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Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes. [ABSTRACT FROM AUTHOR]

Clustered partly interval-censored survival data naturally arise from many medical and epidemiological studies. We propose a Bayesian semiparametric approach for fitting a mixed effects proportional hazards (PH) model to clustered partly interval-censored data. The proposed method allows for not only a random intercept as most frailty models do for clustered survival data, but also random effects of covariates. We assume a normal prior for each random intercept/random effect, seeing the instability of a gamma prior for a frailty in this situation. Simulation studies with data generated from both mixed effects PH model and mixed effects accelerated failure times model are conducted, to evaluate the performance of the proposed method and compare it with the three methods currently available in the literature. The application of the proposed approach is illustrated through analyzing the progression-free survival data derived from a phase III metastatic colorectal cancer clinical trial. [ABSTRACT FROM AUTHOR]

Life history trade‐offs are one of the central tenets of evolutionary demography. Trade‐offs, depicting negative covariances between individuals' life history traits, can arise from genetic constraints, or from a finite amount of resources that each individual has to allocate in a zero‐sum game between somatic and reproductive functions. While theory predicts that trade‐offs are ubiquitous, empirical studies have often failed to detect such negative covariances in wild populations. One way to improve the detection of trade‐offs is by accounting for the environmental context, as trade‐off expression may depend on environmental conditions. However, current methodologies usually search for fixed covariances between traits, thereby ignoring their context dependence. Here, we present a hierarchical multivariate ‘covariance reaction norm’ model, adapted from Martin (2023), to help detect context dependence in the expression of life‐history trade‐offs using demographic data. The method allows continuous variation in the phenotypic correlation between traits. We validate the model on simulated data for both intraindividual and intergenerational trade‐offs. We then apply it to empirical datasets of yellow‐bellied marmots (Marmota flaviventer) and Soay sheep (Ovis aries) as a proof‐of‐concept showing that new insights can be gained by applying our methodology, such as detecting trade‐offs only in specific environments. We discuss its potential for application to many of the existing long‐term demographic datasets and how it could improve our understanding of trade‐off expression in particular, and life history theory in general. [ABSTRACT FROM AUTHOR]

Side effects are a common concern of current and potential HIV pre-exposure prophylaxis (PrEP) users, potentially leading to missed doses. We examined the relationship between reported side effects and adherence in the Ontario PrEP Cohort Study (ON-PrEP). In total, 600 predominantly gay (87.3%), White (65.8%), and male (95.0%) participants completed questionnaires assessing the presence and severity of five side effect categories (nausea, diarrhea, headache, abdominal pain, and "other") as well as their adherence to daily PrEP (any missed doses in the previous 4 days). In total, 175 participants (29%) ever reported experiencing side effects: most commonly diarrhea (7.5% of study visits), and most were of mild severity. Lower incomes (p = 0.01), identifying as bisexual (p = 0.04), and baseline concern about side effects (p < 0.001) were associated with ever reporting side effects. The odds of reporting any side effects decreased by a factor of 0.44 (95% confidence interval 0.25–0.80) with each additional year of PrEP use, however 1 in 10 participants still reported side effects after 1 year of use. The odds of reporting optimal adherence were 0.48 (0.28–0.83) times lower for participants reporting any side effects, 0.67 (0.51–0.89) times lower per additional side effect category reported, and 0.78 (0.65–0.97) times lower per incremental increase in side effect severity ratings. We found some evidence of interaction between side effect measures and duration of PrEP use, suggesting that these relationships were stronger for participants taking PrEP for longer. Clinicians should make efforts to ascertain patients' experience of side effects and consider risk counseling and alternative PrEP regimens to promote adherence. [ABSTRACT FROM AUTHOR]

Some versions of secularization theory propose that existential security, education, and urbanicity exert directly measurable negative effects on religiosity cross‐culturally. However, few studies have tested this using longitudinal data. Nor have researchers adequately examined how much the relationship between these modern social conditions (MSCs) and religiosity varies society‐to‐society. This study addresses these limitations in a series of new analyses, using 1989–2020 World/European Values Survey data from approximately 100 countries. Results suggest that the three MSCs do not exert independent, negative effects on religiosity in general, at least not in the short or medium term. Indeed, national‐average increases in these MSCs were not found to predict decreased religiosity. And, interestingly and unexpectedly, the direction of individual‐level relationships between each MSC and religiosity varied greatly between countries and world regions. These findings suggest scholars should probably look elsewhere to explain why average religiosity has decreased in some world locations over recent decades. [ABSTRACT FROM AUTHOR]

Abstract Background The burden of Malaria in Zambia remains a challenge, with the entire population at risk of contracting this infectious disease. Despite concerted efforts by African countries, including Zambia, to implement malaria policies and strategies aimed at reducing case incidence, the region faces significant hurdles, especially with emerging pandemics such as COVID-19. The efforts to control malaria were impacted by the constraints imposed to curb its transmission during the COVID-19 pandemic. The aim of the study was to assess the effect of the COVID-19 pandemic on malaria cases in Zambia and the factors associated by comparing the COVID-19 period and the pre-COVID-19 era. Methods This was a cross-sectional panel study in which routinely collected programmatic data on malaria was used. The data were extracted from the Health Management Information System (HMIS) for the period January 2018 to January 2022. The period 2018 to 2022 was selected purely due to the availability of data and to avoid the problem of extrapolating too far away from the period of interest of the study. A summary of descriptive statistics was performed in which the number of cases were stratified by province, age group, and malaria cases. The association of these variables with the COVID-19 era was checked using the Wilcoxon rank-sum test and Kruskal‒Wallis test as applicable. In establishing the factors associated with the number of malaria cases, a mixed-effect multilevel model using the Poisson random intercept and random slope of the COVID-19 panel. The model was employed to deal with the possible correlation of the number of cases in the non-COVID-19 panel and the expected correlation of the number of cases in the COVID-19 panel. Results A total of 18,216 records were extracted from HMIS from January 2018 to January 2022. Stratifying this by the COVID-19 period/era, it was established that 8,852 malaria cases were recorded in the non-COVID-19 period, whereas 9,364 cases were recorded in the COVID-19 era. Most of the people with malaria were above the age of 15 years. Furthermore, the study found a significant increase in the relative incidence of the COVID-19 panel period compared to the non-COVID-19 panel period of 1.32, 95% CI (1.18, 1.48, p

