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IntroductionAlthough the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics.MethodsBased on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic.ResultsWe found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022.ConclusionOur results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.

Biclustering is a useful method for simultaneously grouping samples and features and has been applied across various biomedical data types. However, most existing biclustering methods lack the ability to integratively analyze multi-modal data such as multi-omics data such as genome, transcriptome and epigenome. Moreover, the potential of leveraging biological knowledge represented by graphs, which has been demonstrated to be beneficial in various statistical tasks such as variable selection and prediction, remains largely untapped in the context of biclustering. To address both, we propose a novel Bayesian biclustering method called Bayesian graph-guided biclustering (BGB). Specifically, we introduce a new hierarchical sparsity-inducing prior to effectively incorporate biological graph information and establish a unified framework to model multi-view data. We develop an efficient Markov chain Monte Carlo algorithm to conduct posterior sampling and inference. Extensive simulations and real data analysis show that BGB outperforms other popular biclustering methods. Notably, BGB is robust in terms of utilizing biological knowledge and has the capability to reveal biologically meaningful information from heterogeneous multi-modal data. [ABSTRACT FROM AUTHOR]

Cognitive functioning in older age profoundly impacts quality of life and health. While most research on cognition in older age has focused on mean levels, intraindividual variability (IIV) around this may have risk factors and outcomes independent of the mean value. Investigating risk factors associated with IIV has typically involved deriving a summary statistic for each person from residual error around a fitted mean. However, this ignores uncertainty in the estimates, prohibits exploring associations with time-varying factors, and is biased by floor/ceiling effects. To address this, we propose a mixed-effects location scale beta-binomial model for estimating average probability and IIV in a word recall test in the English Longitudinal Study of Ageing. After adjusting for mean performance, an analysis of 9,873 individuals across 7 (mean = 3.4) waves (2002–2015) found IIV to be greater at older ages, with lower education, in females, with more difficulties in activities of daily living, in later birth cohorts, and when interviewers recorded issues potentially affecting test performance. Our study introduces a novel method for identifying groups with greater IIV in bounded discrete outcomes. Our findings have implications for daily functioning and care, and further work is needed to identify the impact for future health outcomes. [ABSTRACT FROM AUTHOR]

Network meta‐analysis (NMA) is a statistical procedure to simultaneously compare multiple interventions. Despite the added complexity of performing an NMA compared with the traditional pairwise meta‐analysis, under proper assumptions the NMA can lead to more efficient estimates on the comparisons of interventions by combining and contrasting the direct and indirect evidence into a form of evidence that can be used to underpin treatment guidelines. Two broad classes of NMA methods are commonly used in practice: the contrast‐based (CB‐NMA) and the arm‐based (AB‐NMA) models. While CB‐NMA only focuses on the relative effects by assuming fixed intercepts, the AB‐NMA offers greater flexibility on the estimands, including both the absolute and relative effects by assuming random intercepts. A major criticism of the AB‐NMA, on which we aim to elaborate in this paper, is that it does not retain randomization within trials, which may introduce bias in the estimated relative effects in some scenarios. This criticism was drawn under the implicit assumption that a given relative effect is transportable, in which case the data generating mechanism favors the inference based on CB‐NMA, which models the relative effect. In this article, we aim to review, summarize, and elaborate on the underlying assumptions, similarities and differences, and also the advantages and disadvantages, between CB‐NMA and AB‐NMA methods. As indirect treatment comparison is susceptible to risk of bias no matter which approach is taken, it is important to consider both approaches in practice as complementary sensitivity analyses and to provide the totality of evidence from the data. This article is categorized under:Statistical Models > Bayesian ModelsStatistical Models > Generalized Linear ModelsStatistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory [ABSTRACT FROM AUTHOR]

Understanding the spatio‐temporal patterns of the coronavirus disease 2019 (COVID‐19) is essential to construct public health interventions. Spatially referenced data can provide richer opportunities to understand the mechanism of the disease spread compared to the more often encountered aggregated count data. We propose a spatio‐temporal Dirichlet process mixture model to analyze confirmed cases of COVID‐19 in an urban environment. Our method can detect unobserved cluster centers of the epidemics, and estimate the space‐time range of the clusters that are useful to construct a warning system. Furthermore, our model can measure the impact of different types of landmarks in the city, which provides an intuitive explanation of disease spreading sources from different time points. To efficiently capture the temporal dynamics of the disease patterns, we employ a sequential approach that uses the posterior distribution of the parameters for the previous time step as the prior information for the current time step. This approach enables us to incorporate time dependence into our model in a computationally efficient manner without complicating the model structure. We also develop a model assessment by comparing the data with theoretical densities, and outline the goodness‐of‐fit of our fitted model. [ABSTRACT FROM AUTHOR]

When evaluating a diagnostic test, it is common that a gold standard may not be available. One example is the diagnosis of SARS‐CoV‐2 infection using saliva sampling or nasopharyngeal swabs. Without a gold standard, a pragmatic approach is to postulate a "reference standard," defined as positive if either test is positive, or negative if both are negative. However, this pragmatic approach may overestimate sensitivities because subjects infected with SARS‐CoV‐2 may still have double‐negative test results even when both tests exhibit perfect specificity. To address this limitation, we propose a Bayesian hierarchical model for simultaneously estimating sensitivity, specificity, and disease prevalence in the absence of a gold standard. The proposed model allows adjusting for study‐level covariates. We evaluate the model performance using an example based on a recently published meta‐analysis on the diagnosis of SARS‐CoV‐2 infection and extensive simulations. Compared with the pragmatic reference standard approach, we demonstrate that the proposed Bayesian method provides a more accurate evaluation of prevalence, specificity, and sensitivity in a meta‐analytic framework. [ABSTRACT FROM AUTHOR]

