes such as simple statistics or generalized linear models lack the flexibility required for a good approximation of the underlying data-generating process in practice. In this paper, we propose an algorithm for a distributed, privacy-preserving, and lossless estimation of generalized additive mixed models (GAMM) using component-wise gradient boosting (CWB). Making use of CWB allows us to reframe the GAMM estimation as a distributed fitting of base learners using the L 2 -loss. In order to account for the heterogeneity of different data location sites, we propose a distributed version of a row-wise tensor product that allows the computation of site-specific (smooth) effects. Our adaption of CWB preserves all the important properties of the original algorithm, such as an unbiased feature selection and the feasibility to fit models in high-dimensional feature spaces, and yields equivalent model estimates as CWB on pooled data. Next to a derivation of the equivalence of both algorithms, we also showcase the efficacy of our algorithm on a distributed heart disease data set and compare it with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Generalized linear mixed models are commonly used to describe relationships between correlated responses and covariates in medical research. In this paper, we propose a simple and easily implementable regularized estimation approach to select both fixed and random effects in generalized linear mixed model. Specifically, we propose to construct and optimize the objective functions using the confidence distributions of model parameters, as opposed to using the observed data likelihood functions, to perform effect selections. Two estimation methods are developed. The first one is to use the joint confidence distribution of model parameters to perform simultaneous fixed and random effect selections. The second method is to use the marginal confidence distributions of model parameters to perform the selections of fixed and random effects separately. With a proper choice of regularization parameters in the adaptive LASSO framework, we show the consistency and oracle properties of the proposed regularized estimators. Simulation studies have been conducted to assess the performance of the proposed estimators and demonstrate computational efficiency. Our method has also been applied to two longitudinal cancer studies to identify demographic and clinical factors associated with patient health outcomes after cancer therapies. [ABSTRACT FROM AUTHOR]

Abstract Background Stunting among children under 5 years of age remains a worldwide concern, with 148.1 million (22.3%) stunted in 2022. The recent 2019/2020 Rwanda Demographic Health Survey (RDHS) revealed that the prevalence of stunting in Rwanda among under five children was 33.5%. In Rwanda, there is no sufficient evidence on stunting status to guide prioritized interventions at the sector level, the lowest administrative unit for implementing development initiatives. This study aimed to provide reliable estimates of stunting prevalence in Rwanda at the sector level. Methods In this article, Small Area Estimation (SAE) techniques were used to provide sector level estimates of stunting prevalence in children under five in Rwanda. By plugging in relevant significant covariates in the generalized linear mixed model, model-based estimates are produced for all sectors with their corresponding Mean Square Error (MSE). Results The findings showed that, overall, 40 out of 416 sectors had met the national target of having a stunting rate less than or equal to 19%, while 194 sectors were far from meeting this target, having a stunting rate higher than the national prevalence of 33.5% in the year 2020. The majority of the sectors with stunting prevalence that were higher than the national average of 33.5% were found in the Northern Province with 68 sectors out of 89 and in Western Province with 64 sectors out of 96. In contrast, the prevalence of stunting was lower in the City of Kigali where 14 out of 35 sectors had a stunting rate between 0 and 19%, and all sectors were below the national average. This study showed a substantial connection between stunting and factors such as household size, place of residence, the gender of the household head, and access to improved toilet facilities and clean water. Conclusion The results of this study may guide and support informed policy decisions and promote localised and targeted interventions in Rwanda’s most severely affected sectors with a high stunting prevalence in Rwanda.

The coronavirus disease 2019 (COVID-19) pandemic was defined by the World Health Organization (WHO) as a global epidemic on March 11, 2020, as the infectious disease that threatens public health fatally. In this study, the main aim is to model the impact of various air pollution causes on mortality data due to the COVID-19 pandemic by Generalized Linear Mixed Model (GLMM) approach to make global statistical inferences about 174 WHO member countries as subjects in the six WHO regions. “Total number of deaths by these countries due to the COVID-19 pandemic” until July 27, 2022, is taken as the response variable. The explanatory variables are taken as the WHO regions, the number of deaths from air pollution causes per 100.000 population as “household air pollution from solid fuels,” “ambient particulate matter pollution,” and “ambient ozone pollution.” In this study, Poisson, geometric, and negative binomial (NB) regression models with “country” taken as fixed and random effects, as special cases of GLMM, are fitted to model the response variable in the aspect of the above-mentioned explanatory variables. In the Poisson, geometric, and NB regression models, Iteratively Reweighted Least Squares parameter estimation method with the Fisher-Scoring iterative algorithm under the log-link function as canonical link function is used. In the GLMM approach, Laplace approximation is also used in the prediction of random effects. In this study, six different Poisson, geometric, and NB regression models with fixed and random effects are established for 174 countries all over the world to make global statistical inferences for investigating the relationships between “total number of deaths” by these countries due to the COVID-19 pandemic and “air pollution causes.” As a result of this study, “NB mixed-effects regression model” as the most appropriate GLMM is used to make global statistical inferences about the impact of the various air pollution causes on the mortality data due to the COVID-19 pandemic.

Understanding the relationships between species and their pattern of coexistence is essential in conservation planning. Rüppell's fox and sand cat are two poorly known species of desert areas that coexist in most parts of the desert belt of Africa and the Middle East. However, their habitat selection in many parts of their distribution are unknown. In this research, using the data collected from a three-year monitoring plan, we investigated the relationship between the den sites of Rüppell's fox and sand cat and habitat variables based on the generalized linear mixed model (GLMM) method. The results showed that for Rüppell's foxes, only the density of rodent burrows had a significant and positive effect. In the sand cat model, in addition to prey, vegetation density (P-value < 0.05) and vegetation height (P-value < 0.10) had significantly positive effects and elevation had a significantly negative effect (P-value < 0.10). We found that the sand cat is more specialized in habitat selection and depends more on the habitat cover. While the Rüppell's fox chose a more diverse range of soil and vegetation classes, the sand cat selected its den sites exclusively in sandy loam soils with the predominant cover of Haloxylon spp. and Caligonum spp. Acting as a generalist species, Rüppell's fox displayed a more pronounced response to human presence and selected dens with a broader view of their surroundings. These findings enhance our understanding of mesocarnivore coexistence in desert regions and provide valuable insights for the conservation planning of these species.