Background: The burden of Malaria in Zambia remains a challenge, with the entire population at risk of contracting this infectious disease. Despite concerted efforts by African countries, including Zambia, to implement malaria policies and strategies aimed at reducing case incidence, the region faces significant hurdles, especially with emerging pandemics such as COVID-19. The efforts to control malaria were impacted by the constraints imposed to curb its transmission during the COVID-19 pandemic. The aim of the study was to assess the effect of the COVID-19 pandemic on malaria cases in Zambia and the factors associated by comparing the COVID-19 period and the pre-COVID-19 era. Methods: This was a cross-sectional panel study in which routinely collected programmatic data on malaria was used. The data were extracted from the Health Management Information System (HMIS) for the period January 2018 to January 2022. The period 2018 to 2022 was selected purely due to the availability of data and to avoid the problem of extrapolating too far away from the period of interest of the study. A summary of descriptive statistics was performed in which the number of cases were stratified by province, age group, and malaria cases. The association of these variables with the COVID-19 era was checked using the Wilcoxon rank-sum test and Kruskal‒Wallis test as applicable. In establishing the factors associated with the number of malaria cases, a mixed-effect multilevel model using the Poisson random intercept and random slope of the COVID-19 panel. The model was employed to deal with the possible correlation of the number of cases in the non-COVID-19 panel and the expected correlation of the number of cases in the COVID-19 panel. Results: A total of 18,216 records were extracted from HMIS from January 2018 to January 2022. Stratifying this by the COVID-19 period/era, it was established that 8,852 malaria cases were recorded in the non-COVID-19 period, whereas 9,364 cases were recorded in the COVID-19 era. Most of the people with malaria were above the age of 15 years. Furthermore, the study found a significant increase in the relative incidence of the COVID-19 panel period compared to the non-COVID-19 panel period of 1.32, 95% CI (1.18, 1.48, p < 0.0001). The observed numbers, as well as the incident rate ratio, align with the hypothesis of this study, indicating an elevated incidence rate ratio of malaria during the COVID-19 period. Conclusion: This study found that there was an increase in confirmed malaria cases during the COVID-19 period compared to the non-COVID-19 period. The study also found Age, Province, and COVID-19 period to be significantly associated with malaria cases. [ABSTRACT FROM AUTHOR]

AbstractThe disease progression dynamics observed in electronic health records often reflect patients’ health condition evolution, holding the promise of enabling the development of clinical predictive models. These dynamics, however, generally display significant variability among patients, due to some critical factors (e.g., gender and age) and patient-level heterogeneity. Moreover, future health state may not only depend on the current state, but also more distant history states due to the complicated disease progression. To capture this complex transition behavior and address mixed effects in clinical prediction problems, we propose a novel and flexible Bayesian Mixed-Effect Higher-Order Hidden Markov Model (MHOHMM), and develop a classifier based on MHOHMMs. A range of MHOHMMs are designed to capture different data structures and the optimal one is identified by using the k-fold cross-validation approach. An effective two-stage Markov chain Monte Carlo (MCMC) sampling algorithm is designed for model inference. A simulation study is conducted to evaluate the performance of the proposed sampling algorithm and the MHOHMM-based classification method. The practical utility of the proposed framework is demonstrated by a case study on the acute hypotensive episode prediction for intensive care unit patients. Our results show that the MHOHMM-based framework provides good prediction performance. [ABSTRACT FROM AUTHOR]

Estimation of variance components is essential in data analysis based on a linear mixed model. Among different estimation methods, the most commonly used in practice are likelihood-based approaches: maximum likelihood and restricted maximum likelihood. These methods do not guarantee positive-unbiased estimators, however. The method of moments ensures unbiasedness but may yield negative estimates, which can cause problems when estimating fixed effects by generalized least squares. The paper develops an algorithm to derive strictly positive unbiased estimators with minimum variance in a well-defined class. The key idea is to take the method-of-moments estimator, given by a quadratic form of a symmetric matrix A , as a starting point and modify it using the class of square non-singular regularization matrices Q while preserving unbiasedness in addition to ensuring positivity. Different subclasses of structured Q are possible for convenience instead of all possible Q matrices. A search algorithm then finds a local or global optimal matrix A depending on Q and the corresponding optimal variance component estimator by minimizing the variance, a function of the unknown variance components and kurtosis parameters in the model. Availability of an optimal matrix A means that the theoretical and numerical properties of the estimator can be conveniently studied, also in small samples. The proposed method further allows finding matrices A leading to quadratic forms closely approximating the corresponding numerical values of the likelihood-based estimates. The paper investigates the dependence of variance functions on the unknown model parameters. Using two illustrative examples, Examples I and II, the paper also illustrates the use of the matrix Q and the determination of the kurtosis parameters in the search for the optimal variance component estimators by keeping the bias zero and the variance trim for the bias–variance trade-offs. [ABSTRACT FROM AUTHOR]

