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We established a mixed-effects model incorporating climatic factors for the base diameter and length of the primary branches of Larix kaempferi using stepwise regression, based on climatic data from a total of 40 standard plots located in Xiaolongshan, Gansu Province, Changlinggang Forest Farm in Jianshi County, Hubei Province, and Dagujia Forest Farm in Qingyuan County, Liaoning Province, as well as the data from 120 L. kaempferi sample trees. Additionally, we created prediction charts for the fixed effects portion of the optimal mixed model to determine the relationship between climatic factors and base diameter and branch length, to explore the differential response of L. kaempferi branches to climatic variables. The results showed that the base diameter mixing model with annual mean temperature and water vapor deficit and the branch length mixing model with annual mean temperature had the best fitting effect, with R2 of 0.6152 and 0.6823, respectively. Based on the fixed effects prediction chart of the mixed model, the overall basal diameter showed an increasing trend with the increases of relative branch depth. The average basal diameter size was in an order of young-aged plantation

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Interval-censored multistate data arise in many studies of chronic diseases, where the health status of a subject can be characterized by a finite number of disease states and the transition between any two states is only known to occur over a broad time interval. We relate potentially time-dependent covariates to multistate processes through semiparametric proportional intensity models with random effects. We study nonparametric maximum likelihood estimation under general interval censoring and develop a stable expectation-maximization algorithm. We show that the resulting parameter estimators are consistent and that the finite-dimensional components are asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we demonstrate through extensive simulation studies that the proposed numerical and inferential procedures perform well in realistic settings. Finally, we provide an application to a major epidemiologic cohort study.

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The agreement intra-class correlation coefficient (ICCa) is a suitable statistical index for inter-rater reliability studies. With balanced Gaussian data, we prove the explicit form of ICCa asymptotic normality (ASN), valid both with analysis of variance (ANOVA), maximum likelihood (ML), or restricted ML (REML) estimates. An asymptotic confidence interval is then derived and its performances are examined by simulation compared to the most commonly used methods, under small, moderate and large sample size designs. Then, we deduce sample size calculation formulas, for the number of subjects and observers needed, to achieve a desired confidence interval width or an acceptable ICCa value test power and give concrete examples of their use. Finally, we propose a likelihood ratio test (LRT) to compare two ICCa's from two distinct subpopulations of patients (or raters) and study by simulation its first order risk and power properties. These methods are illustrated using data from two inter-rater reliability studies, one in physiotherapy with 42 patients and 10 raters and the second in neonatology with 80 subjects and 14 raters. In conclusion, we made recommendations to employ the proposed confidence interval for medium to large samples combined with the quantification of the minimal required sample size at the planning step, or the posterior-power at the analysis step, using simple dedicated formulas. Furthermore, with sufficient sizes, the proposed LRT seems suitable to compare inter-rater reliability between two patient subpopulations. Used wisely, this proposed methods toolbox can remedy common current issues in inter-rater reliability studies.

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Animal research often involves measuring the outcomes of interest multiple times on the same animal, whether over time or for different exposures. These repeated outcomes measured on the same animal are correlated due to animal-specific characteristics. While this repeated measures data can address more complex research questions than single-outcome data, the statistical analysis must take into account the study design resulting in correlated outcomes, which violate the independence assumption of standard statistical methods (e.g. a two-sample t-test, linear regression). When standard statistical methods are incorrectly used to analyze correlated outcome data, the statistical inference (i.e. confidence intervals and p-values) will be incorrect, with some settings leading to null findings too often and others producing statistically significant findings despite no support for this in the data. Instead, researchers can leverage approaches designed specifically for correlated outcomes. In this article, we discuss common study designs that lead to correlated outcome data, motivate the intuition about the impact of improperly analyzing correlated outcomes using methods for independent data, and introduce approaches that properly leverage correlated outcome data.