Background: Stunting among children under 5 years of age remains a worldwide concern, with 148.1 million (22.3%) stunted in 2022. The recent 2019/2020 Rwanda Demographic Health Survey (RDHS) revealed that the prevalence of stunting in Rwanda among under five children was 33.5%. In Rwanda, there is no sufficient evidence on stunting status to guide prioritized interventions at the sector level, the lowest administrative unit for implementing development initiatives. This study aimed to provide reliable estimates of stunting prevalence in Rwanda at the sector level. Methods: In this article, Small Area Estimation (SAE) techniques were used to provide sector level estimates of stunting prevalence in children under five in Rwanda. By plugging in relevant significant covariates in the generalized linear mixed model, model-based estimates are produced for all sectors with their corresponding Mean Square Error (MSE). Results: The findings showed that, overall, 40 out of 416 sectors had met the national target of having a stunting rate less than or equal to 19%, while 194 sectors were far from meeting this target, having a stunting rate higher than the national prevalence of 33.5% in the year 2020. The majority of the sectors with stunting prevalence that were higher than the national average of 33.5% were found in the Northern Province with 68 sectors out of 89 and in Western Province with 64 sectors out of 96. In contrast, the prevalence of stunting was lower in the City of Kigali where 14 out of 35 sectors had a stunting rate between 0 and 19%, and all sectors were below the national average. This study showed a substantial connection between stunting and factors such as household size, place of residence, the gender of the household head, and access to improved toilet facilities and clean water. Conclusion: The results of this study may guide and support informed policy decisions and promote localised and targeted interventions in Rwanda's most severely affected sectors with a high stunting prevalence in Rwanda. [ABSTRACT FROM AUTHOR]

We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome‐wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo‐likelihood approach. Our Bayesian approach assumes a flat prior for the fixed effects and includes both approximate reference prior and half‐Cauchy prior choices for the variances of random effects. Since the flat prior on the fixed effects is improper, we develop a fractional Bayes factor approach to obtain posterior probabilities of the several competing models. Simulation studies with Poisson GLMMs with spatial random effects and overdispersion random effects show that our approach performs favorably when compared to widely used competing Bayesian methods including deviance information criterion and Watanabe–Akaike information criterion. We illustrate the usefulness and flexibility of our approach with three case studies including a Poisson longitudinal model, a Poisson spatial model, and a logistic mixed model. Our proposed approach is implemented in the R package GLMMselect that is available on CRAN. [ABSTRACT FROM AUTHOR]

Cluster randomized trials (CRTs) refer to a popular class of experiments in which randomization is carried out at the group level. While methods have been developed for planning CRTs to study the average treatment effect, and more recently, to study the heterogeneous treatment effect, the development for the latter objective has currently been limited to a continuous outcome. Despite the prevalence of binary outcomes in CRTs, determining the necessary sample size and statistical power for detecting differential treatment effects in CRTs with a binary outcome remain unclear. To address this methodological gap, we develop sample size procedures for testing treatment effect heterogeneity in two‐level CRTs under a generalized linear mixed model. Closed‐form sample size expressions are derived for a binary effect modifier, and in addition, a computationally efficient Monte Carlo approach is developed for a continuous effect modifier. Extensions to multiple effect modifiers are also discussed. We conduct simulations to examine the accuracy of the proposed sample size methods. We present several numerical illustrations to elucidate features of the proposed formulas and to compare our method to the approximate sample size calculation under a linear mixed model. Finally, we use data from the Strategies and Opportunities to Stop Colon Cancer in Priority Populations (STOP CRC) CRT to illustrate the proposed sample size procedure for testing treatment effect heterogeneity. [ABSTRACT FROM AUTHOR]

Simple Summary: Treating patients for metastatic prostate cancer based on the information of PSMA-PET bears the risk to "under- or overtreat" patients, given that many lesions seen only on PSMA-PET but not on conventional imaging (CI) could alter their management. It is not possible to predict the disease status using CI with PSMA-PET/CT, because bone lesions can be positive on bone scintigraphy (BS), without evidence of the disease on CT. Some authors suggested using clinical parameters to predict BS results, but this does not reach enough accuracy to adjust the therapy. If an algorithm based on PSMA-PET/CT data were able to predict if lesions are visible on BS, this might be a possible way to adjust patient management based on CI-based guidelines in light of PSMA-PET/CT. Therefore, we aimed to develop a model to predict the visibility of bone lesions on BS based on PSMA-PET/CT data. Objective: The increasing use of PSMA-PET/CT for restaging prostate cancer (PCa) leads to a patient shift from a non-metastatic situation based on conventional imaging (CI) to a metastatic situation. Since established therapeutic pathways have been designed according to CI, it is unclear how this should be translated to the PSMA-PET/CT results. This study aimed to investigate whether PSMA-PET/CT and clinical parameters could predict the visibility of PSMA-positive lesions on a bone scan (BS). Methods: In four different centers, all PCa patients with BS and PSMA-PET/CT within 6 months without any change in therapy or significant disease progression were retrospectively selected. Up to 10 non-confluent clear bone metastases were selected per PSMA-PET/CT and SUVmax, SUVmean, PSMAtot, PSMAvol, density, diameter on CT, and presence of cortical erosion were collected. Clinical variables (age, PSA, Gleason Score) were also considered. Two experienced double-board physicians decided whether a bone metastasis was visible on the BS, with a consensus readout for discordant findings. For predictive performance, a random forest was fit on all available predictors, and its accuracy was assessed using 10-fold cross-validation performed 10 times. Results: A total of 43 patients were identified with 222 bone lesions on PSMA-PET/CT. A total of 129 (58.1%) lesions were visible on the BS. In the univariate analysis, all PSMA-PET/CT parameters were significantly associated with the visibility on the BS (p < 0.001). The random forest reached a mean accuracy of 77.6% in a 10-fold cross-validation. Conclusions: These preliminary results indicate that there might be a way to predict the BS results based on PSMA-PET/CT, potentially improving the comparability between both examinations and supporting decisions for therapy selection. [ABSTRACT FROM AUTHOR]