Saplings (tree species whose diameter at breast height ranges from 1.1 to 9.0 cm) are important in forest growth and development. Their abundance and density can lead to specific forest successional trajectories and ecosystem characteristics. Yet, the consideration of saplings in forest management planning is a relatively new topic and is still rarely included in forest growth models. We developed sapling density models for 10 species groups with the objective of providing forest managers with additional tools to support the development of more precise prediction systems. We used dendrometric and environmental variables to model sapling density and species-wise density ratios. We evaluated Poisson and gamma regressions for the modelling of sapling density and zero-inflated logistic regressions to model species ratios. We used repeated measurements (from 1982 to 2019) from permanent sample plots located in hardwood forests in northeastern North America. Our results show that the gamma regression was superior to the Poisson regression. The cross-validated mean bias using gamma regression was 55 stems·ha−1 with a relative percentage error of 2.5% and an R2 of 0.43. The species-wise sapling density ratio model had an overall R2 of 0.93, and the species-wise mean R2 ranged between 0.90 and 0.96. Among the examined model covariates, stem density of merchantable-sized trees and latitude were significant in both models. We believe that the models we developed can be useful for forest management planning and sustainable merchantable production. [ABSTRACT FROM AUTHOR]

Abstract Partly interval-censored (PIC) data arise frequently in medical studies of diseases that require periodic examinations for symptoms of interest, such as progression-free survival and relapse-free survival. The proportional hazards (PH) model is the most widely used model in survival analysis. This paper introduces our new R package, PICBayes, which implements a set of functions for fitting the PH model to different complexities of partly interval-censored data under the Bayesian semiparametric framework. The main function of PICBayes is to fit (1) the PH model to PIC data; (2) the PH model with spatial frailty to areally-referenced PIC data; (3) the PH model with one random intercept to clustered PIC data; (4) the PH model with one random intercept and one random effect to clustered PIC data; and (5) general mixed effects PH model to clustered PIC data. We also included the corresponding functions for general interval-censored data. A random intercept or random effect can follow both a normal prior and a Dirichlet process mixture prior. The use of the package is illustrated by analyzing two real data sets. [ABSTRACT FROM AUTHOR]

This paper proposed a general framework based on semiparametric additive mixed effects model to identify subgroups on each covariate and estimate the corresponding regression functions simultaneously for longitudinal data, thus it could reveal which covariate contributes to the existence of subgroups among population. A backfitting combined with k-means algorithm was developed to detect subgroup structure on each covariate and estimate each semiparametric additive component across subgroups. A Bayesian information criterion is employed to estimate the actual number of groups. The efficacy and accuracy of the proposed procedure in identifying the subgroups and estimating the regression functions are illustrated through numerical studies. In addition, the authors demonstrate the usefulness of the proposed method with applications to PBC data and Industrial Portfolio's Return data and provide meaningful partitions of the populations. [ABSTRACT FROM AUTHOR]

Individual quality and environmental conditions may mask or interact with energetic trade-offs in life history evolution. Deconstructing these sources of variation is especially difficult in long-lived species that are rarely observed on timescales long enough to disentangle these effects. Here, we investigated relative support for variation in female quality and costs of reproduction as factors shaping differences in life history trajectories using a 32-year dataset of repeated reproductive measurements from 273 marked, known-age female gray seals (Halichoerus grypus). We defined individual reproductive investment using two traits, reproductive frequency (a female's probability of breeding) and provisioning performance (offspring weaning mass). Fitted hierarchical Bayesian models identified individual investment relative to conspecifics (over a female's entire life and in three age classes) and subsequently estimated how these investment metrics and the Atlantic Multidecadal Oscillation are associated with longevity. Individual differences (i.e., quality) contributed a large portion of the variance in reproductive traits. Females that consistently invest well in their offspring relative to other females survive longer. The best-supported model estimated survival as a function of age class–specific provisioning performance, where late-life performance was particularly variable and had the greatest impact on survival, possibly indicating individual variation in senescence. There was no evidence to support a trade-off in reproductive performance and survival at the individual level. Overall, these results suggest that in gray seals, individual quality is a stronger driver in life history variation than individual strategies resulting from energetic trade-offs. [ABSTRACT FROM AUTHOR]

We develop a generalized linear mixed model (GLMM) for bivariate count responses for statistically analyzing dragonfly population data from the Northern Netherlands. The populations of the threatened dragonfly species Aeshna viridis were counted in the years 2015–2018 at 17 different locations (ponds and ditches). Two different widely applied population size measures were used to quantify the population sizes, namely the number of found exoskeletons ('exuviae') and the number of spotted egg-laying females were counted. Since both measures (responses) led to many zero counts but also feature very large counts, our GLMM model builds on a zero-inflated bivariate geometric (ZIBGe) distribution, for which we show that it can be easily parameterized in terms of a correlation parameter and its two marginal medians. We model the medians with linear combinations of fixed (environmental covariates) and random (location-specific intercepts) effects. Modeling the medians yields a decreased sensitivity to overly large counts; in particular, in light of growing marginal zero inflation rates. Because of the relatively small sample size (n = 114) we follow a Bayesian modeling approach and use Metropolis-Hastings Markov Chain Monte Carlo (MCMC) simulations for generating posterior samples. [ABSTRACT FROM AUTHOR]

Abstract Objectives This paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. And showed the two proposed methods can perform well in revealing the significance of parameter in distributed datasets, comparing to a centralized GLMM algorithm from R package (‘lme4’) as the baseline model. Methods The log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation, abbreviated as LA and GH), which supports federated decomposition of GLMM to bring computation to data. To solve the numerical errors and singularity issues, the loss-less estimation of log-sum-exponential trick and the adaptive regularization strategy was used to tackle the problems caused by federated settings. Results Our proposed method can handle GLMM to accommodate hierarchical data with multiple non-independent levels of observations in a federated setting. The experiment results demonstrate comparable (LA) and superior (GH) performances with simulated and real-world data. Conclusion We modified and compared federated GLMMs with different approximations, which can support researchers in analyzing versatile biomedical data to accommodate mixed effects and address non-independence due to hierarchical structures (i.e., institutes, region, country, etc.).