In Duchenne muscular dystrophy (DMD) and other rare diseases, recruiting patients into clinical trials is challenging. Additionally, assigning patients to long‐term, multi‐year placebo arms raises ethical and trial retention concerns. This poses a significant challenge to the traditional sequential drug development paradigm. In this paper, we propose a small‐sample, sequential, multiple assignment, randomized trial (snSMART) design that combines dose selection and confirmatory assessment into a single trial. This multi‐stage design evaluates the effects of multiple doses of a promising drug and re‐randomizes patients to appropriate dose levels based on their Stage 1 dose and response. Our proposed approach increases the efficiency of treatment effect estimates by (i) enriching the placebo arm with external control data, and (ii) using data from all stages. Data from external control and different stages are combined using a robust meta‐analytic combined (MAC) approach to consider the various sources of heterogeneity and potential selection bias. We reanalyze data from a DMD trial using the proposed method and external control data from the Duchenne Natural History Study (DNHS). Our method's estimators show improved efficiency compared to the original trial. Also, the robust MAC‐snSMART method most often provides more accurate estimators than the traditional analytic method. Overall, the proposed methodology provides a promising candidate for efficient drug development in DMD and other rare diseases. [ABSTRACT FROM AUTHOR]

Estimation of time-varying autoregressive models for count-valued time series can be computationally challenging. In this direction, we propose a time-varying Poisson autoregressive (TV-Pois-AR) model that accounts for the changing intensity of the Poisson process. Our approach can capture the latent dynamics of the time series and therefore make superior forecasts. To speed up the estimation of the TV-AR process, our approach uses the Bayesian Lattice Filter. In addition, the No-U-Turn Sampler (NUTS) is used, instead of a random walk Metropolis–Hastings algorithm, to sample intensity-related parameters without a closed-form full conditional distribution. The effectiveness of our approach is evaluated through model-based and empirical simulation studies. Finally, we demonstrate the utility of the proposed model through an example of COVID-19 spread in New York State and an example of US COVID-19 hospitalization data. [ABSTRACT FROM AUTHOR]

Interrupted time series (ITS) design is a quasi-experimental approach for evaluating the impact of an intervention in public health research using segmented linear regression (SLR) models. Usually, aggregated data across multiple time points before and after an intervention are compared to detect a change in intercept, slope, or both; however, the use of aggregated data can lead to an ecological fallacy, imprecise estimates, loss of power, and spurious inferences. We formulated three models with/without measurement error in the dependent variable to address different data limitations resulted from aggregation. Adopting the Bayesian hierarchical methodology for three standard SLR models from an ITS design, we compared performances of the varying pre-intervention intercept model (VPIM), varying intercept model (VIM) and measurement error model (MEM) with a non-hierarchical model (NHM) using real-life data and simulation studies. The MEM first estimates true value using observed data and standard deviation, then regresses the independent variables on the estimated true values. The results demonstrated the suitability of the hierarchical models through sustained improvement in model performance and parameter estimates over the NHM. The VPIM and VIM provided precise estimates modeling the population of clusters and pooling information across parameters. The measurement error assumption, along with the Bayesian model’s hierarchical formulation and generative nature, helped stabilize the unstable values of the dependent variable based on observed data and standard deviations. [ABSTRACT FROM AUTHOR]

In spatio‐temporal epidemiological analysis, it is of critical importance to identify the significant covariates and estimate the associated time‐varying effects on the health outcome. Due to the heterogeneity of spatio‐temporal data, the subsets of important covariates may vary across space and the temporal trends of covariate effects could be locally different. However, many spatial models neglected the potential local variation patterns, leading to inappropriate inference. Thus, this article proposes a flexible Bayesian hierarchical model to simultaneously identify spatial clusters of regression coefficients with common temporal trends, select significant covariates for each spatial group by introducing binary entry parameters and estimate spatio‐temporally varying disease risks. A multistage strategy is employed to reduce the confounding bias caused by spatially structured random components. A simulation study demonstrates the outperformance of the proposed method, compared with several alternatives based on different assessment criteria. The methodology is motivated by two important case studies. The first concerns the low birth weight incidence data in 159 counties of Georgia, USA, for the years 2007 to 2018 and investigates the time‐varying effects of potential contributing covariates in different cluster regions. The second concerns the circulatory disease risks across 323 local authorities in England over 10 years and explores the underlying spatial clusters and associated important risk factors. [ABSTRACT FROM AUTHOR]

Simple Summary: Food availability plays a significant role in modulating populations, especially in species relying on human-generated food sources like landfills. Sudden changes in food access can negatively impact vital parameters such as survival. For long-lived scavenger species, understanding how different age groups respond to these changes is essential since landfills are condemned to be closed. We studied the response in terms of apparent survival of griffon vultures to a decline in landfill organic matter after a waste treatment center became operational. The proportion of transients increased in all age groups. Survival dropped in juveniles and adult residents but increased in immature residents. These findings suggest that vultures permanently emigrated at higher rates due to intensified competition after the reduction in food. Intriguingly, immature resident vultures showed resilience, indicating the presence of high-quality individuals despite the food scarcity. The reasons behind reduced survival in adult residents during the final four study years remain unclear but are potentially linked to non-natural mortality. This research highlights the challenges facing scavengers as European landfill sites close, thereby stressing the need for food scarcity studies and timely conservation measures. 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. [ABSTRACT FROM AUTHOR]

We use a Bayesian spatio-temporal model, first to smooth small-area initial life expectancy estimates in Barcelona for 2020, and second to predict what small-area life expectancy would have been in 2020 in absence of covid-19 using mortality data from 2007 to 2019. This allows us to estimate and map the small-area life expectancy loss, which can be used to assess how the impact of covid-19 varies spatially, and to explore whether that loss relates to underlying factors, such as population density, educational level, or proportion of older individuals living alone. We find that the small-area life expectancy loss for men and for women have similar distributions, and are spatially uncorrelated but positively correlated with population density and among themselves. On average, we estimate that the life expectancy loss in Barcelona in 2020 was of 2.01 years for men, falling back to 2011 levels, and of 2.11 years for women, falling back to 2006 levels. [ABSTRACT FROM AUTHOR]