Freezing temperatures in fall, winter, and spring can cause damage to multiple perennial fruit crops including northern highbush blueberry (Vaccinium corymbosum). Predictive modeling for lethal temperatures allows producers to make informed decisions about freeze mitigation practices but is lacking for northern highbush blueberry grown in the Pacific Northwest. If buds are hardier than air temperatures, unnecessary use of propane heaters and/or wind machines is costly. In contrast, use of heaters and/or wind machines during freezing, damaging temperatures can minimize crop damage and potential yield loss. The objective of this study was to model cold hardiness across multiple cultivars of northern highbush blueberry grown in various regions in Washington, USA, and to generate predictive cold hardiness models that producers in the Pacific Northwest could use to inform freeze mitigation. Multiple years of experimental cold hardiness data were collected on four cultivars of northern highbush blueberry grown in western and eastern Washington, USA. Freeze chambers were used to reduce bud temperatures systematically, after which buds were dissected and bud survival was assessed. A generalized linear mixed model with a binomial response and logit link was fit to each cultivar to characterize the relationship between bud survival, freezer temperature, recent air temperatures, and growing degree days from fall acclimation to late winter/spring deacclimation. Model simulation was performed to obtain marginal-scale lethal temperature estimates. Model error estimation was performed using cross validation. Results show cultivar-specific cold hardiness models can be generated, and model development and use can help growers make more informed decisions regarding freeze protection that also minimizes costly applications of freeze protection when unnecessary. Furthermore, such models can be adapted to other blueberry growing regions and cultivars experiencing similar climactic conditions. [ABSTRACT FROM AUTHOR]

The generalized linear mixed model for binary outcomes with the probit link function is used in many fields but has a computationally challenging likelihood when there are many random effects. We extend a previously used importance sampler, making it much faster in the context of estimating heritability and related effects from family data by adding a gradient and a Hessian approximation and making a faster implementation. Additionally, a graph-based method is suggested to simplify the likelihood when there are thousands of individuals in each family. Simulation studies show that the resulting method is orders of magnitude faster, has a negligible efficiency loss, and confidence intervals with nominal coverage. We also analyze data from a large study of obsessive-compulsive disorder based on Swedish multi-generational data. In this analysis, the proposed method yielded similar results to a previous analysis, but was much faster. for this article are available online. [ABSTRACT FROM AUTHOR]

Clustered count data, common in health-related research, are routinely analyzed using generalized linear mixed models. There are two well-known challenges in small-sample inference in mixed modeling: bias in the naïve standard error approximation for the empirical best linear unbiased estimator, and lack of clearly defined denominator degrees of freedom. The Kenward–Roger method was designed to address these issues in linear mixed modeling, but neither it nor the simpler option of using between-within denominator degrees of freedom has been thoroughly examined in generalized linear mixed modeling. We compared the Kenward–Roger and between-within methods in two simulation studies. For simulated cluster-randomized trial data, coverage rates for both methods were generally close to the nominal 95% level and never outside 93-97%, even for 5 clusters with an average of 3 observations each. For autocorrelated longitudinal data, between-within intervals were more accurate overall, and under some conditions both the original and improved Kenward–Roger methods behaved erratically. Overall, coverage for Kenward–Roger and between-within intervals was generally adequate, if often conservative. Based on the scenarios examined here, use of between-within degrees of freedom may be a suitable or even preferable alternative to the Kenward–Roger method in some analyses of clustered count data with simple covariance structures. [ABSTRACT FROM AUTHOR]

Abstract Background The effect of urbanization on the morbidity of hepatitis A remains unclear. We aimed to estimate the association between various urbanization-related indices and hepatitis A morbidity in China. Methods Data on the annual morbidity of hepatitis A, urbanization-related measures (i.e., gross domestic product per capita, the number of hospitalization beds per 1000 persons, illiteracy rate, tap water coverage, motor vehicles per 100 persons, population density, and the proportion of arable land), and meteorological factors in 31 provincial-level administrative divisions of Chinese mainland during 2005–2018 were collected from the National Population and Health Science Data Sharing Platform, China Statistical Yearbooks, and the China Meteorological Data Sharing Service System, respectively. Generalized linear mixed models were applied to quantify the impacts of different urbanization-related indices on the morbidity of hepatitis A in China after adjusting for covariates. Results A total of 537,466 hepatitis A cases were reported in China during 2005–2018. The annual morbidity had a decline of 79.4% from 5.64 cases to 1.16 cases per 100,000 people. There were obvious spatial variations with higher morbidity in western China. Nationally, gross domestic product per capita and the number of hospitalization beds per 1000 persons increased from 14,040 to 64,644 CNY and from 2.45 to 6.03 during 2005–2018, respectively. The illiteracy rate decreased from 11.0 to 4.9%. Gross domestic product per capita [relative risk (RR) = 0.96, 95% confidence interval (CI): 0.92–0.99], and the number of hospitalization beds per 1000 persons (RR = 0.79, 95% CI: 0.75–0.83) were associated with the declined morbidity of hepatitis A. By contrast, the increased morbidity of hepatitis A was linked to the illiteracy rate (RR = 1.04, 95% CI: 1.02–1.06). Similar influential factors were detected for children and adults, with greater effects witnessed for children. Conclusions People in the western region suffered the heaviest burden of hepatitis A in Chinese mainland. Nationally, there was a sharp decline in the morbidity of hepatitis A. The urbanization process was associated with the reduction of hepatitis A morbidity in China during 2005–2018. Graphical Abstract