Categorical moderators are often included in mixed‐effects meta‐analysis to explain heterogeneity in effect sizes. An assumption in tests of categorical moderator effects is that of a constant between‐study variance across all levels of the moderator. Although it rarely receives serious thought, there can be statistical ramifications to upholding this assumption. We propose that researchers should instead default to assuming unequal between‐study variances when analysing categorical moderators. To achieve this, we suggest using a mixed‐effects location‐scale model (MELSM) to allow group‐specific estimates for the between‐study variance. In two extensive simulation studies, we show that in terms of Type I error and statistical power, little is lost by using the MELSM for moderator tests, but there can be serious costs when an equal variance mixed‐effects model (MEM) is used. Most notably, in scenarios with balanced sample sizes or equal between‐study variance, the Type I error and power rates are nearly identical between the MEM and the MELSM. On the other hand, with imbalanced sample sizes and unequal variances, the Type I error rate under the MEM can be grossly inflated or overly conservative, whereas the MELSM does comparatively well in controlling the Type I error across the majority of cases. A notable exception where the MELSM did not clearly outperform the MEM was in the case of few studies (e.g., 5). With respect to power, the MELSM had similar or higher power than the MEM in conditions where the latter produced non‐inflated Type 1 error rates. Together, our results support the idea that assuming unequal between‐study variances is preferred as a default strategy when testing categorical moderators. [ABSTRACT FROM AUTHOR]

The need to estimate divergence times in evolutionary histories in the presence of various sources of substitution rate variation has stimulated a rich development of relaxed molecular clock models. Viral evolutionary studies frequently adopt an uncorrelated clock model as a generic relaxed molecular clock process, but this may impose considerable estimation bias if discrete rate variation exists among clades or lineages. For HIV-1 group M, rate variation among subtypes has been shown to result in inconsistencies in time to the most recent common ancestor estimation. Although this calls into question the adequacy of available molecular dating methods, no solution to this problem has been offered so far. Here, we investigate the use of mixed effects molecular clock models, which combine both fixed and random effects in the evolutionary rate, to estimate divergence times. Using simulation, we demonstrate that this model outperforms existing molecular clock models in a Bayesian framework for estimating time-measured phylogenies in the presence of mixed sources of rate variation, while also maintaining good performance in simpler scenarios. By analysing a comprehensive HIV-1 group M complete genome data set we confirm considerable rate variation among subtypes that is not adequately modelled by uncorrelated relaxed clock models. The mixed effects clock model can accommodate this rate variation and results in a time to the most recent common ancestor of HIV-1 group M of 1920 (1915-25), which is only slightly earlier than the uncorrelated relaxed clock estimate for the same data set. The use of complete genome data appears to have a more profound impact than the molecular clock model because it reduces the credible intervals by 50 per cent relative to similar estimates based on short envelope gene sequences.

The aim of this paper is to conduct a systematic and theoretical analysis of estimation and inference for a class of functional mixed effects models (FMEM). Such FMEMs consist of fixed effects that characterize the association between longitudinal functional responses and covariates of interest and random effects that capture the spatial-temporal correlations of longitudinal functional responses. We propose local linear estimates of refined fixed effect functions and establish their weak convergence along with a simultaneous confidence band for each fixed-effect function. We propose a global test for the linear hypotheses of varying coefficient functions and derive the associated asymptotic distribution under the null hypothesis and the asymptotic power under the alternative hypothesis are derived. We also establish the convergence rates of the estimated spatial-temporal covariance operators and their associated eigenvalues and eigenfunctions. We conduct extensive simulations and apply our method to a white-matter fiber data set from a national database for autism research to examine the finite-sample performance of the proposed estimation and inference procedures.

The purpose of this paper was to introduce the nested design and its quantitative data analysis of variance and the SAS implementation. If one of the following two characteristics existed in a specific experimental study， a nested design could be considered to arrange the experiment. Firstly， there was a nested relationship between factors in natural attributes. Secondly， with professional knowledge as the basis， the impact of each factor on the quantitative observation results was divided into primary and secondary. The first feature mentioned above meant that the factors related to the subjects had the conditions for grouping and regrouping. The second feature mentioned above meant that the status of each factor was unequal. In the variance analysis of quantitative data， the calculation formulas of variable error mean square was required to use. Based on four examples and with the help of the SAS software， this paper implemented the univariate analysis of variance for the quantitative data of the nested design， and gave the detailed explanations for the output results of SAS software.