As an alternative to animal use, computer simulations are useful for predicting pharmacokinetics and cardiovascular activities. For this purpose, we constructed a statistical model to simulate the effects of local anesthetic agents. To train the model, animal experiments were performed on 6-week-old male Hartley guinea pigs. Firstly, the guinea pigs' backs were shaved, then local anesthetic agents were subcutaneously injected, with subsequent stimulation of the anesthetized site with a needle six times at regular intervals. The number of reactions (score value) was counted. In this statistical model, the probability of reacting to needle stimulation was calculated using the elapsed time, type of local anesthetic agent, and presence or absence of adrenaline. Score values were assumed to follow a binomial distribution at the calculated probability. Parameters were estimated using the Bayesian hierarchical model and Hamiltonian Monte Carlo method. The predicted curves using the estimated parameters fitted well the observed animal values. When score values were predicted using randomly generated parameters, the median of duration was similar between animal experiments and simulations (Procaine: 55 min vs. 50 min, Lidocaine: both 60 min, and Mepivacaine: both 85 min). This approach effectively modeled the effects of local anesthetic agents. It is possible to create the simulator using the parameter values estimated in this study. [ABSTRACT FROM AUTHOR]

Demographic projections contain uncertainty and probabilistic models aim to quantify it. This paper develops probabilistic projections of Life Expectancy at birth for Argentina for the period 2025-2100 based on the Bayesian Hierarchical Model. Raftery et. to the. (2013) develop a model that allows estimating the rate of increase in life expectancy of a country using previous data from that country, and taking into account past patterns observed in all other countries. Then Raftery (2014) uses the female gap model to develop life expectancy in males. For this purpose, three adjustment periods are evaluated and the results are analyzed based on the selection of the most appropriate one; life expectancy for men and women, its trend and the gap between the two. Finally, the estimated results are compared with the official data from the national statistical agency. The BHM presents plausible results together with a measure of associated un-certainty that allows deciding until when the forecasts would be reliable or reportable, however, the base period includes projections as input data, generating a paradox of using projections to project. The difference that occurs in the projections for men shows the importance in the model of including or not the last point of the data series. Although the probabilistic models represent great advances, details remain to be discussed, such as the base period and its relationship with official sources (whether estimates or projections.

Many problems in marketing and economics require firms to make targeted consumer-specific decisions, but current estimation methods are not designed to scale to the size of modern data sets. In this article, the authors propose a new algorithm to close that gap. They develop a distributed Markov chain Monte Carlo (MCMC) algorithm for estimating Bayesian hierarchical models when the number of consumers is very large and the objects of interest are the consumer-level parameters. The two-stage and embarrassingly parallel algorithm is asymptotically unbiased in the number of consumers, retains the flexibility of a standard MCMC algorithm, and is easy to implement. The authors show that the distributed MCMC algorithm is faster and more efficient than a single-machine algorithm by at least an order of magnitude. They illustrate the approach with simulations with up to 100 million consumers, and with data on 1,088,310 donors to a charitable organization. The algorithm enables an increase of between $1.6 million and $4.6 million in additional donations when applied to a large modern-size data set compared with a typical-size data set. [ABSTRACT FROM AUTHOR]

Recent innovation in trial design to improve study efficiency has led to the development of basket trials in which a single therapeutic treatment is tested on several patient populations, each of which forms a basket. In a common setting, patients across all baskets share a genetic marker and as such, an assumption can be made that all patients may have a homogeneous response to treatments. Bayesian information borrowing procedures utilize this assumption to draw on information regarding the response in one basket when estimating the response rate in others. This can improve power and precision of estimates particularly in the presence of small sample sizes, however, can come at a cost of biased estimates and an inflation of error rates, bringing into question validity of trial conclusions. We review and compare the performance of several Bayesian borrowing methods, namely: the Bayesian hierarchical model (BHM), calibrated Bayesian hierarchical model (CBHM), exchangeability‐nonexchangeability (EXNEX) model and a Bayesian model averaging procedure. A generalization of the CBHM is made to account for unequal sample sizes across baskets. We also propose a modification of the EXNEX model that allows for better control of a type I error. The proposed method uses a data‐driven approach to account for the homogeneity of the response data, measured through Hellinger distances. Through an extensive simulation study motivated by a real basket trial, for both equal and unequal sample sizes across baskets, we show that in the presence of a basket with a heterogeneous response, unlike the other methods discussed, this model can control type I error rates to a nominal level whilst yielding improved power. [ABSTRACT FROM AUTHOR]

Morbidities generally show patterns of concentration that vary by space and time. Disease mapping models are useful in estimating the spatiotemporal patterns of disease risks and are therefore pivotal for effective disease surveillance, resource allocation, and the development of prevention strategies. This study considers six spatiotemporal Bayesian hierarchical models based on two spatial conditional autoregressive priors. It could serve as a guideline on the development and application of Bayesian hierarchical models to assess the emerging risk trends, risk clustering, and spatial inequality trends, with estimation of covariables' effects on the interested disease risk. The method is applied to the Florida Birth Record data between 2006 and 2015 to study two cardiovascular risk factors: preeclampsia and gestational diabetes. High-risk clusters were detected in North Central Florida for preeclampsia and in Central Florida for gestational diabetes. While the adjusted disease trend was stable, spatial inequality peaked in 2011–2012 for both diseases. Exposure to PM2.5 at first or/and second trimester increased the risk of preeclampsia and gestational diabetes, but the magnitude is less severe compared to previous studies. In conclusion, this study underscores the significance of selecting appropriate disease mapping models in estimating the intricate spatiotemporal patterns of disease risk and suggests the importance of localized interventions to reduce health disparities. The result also identified an opportunity to study potential risk factors of preeclampsia, as the spike of risk in North Central Florida cannot be explained by current covariables. [ABSTRACT FROM AUTHOR]