Objective To evaluate the relationship between local rainfall and frailty in the elderly. MethodsThe information on 10 424 elderly aged 65 years and above were extracted from four rounds of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) conducted between 2005 and 2014 across China. Frailty index (FI) was used to assess frail status of the elderly; generalized linear mixed model and natural cubic spline function were adopted to explore the association of local rainfall with the prevalence of frailty. Results The baseline prevalence of frailty was 18.6% for all the elderly aged 82.84 ± 11.30 years at baseline survey; at the last follow-up, the frailty prevalence increased to 27.8%. The results of multivariate regression analysis showed that the elderly living in regions with higher annual average local rainfall (highest quartile) had a lower risk of frailty (odds ratio = 0.60, 95% confidence interval: 0.50 – 0.71) compared to those living in regions with lower local rainfall (lowest quartile); while the correlation of local rainfall with prefrailty of the elderly was not significant. Conclusion Local rainfall has a protective effect on the frailty of the elderly and specific concern should be paid when developing relevant healthcare strategies for the elderly in regions with different amount of rainfall.

IntroductionMenstruation-related symptoms (MRSs) significantly impact women's health and contribute to economic burdens worldwide. Current interventions, primarily pharmacological ones, have limitations and side effects that underscore the need for alternative management strategies. This study explores the association between MRSs and lifestyle factors, specifically physical activity and sleep timing across menstrual cycle phases, to inform non-pharmacological intervention development.MethodsFourteen female students from Ritsumeikan University, Japan, with regular menstrual cycles (25–38 days), not on hormonal treatment or engaged in shift work, participated in this observational study. Using a Fitbit Inspire 2, total daily energy expenditure (TDEE) and sleep timing were monitored over a complete cycle. Menstrual cycle phases were defined based on ovulation day, predicted using home luteinizing hormone tests. Participants completed daily electronic questionnaires rating MRSs using a modified menstrual distress questionnaire. Data were analyzed using a generalized linear mixed model with a gamma distribution and logarithmic link function, examining the relationship of TDEE and the midpoint of sleep time (MS time) with MRS severity.Results and discussionThe following observations were noted: first, MRS severity, except for behavioral change symptoms, significantly increased during the menstrual and luteal phases compared to the follicular phase. Second, delayed MS time was associated with reduced pain, concentration symptoms, water retention, and negative affect during the menstrual phase and reduced negative affect during the luteal phase. Finally, an increase in TDEE was associated with reduced concentration symptoms, autonomic reaction symptoms, and negative affect during the menstrual and luteal phases and reduced water retention only during the luteal phase. This study provides insights into the relationship between MRSs and TDEE/MS time, suggesting potential non-therapeutic approaches for symptom management, though further research is needed to substantiate these findings for practical applications.

Understanding the spatial structure of fishing resource stocks is a fundamental aspect for the management and development of sustainable and productive fisheries. However, this aspect is usually simplified under the assumption of a stock unit, which assumes biologically similar and spatially homogeneous individuals, regardless of the real ranges and characteristics of their distribution. The persistence of geographic patterns in the biological traits of species suggests the spatial structuring of a population, a highly relevant aspect in stock assessment, calculation of Biological Reference Points (BRP), and management of fishing resources. In this work, the spatial heterogeneity of the biological traits of common hake (Merluccius gayi) from off-central Chile is modeled. Considering four reference zones, biological data collected over 26 years of monitoring fisheries and acoustic surveys is analyzed. The findings allow to biologically differentiate these areas whose main characteristics are the increase from north to south in the condition factor, gonad weight, and size at maturity. The Spawning Potential Ratio (SPR), fishing mortality, and BRP are calculated. Exploratorily, if these areas were considered closed subpopulations, results show that overexploitation is not a characteristic of all hake fishing areas, and that not considering the heterogeneity of biological traits causes the overestimation of the population condition in the main fishing areas. The aim of this research was to understand the spatial structuring of common hake based on the heterogeneity of biological attributes and its impact for fisheries management purposes.

In the past 30 years, the global influence of the FIFA World Cup has continued to grow. According to statistics, the final match of the 2022 World Cup in Qatar attracted an audience of over 3 billion. Nowadays, nations (and regions) emphasize the correlation between overall social progress, football tradition, and national sporting prowess. This study aims to comprehend the dynamics of international football development and secure a competitive edge in the global arena. The study collected panel data from 68 countries (regions) participating in the final stage of the World Cup from 1994 to 2022. It modelled them using a generalized linear mixed model (GLMM) to reveal the macro determinants of success and winning patterns in international football performance. The findings show that (1) football tradition's “characteristic values” (e.g. World Cup host, the experience of hosting the World Cup and the number of all-time World Cup winners) and national comprehensive sporting strength (as represented by the Olympic Games results) have significantly contributed to the national team's World Cup performance; (2) the country's Human Development Index (HDI) has a significant negative impact on World Cup performance; (3) the history of football participation (e.g. the number of years of membership in the Intercontinental Football Association (IFA), history of professional leagues) will have little impact on World Cup performance; (4) two interactive variables: population scale × national comprehensive sporting strength (GPOP × CSOGMedals) and economic level × football tradition (lnGDP × PLHistory), have a negative and positive combined effect on World Cup performance, respectively.