Colonization of urban areas by synanthropic wildlife introduces novel and complex alterations to established ecological processes, including the emergence and spread of infectious diseases. Aggregation at urban resources can increase disease transfer, with wide-ranging species potentially infecting outlying populations. The garrison at the National Training Center, Fort Irwin, California, USA, was recently colonized by mange-infected coyotes (Canis latrans) that also use the surrounding Mojave Desert. This situation provided an ideal opportunity to examine the effects of urban resources on disease dynamics. We evaluated seasonal space use and determined the influence of anthropogenic subsidies, water sources, and prey density on urban resource selection. We found no difference in home range size between healthy and infected individuals, but infected residents had considerably more spatial overlap with one another than healthy residents. All coyotes selected for anthropogenic subsidies during all seasons, while infected coyotes seasonally selected for urban water sources, and healthy coyotes seasonally selected for urban areas with greater densities of natural prey. These results suggest that while all coyotes were selecting for anthropogenic subsidies, infected resident coyotes demonstrated a greater tolerance for other conspecifics, which could be facilitating the horizontal transfer of sarcoptic mange to non-resident coyotes. Conversely, healthy coyotes also selected for natural prey and healthy residents exhibited a lack of spatial overlap with other coyotes suggesting they were not reliant on anthropogenic subsidies and were maintaining territories. Understanding the association between urban wildlife, zoonotic diseases, and urban resources can be critical in determining effective responses for mitigating future epizootics. [ABSTRACT FROM AUTHOR]

Modern approaches to supervised learning like deep neural networks (DNNs) typically implicitly assume that observed responses are statistically independent. In contrast, correlated data are prevalent in real-life large-scale applications, with typical sources of correlation including spatial, temporal and clustering structures. These correlations are either ignored by DNNs, or ad-hoc solutions are developed for specific use cases. We propose to use the mixed models framework to handle correlated data in DNNs. By treating the effects underlying the correlation structure as random effects, mixed models are able to avoid overfitted parameter estimates and ultimately yield better predictive performance. The key to combining mixed models and DNNs is using the Gaussian negative log-likelihood (NLL) as a natural loss function that is minimized with DNN machinery including stochastic gradient descent (SGD). Since NLL does not decompose like standard DNN loss functions, the use of SGD with NLL presents some theoretical and implementation challenges, which we address. Our approach which we call LMMNN is demonstrated to improve performance over natural competitors in various correlation scenarios on diverse simulated and real datasets. Our focus is on a regression setting and tabular datasets, but we also show some results for classification. Our code is available at https://github.com/gsimchoni/lmmnn. [ABSTRACT FROM AUTHOR]

A retrospective analysis of longitudinally collected athlete monitoring data was conducted to generate a model of neuromuscular recovery after anterior cruciate ligament (ACL) injury and reconstruction (ACLR). Neuromuscular testing data including countermovement jump (CMJ) force‐time asymmetries and knee extensor strength (maximum voluntary contractionext) asymmetries (between‐limb asymmetry index—AI) were obtained from athletes with ACLR using semitendinosus (ST) autograft (n = 29; AI measurements: n = 494), bone patellar tendon bone autograft (n = 5; AI measurements: n = 88) and noninjured controls (n = 178; AI measurements: n = 3188). Explosive strength measured as the rate of torque development was also calculated. CMJ force‐time asymmetries were measured over discrete movement phases (eccentric deceleration phase, concentric phase). Separate additive mixed effects models (additive mixed effects model [AMM]) were fit for each AI with a main effect for the surgical technique and a smooth term for the time since surgery (days). The models explained between 43% and 91% of the deviance in neuromuscular recovery after ACLR. The mean time course was generated from the AMM. Comparative neuromuscular recovery profiles of an athlete with an accelerated progression and an athlete with a delayed progression after a serious multiligament injury were generated. Clinical Significance: This paper provides a new perspective on the utility of longitudinal athlete monitoring including routine testing to develop models of neuromuscular recovery after ACLR that can be used to characterize individual progression throughout rehabilitation. [ABSTRACT FROM AUTHOR]

Renewable energy production has been surging around the world in recent years. To mitigate the increasing uncertainty and intermittency of the renewable generation, proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system operation. One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately. In this paper, we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California, USA, by combining the ideas of random effect regression model, segmented regression model, and the least trimmed squares estimate. Since the log-likelihood of the considered model is not differentiable at breakpoints, we propose a new backfitting algorithm to estimate the unknown parameters. The estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

ObjectiveA fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk.MethodsUsing data from SeizureTracker.com, a patient-reported seizure diary tool containing over 1.2 million recorded seizures across 8 years, a novel epilepsy seizure risk assessment tool (EpiSAT) employing a Bayesian mixed-effects hidden Markov model for zero-inflated count data was developed to estimate changes in underlying seizure risk using patient-reported seizure diary and clinical measurement data. Accuracy for correctly assessing underlying seizure risk was evaluated through a simulation comparison. Implications for the natural history of tuberous sclerosis complex (TSC) were assessed using data from SeizureTracker.com.ResultsEpiSAT led to significant improvement in seizure risk assessment compared to traditional approaches relying solely on observed seizure frequencies. Applied to TSC, four underlying seizure risk states were identified. The expected duration of each state was

Objectives: This paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. And showed the two proposed methods can perform well in revealing the significance of parameter in distributed datasets, comparing to a centralized GLMM algorithm from R package ('lme4') as the baseline model.Methods: The log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation, abbreviated as LA and GH), which supports federated decomposition of GLMM to bring computation to data. To solve the numerical errors and singularity issues, the loss-less estimation of log-sum-exponential trick and the adaptive regularization strategy was used to tackle the problems caused by federated settings.Results: Our proposed method can handle GLMM to accommodate hierarchical data with multiple non-independent levels of observations in a federated setting. The experiment results demonstrate comparable (LA) and superior (GH) performances with simulated and real-world data.Conclusion: We modified and compared federated GLMMs with different approximations, which can support researchers in analyzing versatile biomedical data to accommodate mixed effects and address non-independence due to hierarchical structures (i.e., institutes, region, country, etc.). [ABSTRACT FROM AUTHOR]