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Factors contributing to social inequalities are associated with negative mental health outcomes and disparities in mental well-being. We propose a Bayesian hierarchical controlled interrupted time series to evaluate the impact of policies on population well-being whilst accounting for spatial and temporal patterns. Using data from the UKs Household Longitudinal Study, we apply this framework to evaluate the impact of the UKs welfare reform implemented in the 2010s on the mental health of the participants, measured using the GHQ-12 index. Our findings indicate that the reform led to a 2.36% (95% CrI: 0.57%-4.37%) increase in the national GHQ-12 index in the exposed group, after adjustment for the control group. Moreover, the geographical areas that experienced the largest increase in the GHQ-12 index are from more disadvantage backgrounds than affluent backgrounds.

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INTRODUCTION: Reproductive health service (RHS) helps for people to have a delighted and safe sex through their life journey. It enables especially for women to go safely through pregnancy and childbirth and provide couples with the best chance of having a healthy infant. Therefore, this study aimed to identify the significant determinants of RHS utilization among undergraduate regular class students in Assosa University by using advanced methodology. METHODS: We used cross-sectional study design to collect RHS data from 362 students in Assosa University from 5 to 16, may 2021. These students were selected using stratified random sampling technique. We also used cross-tabulation to summarize the extents of RHS utilization across all predictors in terms of percentage and three varieties of multilevel binary logistic regression model to model the determinants of RHS. RESULTS: 42.27% of undergraduate regular class students in Assosa University utilize at least one type of RHS during their time at Assosa University whereas, 57.73% of undergraduate regular class students in this University are not utilized it. Among three varieties of multilevel binary logistic regression models, the random slopes two-level model was selected as a best fitted model for the datasets. At 5% level of significance, awareness about RHS, gender, preference of service fees and student's monthly average income were significant predictor variables in this model. In addition, the covariates; age, gender and preference of service fees have a significant random effects on utilization of RHS across all colleges/school. CONCLUSION: Students who; preferred service fee as usual rate, have awareness about RHS, are females and have high monthly average income were more likely to utilize RHS. RHS utilization among undergraduate regular students in Assosa University is likely to increase more effectively with interventions that address these factors.

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BACKGROUND: The unprecedented emergence of the COVID-19 pandemic necessitated the development and global distribution of vaccines, making the understanding of global vaccine acceptance and hesitancy crucial to overcoming barriers to vaccination and achieving widespread immunization. OBJECTIVE: This umbrella review synthesizes findings from systematic reviews and meta-analyses to provide insights into global perceptions on COVID-19 vaccine acceptance and hesitancy across diverse populations and regions. METHODS: We conducted a literature search across major databases to identify systematic reviews and meta-analysis that reported COVID-19 vaccine acceptance and hesitancy. The AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews) criteria were used to assess the methodological quality of included systematic reviews. Meta-analysis was performed using STATA 17 with a random effect model. The data synthesis is presented in a table format and via a narrative. RESULTS: Our inclusion criteria were met by 78 meta-analyses published between 2021 and 2023. Our analysis revealed a moderate vaccine acceptance rate of 63% (95% CI 0.60%-0.67%) in the general population, with significant heterogeneity (I2 = 97.59%). Higher acceptance rates were observed among health care workers and individuals with chronic diseases, at 64% (95% CI 0.57%-0.71%) and 69% (95% CI 0.61%-0.76%), respectively. However, lower acceptance was noted among pregnant women, at 48% (95% CI 0.42%-0.53%), and parents consenting for their children, at 61.29% (95% CI 0.56%-0.67%). The pooled vaccine hesitancy rate was 32% (95% CI 0.25%-0.39%) in the general population. The quality assessment revealed 19 high-quality, 38 moderate-quality, 15 low-quality, and 6 critically low-quality meta-analyses. CONCLUSIONS: This review revealed the presence of vaccine hesitancy globally, emphasizing the necessity for population-specific, culturally sensitive interventions and clear, credible information dissemination to foster vaccine acceptance. The observed disparities accentuate the need for continuous research to understand evolving vaccine perceptions and to address the unique concerns and needs of diverse populations, thereby aiding in the formulation of effective and inclusive vaccination strategies. TRIAL REGISTRATION: PROSPERO CRD42023468363; https://tinyurl.com/2p9kv9cr.