Abstract Artificial neural networks (NNs) are a machine learning algorithm that have been used as a convenient alternative of conventional statistical models, such as regression in prediction and classification because of their capability of modeling complex relationships between dependent and independent variables without a priori assumptions about the model form and variable distributions. However, traditional NNs cannot incorporate dependencies of data with a clustering or nesting structure involved in longitudinal studies and cluster sampling. This research is intended to fill this literature gap by integrating the random-effects structure into NNs to account for within-cluster correlations. The proposed NN method incorporating random effects (NNRE) is trained by minimizing the cost function using the backpropagation algorithm combined with the quasi-Newton and gradient descent algorithms. Model overfitting is controlled by using the L 2 regularization method. The trained NNRE model is evaluated for prediction accuracy by using the leaving-one-out cross-validation for both simulated and real data. Prediction accuracy is compared between NNRE and two existing models, the conventional generalized linear mixed model (GLIMMIX) and the generalized neural network mixed model (GNMM), using simulations and real data. Results show that the proposed NNRE results in higher accuracy than both the GLIMMIX and GNMM. [ABSTRACT FROM AUTHOR]

We explore a hierarchical generalized latent factor model for discrete and bounded response variables and in particular, binomial responses. Specifically, we develop a novel two‐step estimation procedure and the corresponding statistical inference that is computationally efficient and scalable for the high dimension in terms of both the number of subjects and the number of features per subject. We also establish the validity of the estimation procedure, particularly the asymptotic properties of the estimated effect size and the latent structure, as well as the estimated number of latent factors. The results are corroborated by a simulation study and for illustration, the proposed methodology is applied to analyze a dataset in a gene–environment association study. [ABSTRACT FROM AUTHOR]

Generalized linear models and generalized linear mixed models (GLMMs) are fundamental tools for predictive analyses. In insurance, GLMMs are particularly important, because they provide not only a tool for prediction but also a theoretical justification for setting premiums. Although thousands of resources are available for introducing GLMMs as a classical and fundamental tool in statistical analysis, few resources seem to be available for the insurance industry. This study targets insurance professionals already familiar with basic actuarial mathematics and explains GLMMs and their linkage with classical actuarial pricing tools, such as the B¨uhlmann premium method. Focus of the study is mainly on the modeling aspect of GLMMs and their application to pricing, while avoiding technical issues related to statistical estimation, which can be automatically handled by most statistical software. [ABSTRACT FROM AUTHOR]

The current paper aims to explore the drivers of the implementation of e-commerce and social commerce among Italian virgin olive oil micro-companies (10 employees or less), according to the Technology-Organization-Environment framework. In order to observe this phenomenon over time, a Generalized Linear Mixed Model was used for the analysis, because it allows considering the heterogeneity characteristic of such types of companies. The results show that technological change is influencing the drivers toward e-commerce adoption and that the size is not as important as it seems. The implementation of new technological sales channels could allow not only to broaden micro firms' sales horizons but also to overcome significant geographical barriers, enabling expansion toward otherwise inaccessible international markets. It is even more critical considering that the current pandemic situation is forcing the transition from direct/physical to online sales. [ABSTRACT FROM AUTHOR]

Chance models of scientific creative productivity allow estimation of researcher capacity. One prominent such model is the Q model in which the impact of a scholarly work is modeled as a multiplicative function of researcher capacity and a potential impact (i.e., luck) parameter. Previous work estimated researcher capacity based on an approximation of the Q parameter. In this work, however, I outline how the Q model can be estimated within the framework of generalized linear mixed models. This way estimates of researcher capacity (and all other parameters of the Q model) are readily available and obtained by standard statistical software packages. Usage of such software further allows comparing different distributional assumptions and calculation of reliability of the Q parameter (i.e., researcher capacity). This is illustrated for a large dataset of multidisciplinary scientists (N = 20,296). The Poisson Q model was found to have negligibly better predictive accuracy than the original normal Q model. Reliability estimates revealed excellent reliability of Q estimates with conditional reliability being mostly in acceptable ranges. Reliability of Q parameter estimates further depended heavily on the number of publications of a scientist with reliability increasing with the number of papers. The future and limitations of the Q model in the context of researcher capacity estimation are thoroughly discussed. [ABSTRACT FROM AUTHOR]

Abstract Spatio‐temporal datasets that are difficult to analyse are commonly derived from ecological surveys. There are software packages available to analyse these datasets, but many of them require advanced coding skills. There is a growing need for easy‐to‐use packages that researchers can use to analyse common ecological datasets. We develop a particular generalized linear mixed model framework for spatio‐temporal point‐referenced data that is flexible enough to accommodate data from most ecological surveys while being structured enough to facilitate analyses without advanced coding. Our implementation in the starve package uses a computationally efficient version of a nearest‐neighbour Gaussian process enabling analysis of relatively large datasets. A tutorial analysis of a Carolina wren survey presents a recommended workflow for analyses while showcasing the capabilities of the package. Our model and package are tools that can easily be added to researchers' routine to help make sense of data from ecological surveys. We emphasize the ability of our model to create fine‐scale spatio‐temporal predictions which can then be used to visualize and identify important trends in species distributions.