The dimensions and morphological characteristics of a tree canopy are indicators of photosynthetic capacity and are influenced by competition. Morphometric indices (MI) and correlations between dasometric variables can be used in forest management. The objectives of this study were to calculate and correlate some MI, and to model the proportional crown relationship (pc %) as a function of the normal diameter (dn), using mixed effects models (MEM) in forest plantations of Pinus pseudostrobus and P. montezumae. 11 indices and morphometric values were estimated using information regarding dn, crown projection diameter, clean stem height and total height (A). In addition, a MEM was produced using the classification variables of altitude, exposure and slope, in order to estimate the pc %. The slenderness index (A/dn) and the crown area ratio were 0.65 m cm-1 and 31.19 m2 for P. pseudostrobus, and 0.55 m cm-1 and 21.85 m2 in P. montezumae, while the dimension of the crown diameter was 25 and 31 times the dn for these two species. The best conditions for development of P. pseudotrobus occurred between the elevation intervals of 2,750 - 2,950 m a.s.l., while for P. montezumae the west and southwest exposures showed higher values. MI and MEM adjustment offer a valuable tool to calculate essential parameters of forest management and establishment of forest plantations for these two species. [ABSTRACT FROM AUTHOR]

Abstract Background Holling (Can Entomol 91(5):293–320, 1959) was the first to describe a functional response between a predator’s consumption-rate and the density of its prey. The same concept can be applied to the habitat selection of herbivores, specifically, the change in relative habitat use with the change in habitat availability. Functional responses in habitat selection at a home-range scale have been reported for several large herbivores. However, a link to Holling’s original functional response types has never been drawn, although it could replace the current phenomenological view with a more mechanistically based understanding of functional responses. Methods In this study, discrete choice models were implemented as mixed-effects baseline-category logit models to analyze the variation in habitat selection of a large herbivore at seasonal and diurnal scales. Thus, changes in the use of land cover types with respect to their availability were investigated by monitoring 11 land cover types commonly used by roe deer (Capreolus capreolus) in the Bavarian Forest National Park, Germany. Functional response curves were then fitted using Holling’s formulas. Results Strong evidence of non-linear functional responses was obtained for almost all of the examined land cover types. The shape of the functional response curves varied depending on the season, the time of day, and in some cases between sexes. These responses could be referenced to Holling’s types, with a predominance of type II. Conclusions Our results indicate that Holling’s types can be applied to describe general patterns of the habitat selection behavior of herbivores. Functional responses in habitat selection may occur in situations requiring a trade-off in the selection of land cover types offering different resources, such as due to the temporally varying physiological needs of herbivores. Moreover, two associated parameters defining the curves (prey density and predation rate) can aid in the identification of temporal variations and in determinations of the strength of the cost-benefit ratio for a specific land cover type. Application of our novel approach, using Holling’s equations to describe functional responses in the habitat selection of herbivores, will allow the assignment of general land cover attraction values, independent of availability, thus facilitating the identification of suitable habitats.

Summary: Most modern articles in the palaeomagnetism literature are based on statistics developed by Fisher's 1953 paper 'Dispersion on a sphere', which assumes independent and identically distributed (iid) spherical data. However, palaeomagnetic sample designs are usually hierarchical, where specimens are collected within sites and the data are then combined across sites to calculate an overall mean direction for a geological formation. The specimens within sites are typically more similar than specimens between different sites, and so the iid assumptions fail. This article has three principal goals. The first is to review, contrast and compare both the statistics and geophysics literature on the topic of analysis methods for clustered data on spheres. The second is to present a new hierarchical parametric model, which avoids the unrealistic assumption of rotational symmetry in Fisher's 1953 paper 'Dispersion on a sphere' and may be broadly useful in the analysis of many palaeomagnetic datasets. To help develop the model, we use publicly available data as a case study collected from the Golan Heights volcanic plateau. The third goal is to explore different methods for constructing confidence regions for the overall mean direction based on clustered data. Two bootstrap confidence regions that we propose perform well and will be especially useful to geophysics practitioners. [ABSTRACT FROM AUTHOR]

This paper constructs the double penalized expectile regression for linear mixed effects model, which can estimate coefficient and choose variable for random and fixed effects simultaneously. The method based on the linear mixed effects model by cojoining double penalized expectile regression. For this model, this paper proposes the iterative Lasso expectile regression algorithm to solve the parameter for this mode, and the Schwarz Information Criterion (SIC) and Generalized Approximate Cross-Validation Criterion (GACV) are used to choose the penalty parameters. Additionally, it establishes the asymptotic normality of the expectile regression coefficient estimators that are suggested. Though simulation studies, we examine the effects of coefficient estimation and the variable selection at varying expectile levels under various conditions, including different signal-to-noise ratios, random effects, and the sparsity of the model. In this work, founding that the proposed method is robust to various error distributions at every expectile levels, and is superior to the double penalized quantile regression method in the robustness of excluding inactive variables. The suggested method may still accurately exclude inactive variables and select important variables with a high probability for high-dimensional data. The usefulness of doubly penalized expectile regression in applications is illustrated through a case study using CD4 cell real data. [ABSTRACT FROM AUTHOR]