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Popular statistical software provides the Bayesian information criterion (BIC) for multi-level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi-level model with respect to level-specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multi-level models. In this study, we derive the BIC's penalty term for level-specific fixed- and random-effect selection in a two-level nested design. In this new version of BIC, called BIC E 1 , this penalty term is decomposed into two parts if the random-effect variance-covariance matrix has full rank: (a) a term with the log of average sample size per cluster and (b) the total number of parameters times the log of the total number of clusters. Furthermore, we derive the new version of BIC, called BIC E 2 , in the presence of redundant random effects. We show that the derived formulae, BIC E 1 and BIC E 2 , adhere to empirical values via numerical demonstration and that BIC E ( E indicating either E 1 or E 2 ) is the best global selection criterion, as it performs at least as well as BIC with the total sample size and BIC with the number of clusters across various multi-level conditions through a simulation study. In addition, the use of BIC E 1 is illustrated with a textbook example dataset.

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Introduction: Telehealth interventions have the potential of improving health outcomes for individuals with chronic obstructive pulmonary disease (COPD). However, the precise impact of telehealth on exacerbation and hospital readmissions remains inconclusive. This lack of knowledge on the effectiveness of telehealth for COPD care might be due to lack of clarity regarding which variables are most strongly associated with enrolment and dropout rates. Objectives: Among individuals with COPD in telehealth studies, we aimed to: (1) estimate the extent to which trial-related variables are associated with enrolment and dropout rates, and identify reasons for dropouts; (2) estimate the extent to which patients-related and intervention-related variables are associated with dropout rates; (3) estimate the effect of enrolment rate and dropout rate on effect size; (4) estimate the effect of trial-related, patient-related, and intervention-related variables on effect size. Methods: A systematic literature search was conducted using four electronic databases. Two independent reviewers screened all retrieved titles, abstracts and full texts according to the inclusion criteria and extracted the data. A random-effect meta-regression analysis was conducted to estimate the overall enrolment and dropout rates, and estimated the different variables' effects on the enrolment rate, dropout rate, and effect sizes in the studies included in the review. Results: A total of 56 studies comprising 7530 participants were identified. The estimated enrolment and dropout rates were 50.3 % and 14.9 %, respectively. Trial-related variables influence enrollment and dropout rates, including RCT designs and the recruitments. The patient-related variables, including age and severity of the disease, and intervention-related variables, including the components of the intervention and mode of delivery, influence dropout rates. Studies with low dropout rates had a bigger effect size by 0.23. The main reported reasons for dropping out of the intervention were related to death (21 %) followed by lost to follow-up (14 %). Conclusion: Trial, patient, and intervention-related variables were found to influence the enrolment and dropout rates. This would help plan and develop a more appealing telehealth intervention that patients can easily accept and incorporate into their everyday lives. Registration information: International Prospective Register of Systematic Reviews (PROSPERO); ID: CRD42017078541.

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Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R2= 0.75, whereas random effect generalized linear model had an R2= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.

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We propose a novel frailty model with change points applying random effects to a Cox proportional hazard model to adjust the heterogeneity between clusters. In the specially focused eight Empowered Action Group (EAG) states in India, there are problems with different survival curves for children up to the age of five in different states. Therefore, when analyzing the survival times for the eight EAG states, we need to adjust for the effects among states (clusters). Because the frailty model includes random effects, the parameters are estimated using the expectation-maximization (EM) algorithm. Additionally, our model needs to estimate change points; we thus propose a new algorithm extending the conventional estimation algorithm to the frailty model with change points to solve the problem. We show a practical example to demonstrate how to estimate the change point and the parameters of the distribution of random effect. Our proposed model can be easily analyzed using the existing R package. We conducted simulation studies with three scenarios to confirm the performance of our proposed model. We re-analyzed the survival time data of the eight EAG states in India to show the difference in analysis results with and without random effect. In conclusion, we confirmed that the frailty model with change points has a higher accuracy than the model without a random effect. Our proposed model is useful when heterogeneity needs to be taken into account. Additionally, the absence of heterogeneity did not affect the estimation of the regression parameters.