Ayitenew Agegn Gwadu,1 Mengistu Abebe Tegegne,1 Kidist Belay Mihretu,1 Awoke Seyoum Tegegne2 1Departments of Statistics, Dire Dawa University, Dire Dawa, Ethiopia; 2Department of Statistics, Bahir Dar University, Bahir Dar, EthiopiaCorrespondence: Awoke Seyoum Tegegne, Email bisrategebrail@yahoo.comBackground: HIV attacks the CD4 cells which are responsible for the body’s immune response to infectious agents. The main objective of this study was to identify predictors of viral load status over time among HIV patients under HAART in Zewditu Memorial Hospital.Methods: A retrospective institutional-based cohort study design was conducted on 161 HIV-infected adults under HAART whose follow-ups were from January 2014 up to December 2017. A generalized linear mixed-effects model was conducted to infer predictors of the status of viral load at 95% of CI).Results: The descriptive statistics revealed that about 55.9% of the adults under treatment had a detected viral load status. Among the potential predictors, visiting time of patients (AOR = 0.731, 95%: (0.634,0.842) and p-value < 0.01), age of patients (AOR = 1.0666, 95% CI: (1.0527,1.0917) and p-value < 0.01), weight (AOR=. 0.904, 95% CI: (0.862, 0.946) and p-value < 0.01), baseline CD4 cell count (AOR = 0.996, 95% CI: (0.994, 0.998) and P-value < 0.01), educated patients (AOR = 0.030, 95% CI: (0.002, 0.385) and p-value=0.0053), rural patients (AOR = 6.30,95% CL: (1.78, 2.25) and p-value=0.0043), working status patients (AOR = 0.5905, 95% CI: (0.547,0.638), p-value < 0.01), poor adherent patients (AOR = 1.120, 95% CI; (1.035,1.391) and p-value = 0.016) and patients disclosed the disease status (AOR = 0.195, 95% CI: (0.023, 0.818) and p-value=0.0134) significantly affected the detection status of viral loads, keeping all other covariates constant.Conclusion: The predictor variables; visiting times, the weight of patients, residence area, age of patients, educational level, clinical stages, functional status, baseline CD4 cell count, adherence status, and disclosure status of the disease statistically and significantly affected the status of viral load. Hence, health-related education should be given for patients to disclose their disease status, to be good adherents based on the prescription given to the health staff. Due attentions should be given for rural and uneducated patients. Attention should be forwarded to for non-adherent patients to follow the instruction given by the health staff.&#x00A0Keywords: logistic regression, HAART, retrospective study, generalized linear mixed model, odds ratio

The aim of the current study is to analyze the effects of red and yellow cards on the scoring rate in elite soccer. The sample was composed of 1826 matches in the top five European leagues. All events were structured in 5-min intervals and were analyzed by means of a Generalized Linear Mixed Model with Poisson distribution, considering the presence of correlated data, where the dependent variable is represented by scoring rate. Team strength and home advantage were considered implicitly by means of a transformation of the betting odds for each game. The model also took into account the goal difference and time evolution. Overall, we found that after a sending off, each team's scoring rate changes significantly, damaging the penalised team and favouring its opponent. When the player who is sent off belongs to the Away team, the impact of a red card is more or less maintained over time intervals. The red card effect, on the other hand, tends to fade over time when the affected team is stronger. The relative difference in scoring rates is also affected by the goal difference and the difference in booked players, being slightly lower for the team going ahead if it has more booked players. Our approach allows estimating the expected cumulative soring rate through time for various red card scenarios. Particularly if a red card is given with 30 min of remaining time, the expected impact is 0.39 goals if the guilty player is on the visiting team and 0.50 if he plays for the home team. Coaches and analysts could use this information to establish objectives for players and teams in training and matches and to be prepared for these very different scenarios of numerical superiority or inferiority. [ABSTRACT FROM AUTHOR]

Mixed pixels and spectral variations commonly occur in images of karst areas because these areas have rugged topography and high surface heterogeneity. Consequently, the bedrock exposure rate cannot be accurately estimated using the existing rock index method based on remote sensing and the fixed endmember mixed pixel decomposition model. In order to solve this problem, using LANDSAT operational land imager/thematic mapper (OLI/TM) images as the data source, this study estimated the bedrock exposure rate based on a generalized linear mixed model (GLMM) considering spectral variability. In addition, this approach was compared with existing commonly used methods for estimating bedrock exposure rate. The results show that: (1) GLMM showed the highest performance in estimating bedrock exposure rate, with a total accuracy of 88.05% and a kappa coefficient of 0.845, and the accuracy exceeded 70% for different levels of bedrock exposure rate. However, the total accuracy of other commonly used bedrock exposure methods is was below 66%, being higher only in areas with bedrock exposure rates less than 20% and more than 70%. (2) By applying each method to different terrain scenes, GLMM was found to be more stable than other methods, and the estimated bedrock exposure rate is the closest to the ground reference value with a root mean square error less than 0.093. Other methods, including fully constrained least squares unmixing (FCLSU) and karst bare‐rock index (KBRI), have a certain gap in different scenes, and their accuracy is not high. (3) Without any ground reference data, the accuracy of the direct unmixing result of GLMM is very close to that calculated by the regression model, and the accuracy difference among different grades of bedrock exposure rate is less than 5%, thus outperforming the other methods. GLMM can effectively estimate the bedrock exposure rate at different times using different data. Therefore, GLMM has great potential in extracting rocky desertification information in karst areas. It can also be a reference method for the rapid, accurate, and long‐term evaluation of rocky desertification in karst areas. [ABSTRACT FROM AUTHOR]