Empowering women living in the rural economies of the developing world is important to reduce the vulnerability of agricultural households to food insecurity. Despite the existing literature on food insecurity, only a few studies have investigated household vulnerability to food insecurity - important for devising policies and strategies to eradicate food insecurity. This study aims to evaluate the impact of rural women’s empowerment in reducing household’s vulnerability to food insecurity using a household vulnerability to food insecurity index (HVFII) and a rural women composite empowerment index (RWCEI) which has multiple domains. Data from the Pakistan rural household panel survey data of 1868 farming households was analysed using a multi-level mixed effects ordered logistic approach. Descriptive statistics indicate that 33% and 14% of households face severe to mild vulnerability to food insecurity respectively. Of all the domains, rural women are particularly disempowered in economic autonomy and education. Multi-level modelling demonstrated that women’s empowerment overall has positive and significant impact in mitigating the risks related to household food insecurity. Other factors included age of the head of household, and accessing a safety net program, and community infrastructure. The study suggests that those responsible for mitigating household vulnerability to food insecurity in rural Pakistan should specifically focus on empowering women, particularly helping them to be economically autonomous, and providing more information on health and food. [ABSTRACT FROM AUTHOR]

Key message: Seemingly unrelated mixed-effects models were used to model biomass models forLarix olgensis, Pinus koraiensis, andPinus sylvestris, and the developed models considered multiple variables to enhance the practicality of the models. Korean larch (Larix olgensis), Korean pine (Pinus koraiensis), and Mongolian pine (Pinus sylvestris) are important coniferous species in the northeastern area of the P.R. China; hence, accurate biomass estimates of these species are meaningful for evaluating forest health and calculating carbon storage. Based on the tree variables of diameter at breast height ( DBH ), tree height (H ), age, live crown length and crown width, three log-transformed species-specific biomass functions were developed using linear seemingly unrelated regression (SUR) since the error structures of the component biomass model (stem, branches, foliage and roots) were proven to be multiplicative. Furthermore, the plot-level random effect was introduced into the SUR model, namely the SURM model, to achieve more accurate biomass estimates. The results showed that the SURM model has a better fitting performance than the SUR models for three species and model types. The determination coefficient, R 2 , was always larger for the SURM models than for the corresponding SUR models, while the root mean square error, RMSE , was always smaller for the SURM models. By leave-one-out validation, the SURM models were proven to provide more accurate predictions because the mean prediction percent error (MPE ) was close to 0 and the mean absolute percentage error (MAPE ) was smaller than that of the corresponding SUR models across species and model types. In addition, the size and design of the sample used to calibrate for random effects were assessed using the MAPE statistical index, MAPE . The calibration of the SURM showed a decreasing pattern of bias as the sample size increased, indicating that more available sampled trees improved the prediction accuracy of SURM models, while only slight differences existed across sampling designs. Overall, the newly developed biomass models have advantages in various data structures, and will be meaningful and useful in terms of accurately predicting the biomass of three important species in Northeast China. [ABSTRACT FROM AUTHOR]

The likelihood of a US hotel including free breakfast with its rent is empirically measured as a function of hotel star rating, capacity, and rack rate. We used mixed-effects logistic regression to analyze the choices made by 1607 US hotels regarding whether to offer or not offer free breakfast with their room rent. Our analysis reveals that the probability of a hotel including free breakfast with its rent decreases with an increase in the star rating, decreases with an increase in hotel capacity, and decreases with an increase in the rack rate. These results have practical implications for hotel strategy. [ABSTRACT FROM AUTHOR]

We propose a latent-lognormal variance components model for counts that is analogous to the standard normal model for continuous measurements. We analyze gauge repeatability and reproducibility study data using Bayesian methods and illustrate the use of this latent-lognormal model with simulated and real data. [ABSTRACT FROM AUTHOR]

Background: Holling (Can Entomol 91(5):293–320, 1959) was the first to describe a functional response between a predator's consumption-rate and the density of its prey. The same concept can be applied to the habitat selection of herbivores, specifically, the change in relative habitat use with the change in habitat availability. Functional responses in habitat selection at a home-range scale have been reported for several large herbivores. However, a link to Holling's original functional response types has never been drawn, although it could replace the current phenomenological view with a more mechanistically based understanding of functional responses. Methods: In this study, discrete choice models were implemented as mixed-effects baseline-category logit models to analyze the variation in habitat selection of a large herbivore at seasonal and diurnal scales. Thus, changes in the use of land cover types with respect to their availability were investigated by monitoring 11 land cover types commonly used by roe deer (Capreolus capreolus) in the Bavarian Forest National Park, Germany. Functional response curves were then fitted using Holling's formulas. Results: Strong evidence of non-linear functional responses was obtained for almost all of the examined land cover types. The shape of the functional response curves varied depending on the season, the time of day, and in some cases between sexes. These responses could be referenced to Holling's types, with a predominance of type II. Conclusions: Our results indicate that Holling's types can be applied to describe general patterns of the habitat selection behavior of herbivores. Functional responses in habitat selection may occur in situations requiring a trade-off in the selection of land cover types offering different resources, such as due to the temporally varying physiological needs of herbivores. Moreover, two associated parameters defining the curves (prey density and predation rate) can aid in the identification of temporal variations and in determinations of the strength of the cost-benefit ratio for a specific land cover type. Application of our novel approach, using Holling's equations to describe functional responses in the habitat selection of herbivores, will allow the assignment of general land cover attraction values, independent of availability, thus facilitating the identification of suitable habitats. [ABSTRACT FROM AUTHOR]