Simple Summary: Foodborne salmonellosis remains one of the top zoonotic diseases affecting public health worldwide, and its incidence has remained stable in the last years in the European Union (EU) triggering questions on the usefulness of currently available measures to prevent its occurrence. A main focus of Salmonella national control programs is monitoring the presence of the bacteria in animal reservoirs, especially in poultry, and for this reason, thousands of samples are collected every year in poultry farms in EU countries, but the importance of certain factors in the probability of detecting Salmonella remains poorly understood. A thorough analysis conducted on data collected in all laying hen flocks sampled in Spain in 2015–2020 revealed that even though the presence of Salmonella was rare (<3.5% of positive sampling events), when samples were collected in certain months (fall–winter) and housing systems (caged flocks) and by competent authorities (as opposed to food business operators), the probability of detecting Salmonella increased significantly. These results demonstrate that the sensitivity of the sampling strategy may be influenced by how and when samples are collected and that certain flocks may be at an increased risk of infection. Trends in Salmonella human infections are assumed to be related to the distribution of the pathogen in the animal reservoir/food products, and cases in humans are most often linked to poultry and poultry products (eggs, meat). Therefore, ongoing Salmonella national control programs (NCPs) in European Union Member States have the objective of monitoring and reducing its prevalence in commercial poultry flocks. Results from NCPs have shown certain factors (housing systems, season of sampling and if sampling is conducted by food business operators (FBOps) or competent authorities (CAs), among others) can influence detection rates, but associations are often not consistent. Here, we analyzed data from the Spanish NCP on 7216 laying hen flocks subjected to 36,193 sampling events over a six-year period to characterize its performance and identify variables influencing detection rates. Overall, 1205 sampling events were positive for Salmonella spp. (any serovar) and 132 for S. Enteritidis-S. Typhimurium/monophasic. Bayesian multivariable models adjusting for multiple covariates concluded that sampling events later in the year, in caged flocks with older animals and conducted by CAs had increased odds of positivity for Salmonella spp., revealing aspects linked with a differential estimation of Salmonella levels in laying hen flocks. [ABSTRACT FROM AUTHOR]

Real estate valuation is typically based on hedonic regression models where the expected price of a property is explained in dependence of its attributes. However, investors in the housing market are equally interested in the distribution of real estate market values (including price variation), that is, determining the impact of attributes of a property on the entire conditional distribution. We therefore consider Bayesian structured additive distributional and quantile regression models for real estate valuation. In the first approach, each parameter of a potentially complex parametric response distribution is related to a structured additive predictor. In contrast, the second approach proceeds differently and models arbitrary quantiles of the response distribution directly and nonparametrically. Both models presented are based on a multilevel version of structured additive regression thereby utilizing the typical hierarchical structure of real estate data. We demonstrate the proposed methodology within a detailed case study based on more than 3 000 owner-occupied single family homes in Austria, discuss interpretation of the resulting effect estimates, and compare models based on their predictive ability. [ABSTRACT FROM AUTHOR]

In the field of ecological epidemiological studies, accurate estimation of the long-term health effects of air pollution is crucial. Two-step models that involve exposure assessment and health effects estimation are often used for this purpose. However, the accuracy of exposure assessment is uncertain and may not accurately reflect true exposure. Despite several proposed methods to manage this uncertainty, the impact of different approaches on air pollution inferences remains uncertain. In this study, we conduct a simulation study to compare the inferences of air pollution impact from various exposure uncertainty propagation models while investigating their health effects. The results suggest that the Without-uncertainty model and the Multi-set method are preferable to the prior method and pollution-health jointly model (without cut-off). Moreover, a case study further reinforces the evidence of a link between mortality and PM 2.5 concentrations, showing that an increase of 1 μ g ⋅ m − 3 in PM 2.5 concentration is likely to increase all-cause deaths in Scotland by 4.51% [95% credible interval (CI), 3.42%, 5.49%] to 7.51% (95% CI, 6.28%, 8.80%). These findings have important implications for policymakers and public health officials seeking to mitigate the harmful effects of air pollution. [ABSTRACT FROM AUTHOR]

Porosity is one of the most critical quality issues in Additive Manufacturing (AM). As process parameters are closely related to porosity formation, it is vitally important to study their relationship for better process optimization. In this article, motivated by the emerging application of metal AM, a three-level hierarchical mixed-effects modeling approach is proposed to characterize the relationship between microstructural images and process parameters for porosity prediction and microstructure reconstruction. Specifically, a Two-Point Correlation Function (TPCF) is used to capture the morphology of the pores quantitatively. Then, the relationship between the TPCF profile and process parameters is established. A blocked Gibbs sampling approach is developed for parameter inference. Our modeling framework can reconstruct the microstructure based on the predicted TPCF through a simulated annealing optimization algorithm. The effectiveness and advantageous features of our method are demonstrated by both the simulation study and the case study with real-world data from metal AM applications. [ABSTRACT FROM AUTHOR]

Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, 'gap‐filling' approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage. Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi‐ and multivariate) and (3) taxonomic and functional clustering (valuewise, uni‐ and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set–external trait data had little effect on induced taxonomic clustering and stabilized trait–trait correlations. Main Conclusions: Our study extends the criteria for the evaluation of gap‐filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation. [ABSTRACT FROM AUTHOR]

A statistical study was made of the temporal trend in extreme temperatures in the region of Extremadura (Spain) during the period 1981–2015 using a Regional Climate Model. For this purpose, a Weather Research and Forecasting (WRF) Regional Climate Model extreme temperature dataset was obtained. This dataset was then subjected to a statistical study using a Bayesian hierarchical spatio-temporal model with a Generalized Extreme Value (GEV) parametrization of the extreme data. The Bayesian model was implemented in a Markov chain Monte Carlo framework that allows the posterior distribution of the parameters that intervene in the model to be estimated. The role of the altitude dependence of the temperature was considered in the proposed model. The results for the spatial-trend parameter lend confidence to the model since they are consistent with the dry adiabatic gradient. Furthermore, the statistical model showed a slight negative trend for the location parameter. This unexpected result may be due to the internal and modeling uncertainties in the WRF model. The shape parameter was negative, meaning that there is an upper bound for extreme temperatures in the model. [ABSTRACT FROM AUTHOR]

We present a nonparametric Bayesian hierarchical (NBH) model and develop a variational approximation (VA) algorithm for the curve fitting of the functional radiation response data. The NBH model is based on a Bayesian hierarchical (BH) model with a Gaussian-Inverse Wishart process (G-IWP) prior, which simultaneously smooths multiple functional observations and estimates mean-covariance functions. We use the automatic differentiation variational inference (ADVI) algorithm with a Gaussian distribution as the variational distribution for approximating the posterior distribution of parameters of interest, which is applicable to a wide class of probabilistic models and can also be implemented in Stan (a probabilistic programming system). Using the NBH model and the Gaussian ADVI algorithm, we fit a dataset for the semiconductor obtained from the radiation response map (RRM) of South Korea.