Background: The effect of urbanization on the morbidity of hepatitis A remains unclear. We aimed to estimate the association between various urbanization-related indices and hepatitis A morbidity in China. Methods: Data on the annual morbidity of hepatitis A, urbanization-related measures (i.e., gross domestic product per capita, the number of hospitalization beds per 1000 persons, illiteracy rate, tap water coverage, motor vehicles per 100 persons, population density, and the proportion of arable land), and meteorological factors in 31 provincial-level administrative divisions of Chinese mainland during 2005–2018 were collected from the National Population and Health Science Data Sharing Platform, China Statistical Yearbooks, and the China Meteorological Data Sharing Service System, respectively. Generalized linear mixed models were applied to quantify the impacts of different urbanization-related indices on the morbidity of hepatitis A in China after adjusting for covariates. Results: A total of 537,466 hepatitis A cases were reported in China during 2005–2018. The annual morbidity had a decline of 79.4% from 5.64 cases to 1.16 cases per 100,000 people. There were obvious spatial variations with higher morbidity in western China. Nationally, gross domestic product per capita and the number of hospitalization beds per 1000 persons increased from 14,040 to 64,644 CNY and from 2.45 to 6.03 during 2005–2018, respectively. The illiteracy rate decreased from 11.0 to 4.9%. Gross domestic product per capita [relative risk (RR) = 0.96, 95% confidence interval (CI): 0.92–0.99], and the number of hospitalization beds per 1000 persons (RR = 0.79, 95% CI: 0.75–0.83) were associated with the declined morbidity of hepatitis A. By contrast, the increased morbidity of hepatitis A was linked to the illiteracy rate (RR = 1.04, 95% CI: 1.02–1.06). Similar influential factors were detected for children and adults, with greater effects witnessed for children. Conclusions: People in the western region suffered the heaviest burden of hepatitis A in Chinese mainland. Nationally, there was a sharp decline in the morbidity of hepatitis A. The urbanization process was associated with the reduction of hepatitis A morbidity in China during 2005–2018. [ABSTRACT FROM AUTHOR]

Cohen's and Fleiss's kappa are popular estimators for assessing agreement among two and multiple raters, respectively, for a binary response. While additional methods have been developed to account for multiple raters and covariates, they are not always applicable, rarely used, and none simplify to Cohen's kappa. Furthermore, there are no methods to simulate Bernoulli observations under the kappa agreement structure such that the developed methods could be adequately assessed. This manuscript overcomes these shortfalls. First, we developed a model‐based estimator for kappa that accommodates multiple raters and covariates through a generalized linear mixed model and encompasses Cohen's kappa as a special case. Second, we created a framework to simulate dependent Bernoulli observations that upholds all 2‐tuple pair of rater's kappa agreement structure and includes covariates. We used this framework to assess our method when kappa was nonzero. Simulations showed that Cohen's and Fleiss's kappa estimates were inflated unlike our model‐based kappa. We analyzed an Alzheimer's disease neuroimaging study and the classic cervical cancer pathology study. The proposed model‐based kappa and advancement in simulation methodology demonstrates that the popular approaches of Cohen's and Fleiss's kappa are poised to yield invalid conclusions while our work overcomes shortfalls, leading to improved inferences. [ABSTRACT FROM AUTHOR]

Aim: Optimum shifts in species–environment relationships are intensively studied in a wide range of ecological topics, including climate change and species invasion. Numerous statistical methods are used to study optimum shifts, but, to our knowledge, none explicitly estimate it. We extended an existing model to explicitly estimate optimum shifts for multiple species having symmetrical response curves. We called this new Bayesian hierarchical model the Explicit Hierarchical Model of Optimum Shifts (EHMOS). Location: All locations. Taxon: All taxa. Methods: In a simulation study, we compared the accuracy of EHMOS to a mean comparison method and a Bayesian generalized linear mixed model (GLMM). Specifically, we tested if the accuracy of the methods was sensitive to (1) sampling design, (2) species optimum position and (3) species ecological specialization. In addition, we compared the three methods using a real dataset of investigated optimum shifts in 24 Orthopteran species between two time periods along an elevation gradient. Results: Of all the simulated scenarios, EHMOS was the most accurate method. GLMM was the most sensitive method to species optimum position, providing unreliable estimates in the presence of marginal species, that is, species with an optimum close to a sampling boundary. The mean comparison method was also sensitive to species optimum position and ecological specialization, especially in an unbalanced sampling design, with high negative bias and low interval coverage compared to EHMOS. The case study results obtained with EHMOS were consistent with what is expected considering ongoing climate change, with mostly upward shifts, which further improved confidence in the accuracy of the EHMOS method. Main Conclusions: Explicit Hierarchical Model of Optimum Shifts could be used for a wide range of topics and extended to produce new insights, especially in climate change studies. Explicit estimation of optimum shifts notably allows investigation of ecological assumptions that could explain interspecific variability of these shifts. [ABSTRACT FROM AUTHOR]

Road networks pose many well‐documented threats to wildlife, from fragmenting habitats and restricting movement to causing mortality through vehicle collisions. For large, wide‐ranging mammals, home range requirements and seasonal migrations often necessitate road crossings, posing threats to human safety, property, and animal survival. Artificial nightlight, emanating from light posts and urban sky glow, is ubiquitous on and around road networks worldwide; however, its effects on road crossing behavior and the associated mortality risk for wildlife are not well understood. By integrating the latest NASA nightlight products with GPS collar data collected from 67 mule deer (Odocoileus hemionus) over a 7‐year period (2012–2018), we used a resource‐selection framework to assess factors influencing seasonal crossing behavior and road mortality in Salt Lake City, Utah, an expanding metropolitan area in the United States. We found deer preferred to cross the road where surrounding artificial nightlight was lower in both summer and winter seasons, especially during crepuscular and nighttime periods. However, lower nightlight levels also increased the risk of road mortality. Areas with more shrub cover and lower speed limits increased the likelihood of crossing as well as lowered the risk of road mortality. There were five times as many mortality events in winter as in summer, likely because of the combination of deer preference for dark roads mixed with proximity to both higher speed roads and increased human activity. Better understanding how a pervasive and expanding environmental pollutant like artificial nightlight may attract or repel human‐tolerant wildlife species from roadways presents an opportunity to mitigate collision risk while improving population management strategies for this abundant, generalist herbivore and many other economically and ecologically important species. [ABSTRACT FROM AUTHOR]