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The main goals of Phase II trials are to identify the therapeutic efficacy of new treatments and continue monitoring all the possible adverse effects. In Phase II trials, it is important to develop an adaptive randomization (AR) procedure that takes into account both the efficacy and toxicity. In most existing articles, toxicity is modeled as a binary endpoint through an unobservable random effect (frailty) to link the efficacy and toxicity. However, this approach does not capture toxicity profiles that evolve over time. In this article, we propose a new Bayesian adaptive randomization (BAR) procedure using the covariate-adjusted efficacy-toxicity ratio (ETR) index, where efficacy and toxicity are jointly modelled as time-to-event (TTE) outcomes. Furthermore, we also propose early stopping rules for toxicity and futility such that inferior treatments can be dropped at earlier time of trial. Simulation results show that compared to the BAR procedures based solely on the efficacy and that based on TTE efficacy and binary toxicity outcomes, the proposed BAR procedure can better identify the difference in treatment toxicity such that it can assign more patients to the superior treatment arm under some scenarios.

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Heat stress compromises dairy production by decreasing feed intake and milk yield, and it may also alter milk composition and feed efficiency. However, little information is available for evaluating such effects across different levels of heat stress and cows enrolled in heat stress studies. The objectives of this study were to evaluate the effects of heat stress on dry matter intake (DMI), energy-corrected milk (ECM), milk composition, and feed efficiency (kg ECM/kg DMI) and to investigate the relationship between such effects and heat stress intervention and animal characteristics by using meta-analytical approaches. Data from 31 studies (34 trials) fulfilled the inclusion criteria and were used for analysis. Results showed that heat stress decreased DMI, ECM, and milk protein concentration, but did not alter milk fat concentration or feed efficiency. Meta-regression confirmed that such reductions in DMI and ECM were significantly associated with increasing temperature-humidity index (THI). Over the period of heat stress, for each unit increase in THI, DMI and ECM decreased by 4.13% and 3.25%, respectively, in mid-lactation cows. Regression models further revealed the existence of a strong interaction between THI and lactation stage, which partially explained the large heterogeneity in effect sizes of DMI and ECM. The results indicated a need for more research on the relationship between the effect of heat stress and animal characteristics. This study calls for the implementation of mitigation strategies in heat-stressed herds due to the substantial decrease in productivity.

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OBJECTIVES: The scientific literature contains an abundance of prediction models for hospital readmissions. However, no review has yet synthesized their predictors across various patient populations. Therefore, our aim was to examine predictors of hospital readmissions across 13 patient populations. STUDY DESIGN AND SETTING: An overview of systematic reviews was combined with a meta-analytical approach. Two thousand five hundred four different predictors were categorized using common ontologies to pool and examine their odds ratios and frequencies of use in prediction models across and within different patient populations. RESULTS: Twenty-eight systematic reviews with 440 primary studies were included. Numerous predictors related to prior use of healthcare services (odds ratio; 95% confidence interval: 1.64; 1.42-1.89), diagnoses (1.41; 1.31-1.51), health status (1.35; 1.20-1.52), medications (1.28; 1.13-1.44), administrative information about the index hospitalization (1.23; 1.14-1.33), clinical procedures (1.20; 1.07-1.35), laboratory results (1.18; 1.11-1.25), demographic information (1.10; 1.06-1.14), and socioeconomic status (1.07; 1.02-1.11) were analyzed. Diagnoses were frequently used (in 37.38%) and displayed large effect sizes across all populations. Prior use of healthcare services showed the largest effect sizes but were seldomly used (in 2.57%), whereas demographic information (in 13.18%) was frequently used but displayed small effect sizes. CONCLUSION: Diagnoses and patients' prior use of healthcare services showed large effects both across and within different populations. These results can serve as a foundation for future prediction modeling.