Abstract Background Nigeria is among the top five countries in the world with the highest under-five mortality rates. In addition to the general leading causes of under-five mortality, studies have shown that disparity in sociocultural values and practices across ethnic groups in Nigeria influence child survival, thus there is a need for scientific validation. This study quantified the survival probabilities and the impact of socioeconomic and demographic factors, proximate and biological determinants, and environmental factors on the risk of under-five mortality in Nigeria. Methods The Kaplan-Meier survival curve, Nelson Aalen hazard curve, and components survival probabilities were estimated. The Exponential, Gamma, Log-normal, Weibull, and Cox hazard models in a Bayesian mixed effect hierarchical hazard modeling framework with spatial components were considered, and the Deviance and Watanabe Akaike information criteria were used to select the best model for inference. A $$5\%$$ 5 % level of significance was assumed throughout this work. The 2018 Nigeria Demographic and Health Survey dataset was used, and the outcome variable was the time between birth and death or birth and the date of interview for children who were alive on the day of the interview. Results Findings show that the probability of a child dying within the first two months is 0.04, and the probability of a boy child dying before attaining age five is 0.106, while a girl child is 0.094 probability. Gender, maternal education, household wealth status, source of water and toilet facility, residence, mass media, frequency of antenatal and postnatal visits, marital status, place of delivery, multiple births, who decide healthcare use, use of bednet are significant risk factors of child mortality in Nigeria. The mortality risk is high among the maternal age group below 24 and above 44years, and birth weight below 2.5Kg and above 4.5Kg. The under-five mortality risk is severe in Kebbi, Kaduna, Jigawa, Adamawa, Gombe, Kano, Kogi, Nasarawa, Plateau, and Sokoto states in Nigeria. Conclusion This study accentuates the need for special attention for the first two months after childbirth as it is the age group with the highest expected mortality. A practicable way to minimize death in the early life of children is to improve maternal healthcare service, promote maternal education, encourage delivery in healthcare facilities, positive parental attitude to support multiple births, poverty alleviation programs for the less privileged, and a prioritized intervention to Northern Nigeria.

Climate reconstructions derived from proxy records for individual sites often fail to incorporate existing regional information, which may help to determine uncertainties and express variability within specific reconstructions or commonalities between datasets. Such an understanding is crucial when examining past human-environment interactions. Taking the eastern Mediterranean as our case study, we present here a new air temperature model for the last 10,000 years that utilises data from 33 previously published proxy-based independent reconstructions, using a novel fully Bayesian approach that applies multilevel regression models of individual temperature datasets grouped together into 300 year long consecutive sub-intervals. A Bayesian multilevel approach allows the model to share information between each regression model from the individual datasets and the 300 year grouped regression models. The results demonstrate commonalities between individual datasets derived from different sources, and embed the uncertainties within the model. Our results establish that the eastern Mediterranean region was consistently warmer than the 20th century, except for two short intervals at the end of the Early Holocene (between 8400 and 8250 yrs BP) and the start of the mid-Holocene (between 7800 and 7650 yrs BP). We also identify changes within temperature associated with both the 8.2 ka and 4.2 ka BP events, however our findings identify regional warming in the eastern Mediterranean, rather than cooling often associated with the 4.2 ka BP event. Our results are comparable with previous large scale hemispheric reconstructions, demonstrating that our model is a robust candidate for temperature reconstructions within a confined region, which can range from mesoscale up to macroscale. [ABSTRACT FROM AUTHOR]

Freeway crash prediction models are the basic of traffic safety research, yet crash occurrence and the influencing factors change over time. In order to make sure the implemented safety models fit the current traffic environment, this study conducts a comparative analysis of 2017 and 2020 datasets collected from freeways in Suzhou, China. Considering the spatial correlation among analysis units and the hierarchical data structure, a Bayesian conditional autoregressive negative binomial (CAR-NB) model and a Bayesian hierarchical CAR-NB (HCAR-NB) model were used to explore the safety influencing factors, and a traditional NB model was developed for further comparison. To update the HCAR-NB model from 2017 to 2020, Bayesian inference with informative priors was used to improve its goodness of fit and efficiency. Preliminary results showed that 1) the HCAR-NB model outperformed the NB model and CAR-NB model in prediction accuracy, and 2) the number of crashes was significantly correlated with average speed, speed variance, road segment length, number of lanes, and presence of ramps. The potential for safety improvement (PSI) method was applied to the modeling results to identify hotspots for the two years. The results confirmed that the hotspots spatiotemporally shifted among the freeways. The proposed crash prediction model and updating method are expected to assist implementation of informed countermeasures for freeway safety improvement. [ABSTRACT FROM AUTHOR]

We develop a space‐time Bayesian hierarchical modeling (BHM) framework for two flood risk attributes—seasonal daily maximum flow and the number of events that exceed a threshold during a season (NEETM)—at a suite of gauge locations on a river network. The model uses generalized extreme value (GEV) and Poisson distributions as marginals for these flood attributes with non‐stationary parameters. The rate parameters of the Poisson distribution and location, scale, and shape parameters of the GEV are modeled as linear functions of suitable covariates. Gaussian copulas are applied to capture the spatial dependence. The best covariates are selected using the Watanabe‐Akaike information criterion (WAIC). The modeling framework results in the posterior distribution of the flood attributes at all the gauges and various lead times. We demonstrate the utility of this modeling framework to forecast the flood risk attributes during the summer peak monsoon season (July‐August) at five gauges in the Narmada River basin (NRB) of West‐Central India for several lead times (0–3 months). As potential covariates, we consider climate indices such as El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Warm Pool Region (PWPR) from antecedent seasons, which have shown strong teleconnections with the Indian monsoon. We also include new indices related to the East Pacific and West Indian Ocean regions depending on the lead times. We show useful long lead skill from this modeling approach which has a strong potential to enable robust risk‐based flood mitigation and adaptation strategies 3 months before flood occurrences. Key Points: Develop a Bayesian Hierarchical Model framework for forecasting the magnitude and frequency of seasonal streamflow extremes in a river basinThe model provides ensembles forecasts of seasonal flood risk attributes at multiple lead timesApplied this for the first time in India on the Narmada River Basin using large‐scale climate indices as covariates at 0–3 months ahead [ABSTRACT FROM AUTHOR]