In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinforcement learning appears ideal for learning how to optimally make these sequential treatment decisions. However, significant challenges must be overcome before reinforcement learning can be effectively deployed in a mobile healthcare setting. In this work we are concerned with the following challenges: (1) individuals who are in the same context can exhibit differential response to treatments (2) only a limited amount of data is available for learning on any one individual, and (3) non-stationary responses to treatment. To address these challenges we generalize Thompson-Sampling bandit algorithms to develop IntelligentPooling. IntelligentPooling learns personalized treatment policies thus addressing challenge one. To address the second challenge, IntelligentPooling updates each user's degree of personalization while making use of available data on other users to speed up learning. Lastly, IntelligentPooling allows responsivity to vary as a function of a user's time since beginning treatment, thus addressing challenge three. [ABSTRACT FROM AUTHOR]

Angular measurements lie on the circumference of a circle and have different characteristics than standard scalar measurements. For applications involving angular data, treating the measured values as scalars can lead to misinterpretation of results if its wrap-around nature is not taken into account. In this article, we propose a variance components wrapped normal model for angular measurements that is analogous to the standard normal model for continuous measurements. This model allows decomposition of contributions to the overall variance to be separated and compared to understand the drivers of the spread of the data. We analyze gauge R & R study data using Bayesian methods and illustrate the use of this wrapped normal model with simulated and real data. We also performed a small simulation study in considering the design of gauge R & R studies with angular measurements. [ABSTRACT FROM AUTHOR]

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences. [ABSTRACT FROM AUTHOR]

Abstract In Malawi, the current approach to family planning using contraceptive methods is individualised, yet studies have shown that variability in contraceptive-use still remains after accounting for it at individual and household levels. Therefore, this study assessed variability at higher levels such as enumeration areas, districts and regions. Biasness of the estimates was addressed by the use of Bayesian approach. The study used 2015–16 Malawi Demographic Health Survey women data. After ascertaining the significance of association of all explanatory variables with contraceptive use, the top-down (backward) stepwise model selection method was followed in the Bayesian framework using Markov Chain Monte Carlo and defuse priors. Models were compared on the basis of Deviance Information Criteria and significance of parameter estimates was checked via credible intervals while that of cross-cluster variances was checked by examining their diagnostic plots. All the selected socio-demographic factors were strongly associated with contraceptive-use (p-value

This paper constructs the double penalized expectile regression for linear mixed effects model, which can estimate coefficient and choose variable for random and fixed effects simultaneously. The method based on the linear mixed effects model by cojoining double penalized expectile regression. For this model, this paper proposes the iterative Lasso expectile regression algorithm to solve the parameter for this mode, and the Schwarz Information Criterion (SIC) and Generalized Approximate Cross-Validation Criterion (GACV) are used to choose the penalty parameters. Additionally, it establishes the asymptotic normality of the expectile regression coefficient estimators that are suggested. Though simulation studies, we examine the effects of coefficient estimation and the variable selection at varying expectile levels under various conditions, including different signal-to-noise ratios, random effects, and the sparsity of the model. In this work, founding that the proposed method is robust to various error distributions at every expectile levels, and is superior to the double penalized quantile regression method in the robustness of excluding inactive variables. The suggested method may still accurately exclude inactive variables and select important variables with a high probability for high-dimensional data. The usefulness of doubly penalized expectile regression in applications is illustrated through a case study using CD4 cell real data.

Purpose: To identify structural and functional outcome measures among patients with Rho-positive autosomal dominant Retinitis Pigmentosa (adRP) to aid neuroprotection trial design. Methods: This was a retrospective cohort study of 52 patients with Rho-positive adRP. We measured Goldmann Visual Fields (GVF) constriction in four sectors (nasal, temporal, inferior, superior), and sectoral Ellipsoid Zone (EZ) width degeneration using Spectral Domain Optical Coherence Tomography (OCT) scans. Disease progression trajectories were projected using mixed effects modeling. Results: Superior GVF was most constricted at presentation and had the shallowest trajectory (less steep negative slope); Inferior GVF was less constricted (corrected p <.001) and had a steeper negative slope (corrected p =.019) than superior GVF. Temporal EZ was most stable on OCT with a relatively shallow negative trajectory (corrected p =.011). Conclusions: Patients' superior visual fields presented with more constriction and subsequently had a shallow negative slope suggesting the corresponding inferior retina may be "burned out" at presentation. Targeted therapies for adRP will likely show a greater efficacy signal if delivered to the superior and nasal retina, which may demonstrate more change on OCT and GVF over the course of a neuroprotection trial. Translational Relevance: Mixed effects analysis of sectoral visual field constriction and EZ degeneration in Rho-positive adRP can prove useful in monitoring therapeutic efficacy and identifying targets for local therapies. [ABSTRACT FROM AUTHOR]

Two groups of English as a second language students engaged in a fourteen-week repeated reading (RR) treatment: (1) a 3x group (n = 16), which engaged in three readings per session, and (2) a 5x group (n = 15), which engaged in five readings per session. Reading rate and background knowledge were measured at five points to assess the effect of treatment length as well. Results from a mixed effects repeated measures ANCOVA model showed that neither treatment group nor treatment length had a significant effect on reading rate, but background knowledge did. The model also revealed that the fixed effects (e.g., treatment, duration, and background knowledge) explained 8.1% of the variation in reading rates (R² = .081). The random effects of individual variation and text variation explained 0.9% (R² = .009) and 0.3% (R² = .003) of variance in reading rate respectively, meaning the entire model could explain 9.3% of the variation (R² = .093). It was concluded that reading three times per session was more efficient than reading five times per session, and background knowledge is a variable that must be controlled for in reading studies. [ABSTRACT FROM AUTHOR]