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Background: Catastrophic health expenditures (CHE) can trigger illness-caused poverty and compound poverty-caused illness. Our study is the first regional comparative study to analyze CHE trends and health inequality in eastern, central and western China, exploring the differences and disparities across regions to make targeted health policy recommendations. Methods: Using data from China's Household Panel Study (CFPS), we selected Shanghai, Henan and Gansu as representative eastern-central-western regional provinces to construct a unique 5-year CHE unbalanced panel dataset. CHE incidence was measured by calculating headcount; CHE intensity was measured by overshoot and CHE inequality was estimated by concentration curves (CC) and the concentration index (CI). A random effect model was employed to analyze the impact of household head socio-economic characteristics, the household socio-economic characteristics and household health utilization on CHE incidence across the three regions. Results: The study found that the incidence and intensity of CHE decreased, but the degree of CHE inequality increased, across all three regions. For all regions, the trend of inequality first decreased and then increased. We also revealed significant differences across the eastern, central and western regions of China in CHE incidence, intensity, inequality and regional differences in the CHE influencing factors. Affected by factors such as the gap between the rich and the poor and the uneven distribution of medical resources, families in the eastern region who were unmarried, use supplementary medical insurance, and had members receiving outpatient treatment were more likely to experience CHE. Families with chronic diseases in the central and western regions were more likely to suffer CHE, and rural families in the western region were more likely to experience CHE. Conclusions: The trends and causes of CHE varied across the different regions, which requires a further tilt of medical resources to the central and western regions; improved prevention and financial support for chronic diseases households; and reform of the insurance reimbursement policy of outpatient medical insurance. On a regional basis, health policy should not only address CHE incidence and intensity, but also its inequality.

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Food availability shapes demographic parameters and population dynamics. Certain species have adapted to predictable anthropogenic food resources like landfills. However, abrupt shifts in food availability can negatively impact such populations. While changes in survival are expected, the age-related effects remain poorly understood, particularly in long-lived scavenger species. We investigated the age-specific demographic response of a Griffon vulture (Gyps fulvus) population to a reduction in organic matter in a landfill and analyzed apparent survival and the probability of transience after initial capture using a Bayesian Cormack-Jolly-Seber model on data from 2012-2022. The proportion of transients among newly captured immatures and adults increased after the reduction in food. Juvenile apparent survival declined, increased in immature residents, and decreased in adult residents. These results suggest that there was a greater likelihood of permanent emigration due to intensified intraspecific competition following the reduction in food. Interestingly, resident immatures showed the opposite trend, suggesting the persistence of high-quality individuals despite the food scarcity. Although the reasons behind the reduced apparent survival of resident adults in the final four years of the study remain unclear, non-natural mortality potentially plays a part. In Europe landfill closure regulations are being implemented and pose a threat to avian scavenger populations, which underlines the need for research on food scarcity scenarios and proper conservation measures.

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This study employs the principles of computer science and statistics to evaluate the efficacy of the linear random effect model, utilizing Lasso variable selection techniques (including Lasso, Elastic-Net, Adaptive-Lasso, and SCAD) through numerical simulation and empirical research. The analysis focuses on the model's consistency in variable selection, prediction accuracy, stability, and efficiency. This study employs a novel approach to assess the consistency of variable selection across models. Specifically, the angle between the actual coefficient vector ß and the estimated coefficient vector ß Ë† is computed to determine the degree of consistency. Additionally, the boxplot tool of statistical analysis is utilized to visually represent the distribution of model prediction accuracy data and variable selection consistency. The comparative stability of each model is assessed based on the frequency of outliers. This study conducts comparative experiments of numerical simulation to evaluate a proposed model evaluation method against commonly used analysis methods. The results demonstrate the effectiveness and correctness of the proposed method, highlighting its ability to conveniently analyze the stability and efficiency of each fitting model.