Context: Small population sizes and no possibility of metapopulation rescue put narrowly distributed endemic species under elevated risk of extinction from anthropogenic change. Desert spring wetlands host many endemic species that require aquatic habitat and are isolated by the surrounding xeric terrestrial habitat. Aims: We sought to model the occupancy dynamics of the Dixie Valley toad (Anaxyrus williamsi), a recently described species endemic to a small desert spring wetland complex in Nevada, USA. Methods: We divided the species' range into 20 m × 20 m cells and surveyed for Dixie Valley toads at 60 cells during six primary periods from 2018 to 2021, following an occupancy study design. We analysed our survey data by using a multi-state dynamic occupancy model to estimate the probability of adult occurrence, colonisation, site survival, and larval occurrence and the relationship of each to environmental covariates. Key results: The detection probabilities of adult and larval toads were affected by survey length and time of day. Adult Dixie Valley toads were widely distributed, with detections in 75% of surveyed cells at some point during the 3-year study, whereas larvae were observed only in 20% of cells during the study. Dixie Valley toad larvae were more likely to occur in cells far from spring heads with a high coverage of surface water, low emergent vegetation cover, and water temperatures between 20°C and 28°C. Adult toads were more likely to occur in cells with a greater coverage of surface water and water depth >10 cm. Cells with more emergent vegetation cover and surface water were more likely to be colonised by adult toads. Conclusions: Our results showed that Dixie Valley toads are highly dependent on surface water in both spring and autumn. Adults and larvae require different environmental conditions, with larvae occurring farther from spring heads and in fewer cells. Implications: Disturbances to the hydrology of the desert spring wetlands in Dixie Valley could threaten the persistence of this narrowly distributed toad. Desert springs and their unique fauna are under threat worldwide from climate change, invasive species, and human water use. We studied the occupancy dynamics of the endemic Dixie Valley toad in Nevada, USA, and found that adult and larval toads required different environmental conditions. Our study showed that both life stages of this species are highly dependent on aquatic habitat in their desert environment. U.S. Geological Survey photograph by Kelsey Ruehling. [ABSTRACT FROM AUTHOR]

In the era of targeted therapies and immunotherapies, the traditional drug development paradigm of testing one drug at a time in one indication has become increasingly inefficient. Motivated by a real‐world application, we propose a master‐protocol–based Bayesian platform trial design with mixed endpoints (PDME) to simultaneously evaluate multiple drugs in multiple indications, where different subsets of efficacy measures (eg, objective response and landmark progression‐free survival) may be used by different indications as single or multiple endpoints. We propose a Bayesian hierarchical model to accommodate mixed endpoints and reflect the trial structure of indications that are nested within treatments. We develop a two‐stage approach that first clusters the indications into homogeneous subgroups and then applies the Bayesian hierarchical model to each subgroup to achieve precision information borrowing. Patients are enrolled in a group‐sequential way and adaptively assigned to treatments according to their efficacy estimates. At each interim analysis, the posterior probabilities that the treatment effect exceeds prespecified clinically relevant thresholds are used to drop ineffective treatments and "graduate" effective treatments. Simulations show that the PDME design has desirable operating characteristics compared to existing method. [ABSTRACT FROM AUTHOR]

Climate and land use change are two of the primary threats to global biodiversity; however, each species within a community may respond differently to these facets of global change. Although it is typically assumed that species use the habitat that is advantageous for survival and reproduction, anthropogenic changes to the environment can create ecological traps, making it critical to assess both habitat selection (e.g. where species congregate on the landscape) and the influence of selected habitats on the demographic processes that govern population dynamics.We used a long‐term (1958–2011), large‐scale, multi‐species dataset for waterfowl that spans the United States and Canada to estimate species‐specific responses to climate and land use variables in a landscape that has undergone significant environmental change across space and time. We first estimated the effects of change in climate and land use variables on habitat selection and population dynamics for nine species. We then hypothesized that species‐specific responses to environmental change would scale with life‐history traits, specifically: longevity, nesting phenology and female breeding site fidelity.We observed species‐level heterogeneity in the demographic and habitat selection responses to climate and land use change, which would complicate community‐level habitat management. Our work highlights the importance of multi‐species monitoring and community‐level analysis, even among closely related species.We detected several relationships between life‐history traits, particularly nesting phenology, and species' responses to environmental change. One species, the early‐nesting northern pintail (Anas acuta), was consistently at the extreme end of responses to land use and climate predictors and has been a species of conservation concern since their population began to decline in the 1980s. They, and the blue‐winged teal, also demonstrated a positive habitat selection response to the proportion of cropland on the landscape that simultaneously reduced abundance the following year, indicative of susceptibility to ecological traps.By distilling the diversity of species' responses to environmental change within a community, our methodological approach and findings will help improve predictions of community responses to global change and can inform multi‐species management and conservation plans in dynamic landscapes that are based on simple tenets of life‐history theory. [ABSTRACT FROM AUTHOR]

Several penalization approaches have been developed to identify homogeneous subgroups based on a regression model with subject‐specific intercepts in subgroup analysis. These methods often apply concave penalty functions to pairwise comparisons of the intercepts, such that the subjects with similar intercept values are assigned to the same group, which is very similar to the procedure of the penalization approaches for variable selection. Since the Bayesian methods are commonly used in variable selection, it is worth considering the corresponding approaches to subgroup analysis in the Bayesian framework. In this paper, a Bayesian hierarchical model with appropriate prior structures is developed for the pairwise differences of intercepts based on a regression model with subject‐specific intercepts, which can automatically detect and identify homogeneous subgroups. A Gibbs sampling algorithm is also provided to select the hyperparameter and estimate the intercepts and coefficients of the covariates simultaneously, which is computationally efficient for pairwise comparisons compared to the time‐consuming procedures for parameter estimation of the penalization methods (e.g., alternating direction method of multiplier) in the case of large sample sizes. The effectiveness and usefulness of the proposed Bayesian method are evaluated through simulation studies and analysis of a Cleveland Heart Disease Dataset. [ABSTRACT FROM AUTHOR]