Second language learners tend to focus more on learning the meaning of vocabulary than on how to use it in their speech and writing. Although comprehensive vocabulary knowledge is necessary for understanding sentences, productive vocabulary knowledge may also have a positive impact on sentence comprehension. Most studies examining the relationship between production and comprehension have compared these abilities between participants or evaluated unrelated criteria between tasks, which may be insufficient for examining the direct effects of productive knowledge on sentence comprehension. Our study investigates changes in sentence comprehension speed during listening, and we used a within-subjects comparison to examine the effect of productive vocabulary knowledge or the lack thereof. We applied generalized linear mixed models to investigate productive vocabulary knowledge effects by partialing out other influential factors, such as confidence, frequency of target words, stimulus duration, and individual differences. The results showed that the sentences with a producible phrase were processed faster than the sentences that included phrases with only comprehensive knowledge or no comprehension. The effect of productive vocabulary knowledge on the speed of sentence comprehension was directly examined with a within-subject comparison, and its contribution was clearly found. This study emphasizes the value of productive vocabulary knowledge acquisition in enhancing the speed of sentence comprehension. [ABSTRACT FROM AUTHOR]

Background: Technological advances involving RNA-Seq and Bioinformatics allow quantifying the transcriptional levels of genes in cells, tissues, and cell lines, permitting the identification of Differentially Expressed Genes (DEGs). DESeq2 and edgeR are well-established computational tools used for this purpose and they are based upon generalized linear models (GLMs) that consider only fixed effects in modeling. However, the inclusion of random effects reduces the risk of missing potential DEGs that may be essential in the context of the biological phenomenon under investigation. The generalized linear mixed models (GLMM) can be used to include both effects. Methods: We present DEGRE (Differentially Expressed Genes with Random Effects), a user-friendly tool capable of inferring DEGs where fixed and random effects on individuals are considered in the experimental design of RNA-Seq research. DEGRE preprocesses the raw matrices before fitting GLMMs on the genes and the derived regression coefficients are analyzed using the Wald statistical test. DEGRE offers the Benjamini-Hochberg or Bonferroni techniques for P-value adjustment. Results: The datasets used for DEGRE assessment were simulated with known identification of DEGs. These have fixed effects, and the random effects were estimated and inserted to measure the impact of experimental designs with high biological variability. For DEGs' inference, preprocessing effectively prepares the data and retains overdispersed genes. The biological coefficient of variation is inferred from the counting matrices to assess variability before and after the preprocessing. The DEGRE is computationally validated through its performance by the simulation of counting matrices, which have biological variability related to fixed and random effects. DEGRE also provides improved assessment measures for detecting DEGs in cases with higher biological variability. We show that the preprocessing established here effectively removes technical variation from those matrices. This tool also detects new potential candidate DEGs in the transcriptome data of patients with bipolar disorder, presenting a promising tool to detect more relevant genes. Conclusions: DEGRE provides data preprocessing and applies GLMMs for DEGs' inference. The preprocessing allows efficient remotion of genes that could impact the inference. Also, the computational and biological validation of DEGRE has shown to be promising in identifying possible DEGs in experiments derived from complex experimental designs. This tool may help handle random effects on individuals in the inference of DEGs and presents a potential for discovering new interesting DEGs for further biological investigation. [ABSTRACT FROM AUTHOR]

Freezing temperatures in fall, winter, and spring can cause damage to multiple perennial fruit crops including northern highbush blueberry (Vaccinium corymbosum). Predictive modeling for lethal temperatures allows producers to make informed decisions about freeze mitigation practices but is lacking for northern highbush blueberry grown in the Pacific Northwest. If buds are hardier than air temperatures, unnecessary use of propane heaters and/or wind machines is costly. In contrast, use of heaters and/or wind machines during freezing, damaging temperatures can minimize crop damage and potential yield loss. The objective of this study was to model cold hardiness across multiple cultivars of northern highbush blueberry grown in various regions in Washington, USA, and to generate predictive cold hardiness models that producers in the Pacific Northwest could use to inform freeze mitigation. Multiple years of experimental cold hardiness data were collected on four cultivars of northern highbush blueberry grown in western and eastern Washington, USA. Freeze chambers were used to reduce bud temperatures systematically, after which buds were dissected and bud survival was assessed. A generalized linear mixed model with a binomial response and logit link was fit to each cultivar to characterize the relationship between bud survival, freezer temperature, recent air temperatures, and growing degree days from fall acclimation to late winter/spring deacclimation. Model simulation was performed to obtain marginal-scale lethal temperature estimates. Model error estimation was performed using cross validation. Results show cultivar-specific cold hardiness models can be generated, and model development and use can help growers make more informed decisions regarding freeze protection that also minimizes costly applications of freeze protection when unnecessary. Furthermore, such models can be adapted to other blueberry growing regions and cultivars experiencing similar climactic conditions.

日本語の文における文節数および係り受け距離平均（MDD）の分布について、ランダムデータと児童作文データの比較、および学年による分布の変化を分析した。その結果、ランダムデータにおいては文節数は幾何分布、MDDは対数正規分布によく当てはまるのに対して、作文データにおいては文節数は学年によって対数正規分布からガンマ分布に推移し、MDDはガンマ分布によく当てはまることが分かった。また、MDDの平均はランダムデータでは文節数の対数に対して指数的に増大するのに対し、作文データでは線形に増大することが分かった。これは自然言語において係り受け距離が最適化されるという従来の知見を概ね支持する結果と言える。一方、MDDは学年に対して非単調な変化を示し、児童の言語発達の複雑さを示唆する。

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.