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Stand biomass models can be used as basic decision-making tools in forest management planning. The Moso bamboo (Phyllostachys pubescens) forest, a major forest system in tropical and subtropical regions, represents a substantial carbon sink, slowing down the rise of greenhouse gas concentrations in the earth's atmosphere. Bamboo stand biomass models are important for the assessment of the contribution of carbon to the terrestrial ecosystem. We constructed a stand biomass model for Moso bamboo using destructively sampled data from 45 sample plots that were located across the Yixing state-owned farm in Jiangsu Province, China. Among several bamboo stand variables used as predictors in the stand biomass models, mean diameter at breast height (MDBH), mean height (MH), and canopy density (CD) of bamboo contributed significantly to the model. To increase the model's accuracy, we introduced the effects of bamboo forest block as a random effect into the model through mixed-effects modeling. The mixed-effects model described a large part of stand biomass variation (R2 = 0.6987), significantly higher than that of the ordinary least squares regression model (R2 = 0.5748). Our results show an increased bamboo stand biomass with increasing MH and CD, confirming our model's biological logic. The proposed stand biomass model may have important management implications; for example, it can be combined with other bamboo models to estimate bamboo canopy biomass, carbon sequestration, and bamboo biomass at different growth stages.

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Road deaths globally have steadily climbed in recent years with the increase in motorisation. Yet in many economically developed countries they have actually fallen. Explanations for this reduction include the role of improved vehicle and highway design, better enforcement and the impact of the economic downturn, whilst there is also some evidence that microeconomic factors like changes in road fuel prices could be contributing to this situation. This paper investigates the effects of fuel prices on road collision frequency in countries where fuel prices are relatively high. Monthly panel data from 28 EU member states from 2005 to 2018 was analysed for both petrol and diesel prices, and for fatalities, total injury collisions and total collisions using: 1) random effect negative binomial (RENB); and 2) population-averaged negative binomial using generalised estimating equations (GEE). The findings indicate that higher fuel prices lead to lower road traffic deaths, injury collisions and overall collisions across all 28 EU member states. Thus, for every 10 percent increase in fuel prices, there will be a 2.6 percent and 2.2 percent reduction in fatalities for petrol and diesel models, respectively. Similarly, total collisions fall by 1.4 percent for petrol and 1.2 for diesel models, and total casualties fall by 1.6 percent for petrol and 1.4 percent for diesel with every 10 percent rise in fuel price. These results could be due to drivers reducing speed to achieve better fuel efficiency and to people driving less to save money - in particular younger, riskier drivers - hence reducing exposure. These findings suggest that policies replacing petrol and diesel vehicles with alternative fuel sources over the next 20 years could have negative repercussions for road safety if not adequately considered.

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For theoretical studies, reaction norm evolution in a changing environment can be modeled by means of the multivariate breeder's equation, with the reaction norm parameters treated as traits in their own right. This is, however, not a feasible approach for the use of field data, where the intercept and slope values are not available. An alternative approach is to use infinite-dimensional characters and smooth covariance function estimates found by, e.g., random regression. This is difficult because of the need to find, for example, polynomial basis functions that fit the data reasonably well over time, and because reaction norms in multivariate cases are correlated, such that they cannot be modeled independently. Here, I present an alternative approach based on a multivariate linear mixed model of any order, with dynamical incidence and residual covariance matrices that reflect the changing environment. From such a mixed model follows a dynamical BLUP model for the estimation of the individual reaction norm parameter values at any given parent generation, and for updating of the mean reaction norm parameter values from generation to generation by means of Robertson's secondary theorem of natural selection. This will, for example, make it possible to disentangle the microevolutionary and plasticity components in climate change responses. The BLUP model incorporates the additive genetic relationship matrix in the usual way, and overlapping generations can easily be accommodated. Additive genetic and environmental model parameters are assumed to be known and constant, but it is discussed how they can be estimated by means of a prediction error method. The identifiability by the use of field or laboratory data containing environmental, phenotypic, fitness, and additive genetic relationship data is an important feature of the proposed model.