Background: The intracluster correlation coefficient is a key input parameter for sample size determination in cluster-randomised trials. Sample size is very sensitive to small differences in the intracluster correlation coefficient, so it is vital to have a robust intracluster correlation coefficient estimate. This is often problematic because either a relevant intracluster correlation coefficient estimate is not available or the available estimate is imprecise due to being based on small-scale studies with low numbers of clusters. Misspecification may lead to an underpowered or inefficiently large and potentially unethical trial. Methods: We apply a Bayesian approach to produce an intracluster correlation coefficient estimate and hence propose sample size for a planned cluster-randomised trial of the effectiveness of a systematic voiding programme for post-stroke incontinence. A Bayesian hierarchical model is used to combine intracluster correlation coefficient estimates from other relevant trials making use of the wealth of intracluster correlation coefficient information available in published research. We employ knowledge elicitation process to assess the relevance of each intracluster correlation coefficient estimate to the planned trial setting. The team of expert reviewers assigned relevance weights to each study, and each outcome within the study, hence informing parameters of Bayesian modelling. To measure the performance of experts, agreement and reliability methods were applied. Results: The 34 intracluster correlation coefficient estimates extracted from 16 previously published trials were combined in the Bayesian hierarchical model using aggregated relevance weights elicited from the experts. The intracluster correlation coefficients available from external sources were used to construct a posterior distribution of the targeted intracluster correlation coefficient which was summarised as a posterior median with a 95% credible interval informing researchers about the range of plausible sample size values. The estimated intracluster correlation coefficient determined a sample size of between 450 (25 clusters) and 480 (20 clusters), compared to 500–600 from a classical approach. The use of quantiles, and other parameters, from the estimated posterior distribution is illustrated and the impact on sample size described. Conclusion: Accounting for uncertainty in an unknown intracluster correlation coefficient, trials can be designed with a more robust sample size. The approach presented provides the possibility of incorporating intracluster correlation coefficients from various cluster-randomised trial settings which can differ from the planned study, with the difference being accounted for in the modelling. By using expert knowledge to elicit relevance weights and synthesising the externally available intracluster correlation coefficient estimates, information is used more efficiently than in a classical approach, where the intracluster correlation coefficient estimates tend to be less robust and overly conservative. The intracluster correlation coefficient estimate constructed is likely to produce a smaller sample size on average than the conventional strategy of choosing a conservative intracluster correlation coefficient estimate. This may therefore result in substantial time and resources savings. [ABSTRACT FROM AUTHOR]

Rare binary events data arise frequently in medical research. Due to lack of statistical power in individual studies involving such data, meta‐analysis has become an increasingly important tool for combining results from multiple independent studies. However, traditional meta‐analysis methods often report severely biased estimates in such rare‐event settings. Moreover, many rely on models assuming a pre‐specified direction for variability between control and treatment groups for mathematical convenience, which may be violated in practice. Based on a flexible random‐effects model that removes the assumption about the direction, we propose new Bayesian procedures for estimating and testing the overall treatment effect and inter‐study heterogeneity. Our Markov chain Monte Carlo algorithm employs Pólya‐Gamma augmentation so that all conditionals are known distributions, greatly facilitating computational efficiency. Our simulation shows that the proposed approach generally reports less biased and more stable estimates compared to existing methods. We further illustrate our approach using two real examples, one using rosiglitazone data from 56 studies and the other using stomach ulcers data from 41 studies. [ABSTRACT FROM AUTHOR]

Basket trials evaluate a single drug targeting a single genetic variant in multiple cancer cohorts. Empirical findings suggest that treatment efficacy across baskets may be heterogeneous. Most modern basket trial designs use Bayesian methods. These methods require the prior specification of at least one parameter that permits information sharing across baskets. In this study, we provide recommendations for selecting a prior for scale parameters for adaptive basket trials by using Bayesian hierarchical modeling. Heterogeneity among baskets attracts much attention in basket trial research, and substantial heterogeneity challenges the basic assumption of exchangeability of Bayesian hierarchical approach. Thus, we also allowed each stratum‐specific parameter to be exchangeable or nonexchangeable with similar strata by using data observed in an interim analysis. Through a simulation study, we evaluated the overall performance of our design based on statistical power and type I error rates. Our research contributes to the understanding of the properties of Bayesian basket trial designs. [ABSTRACT FROM AUTHOR]

We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity. [ABSTRACT FROM AUTHOR]

Human learning has been characterized by three robust effects (i.e.power law of learning, power law of decay, and spacing), whichhave been validated across multiple domains and time intervals. Toaccount for these different effects mathematical model of learningand retention have been developed. These models hold a great dealof potential for application a wide range of educational and trainingscenarios. However, many models are not validated according fortheir ability to make accurate predictions of human performance.The predictive demand of these models is made increasinglycomplex by the needs of training domain, needing both to predictboth skill decay and reacquisition from little historical data. In thispaper, we examine the predictive capability of the PredictivePerformance Equation (PPE) implemented in a Bayesianhierarchical model. Through a comparison of two Bayesianhierarchical models we show how hierarchical model fit to aparticipant’s performance across a set of items compared to only asingle item improves PPE’s predictive accuracy of both skill decayand reacquisition over multiple learning schedules

Certain general facets of biotic response to climate change, such as shifts in phenology and geographic distribution, are well characterized; however, it is not clear whether the observed similarity of responses across taxa will extend to variation in other population-level processes. We examined population response to climatic variation using long-term incidence data (collected over 42 years) encompassing 149 butterfly species and considerable habitat diversity (10 sites along an elevational gradient from sea level to over 2,700 m in California). Population responses were characterized by extreme heterogeneity that was not attributable to differences in species composition among sites. These results indicate that habitat heterogeneity might be a buffer against climate change and highlight important questions about mechanisms maintaining interpopulation differences in responses to weather. Despite overall heterogeneity of response, population dynamics were accurately predicted by our model for many species at each site. However, the overall correlation between observed and predicted incidence in a cross validation analysis was moderate (Pearson's r = 0.23, SE 0.01), and 97% of observed data fell within the predicted 95% credible intervals. Prediction was most successful for more abundant species as well as for sites with lower annual turnover. Population-level heterogeneity in response to climate variation and the limits of our predictive power highlight the challenges for a future of increasing climatic variability.

Abstract Background Smoking is one of the leading causes of preventable mortality and morbidity worldwide, with the European Region having the highest prevalence of tobacco smoking among adults compared to other WHO regions. The Belgian Health Interview Survey (BHIS) provides a reliable source of national and regional estimates of smoking prevalence; however, currently there are no estimates at a smaller geographical resolution such as the municipality scale in Belgium. This hinders the estimation of the spatial distribution of smoking attributable mortality at small geographical scale (i.e., number of deaths that can be attributed to tobacco). The objective of this study was to obtain estimates of smoking prevalence in each Belgian municipality using BHIS and calculate smoking attributable mortality at municipality level. Methods Data of participants aged 15 + on smoking behavior, age, gender, educational level and municipality of residence were obtained from the BHIS 2018. A Bayesian hierarchical Besag-York-Mollie (BYM) model was used to model the logit transformation of the design-based Horvitz-Thompson direct prevalence estimates. Municipality-level variables obtained from Statbel, the Belgian statistical office, were used as auxiliary variables in the model. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA). Deviance Information Criterion (DIC) and Conditional Predictive Ordinate (CPO) were computed to assess model fit. Population attributable fractions (PAF) were computed using the estimated prevalence of smoking in each of the 589 Belgian municipalities and relative risks obtained from published meta-analyses. Smoking attributable mortality was calculated by multiplying PAF with age-gender standardized and stratified number of deaths in each municipality. Results BHIS 2018 data included 7,829 respondents from 154 municipalities. Smoothed estimates for current smoking ranged between 11% [Credible Interval 3;23] and 27% [21;34] per municipality, and for former smoking between 4% [0;14] and 34% [21;47]. Estimates of smoking attributable mortality constituted between 10% [7;15] and 47% [34;59] of total number of deaths per municipality. Conclusions Within-country variation in smoking and smoking attributable mortality was observed. Computed estimates should inform local public health prevention campaigns as well as contribute to explaining the regional differences in mortality.

Abstract Objective The outbreak of the novel coronavirus disease 2019 (COVID-19) is still affecting African countries. The pandemic presents challenges on how to measure governmental, and community responses to the crisis. Beyond health risks, the socio-economic implications of the pandemic motivated us to examine the transmission dynamics of COVID-19 and the impact of non-pharmaceutical interventions (NPIs). The main objective of this study was to assess the impact of BCG vaccination and NPIs enforced on COVID-19 case-death-recovery counts weighted by age-structured population in Ethiopia, Kenya, and Rwanda. We applied a semi-mechanistic Bayesian hierarchical model (BHM) combined with Markov Chain Monte Carlo (MCMC) simulation to the age-structured pandemic data obtained from the target countries. Results The estimated mean effective reproductive number (Rt) for COVID-19 was 2.50 (C1: 1.99–5.95), 3.51 (CI: 2.28–7.28) and 3.53 (CI: 2.97–5.60) in Ethiopia, Kenya and Rwanda respectively. Our results indicate that NPIs such as lockdowns, and curfews had a large effect on reducing Rt. Current interventions have been effective in reducing Rt and thereby achieve control of the epidemic. Beyond age-structure and NPIs, we found no significant association between COVID-19 and BCG vaccine-induced protection. Continued interventions should be strengthened to control transmission of SARS-CoV-2.

Spatial point process models are theoretically useful for mapping discrete events, such as plant or animal presence, across space; however, the computational complexity of fitting these models is often a barrier to their practical use. The log-Gaussian Cox process (LGCP) is a point process driven by a latent Gaussian field, and recent advances have made it possible to fit Bayesian LGCP models using approximate methods that facilitate rapid computation. These advances include the integrated nested Laplace approximation (INLA) with a stochastic partial differential equations (SPDE) approach to sparsely approximate the Gaussian field and an extension using pseudodata with a Poisson response. To help link the theoretical results to statistical practice, we provide an overview of INLA for point process data and then illustrate their implementation using freely available data. The analyzed datasets include both a completely observed spatial field and an incomplete data situation. Our well-commented R code is shared in the online supplement. Our intent is to make these methods accessible to the practitioner of spatial statistics without requiring deep knowledge of point process theory. [ABSTRACT FROM AUTHOR]

In meta-analysis practice, researchers frequently face studies that report the same outcome differently, such as a continuous variable (e.g., scores for rating depression) or a binary variable (e.g., counts of patients with depression dichotomized by certain latent and unreported depression scores). For combining these two types of studies in the same analysis, a simple conversion method has been widely used to handle standardized mean differences (SMDs) and odds ratios (ORs). This conventional method uses a linear function connecting the SMD and log OR; it assumes logistic distributions for (latent) continuous measures. However, the normality assumption is more commonly used for continuous measures, and the conventional method may be inaccurate when effect sizes are large or cutoff values for dichotomizing binary events are extreme (leading to rare events). This article proposes a Bayesian hierarchical model to synthesize SMDs and ORs without using the conventional conversion method. This model assumes exact likelihoods for continuous and binary outcome measures, which account for full uncertainties in the synthesized results. We performed simulation studies to compare the performance of the conventional and Bayesian methods in various settings. The Bayesian method generally produced less biased results with smaller mean squared errors and higher coverage probabilities than the conventional method in most cases. Nevertheless, this superior performance depended on the normality assumption for continuous measures; the Bayesian method could lead to nonignorable biases for non-normal data. In addition, we used two case studies to illustrate the proposed Bayesian method in real-world settings. [ABSTRACT FROM AUTHOR]

For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I + cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development. [ABSTRACT FROM AUTHOR]

We propose a new solution under the Bayesian framework to simultaneously estimate mean-based asynchronous changepoints in spatially correlated functional time series. Unlike previous methods that assume a shared changepoint at all spatial locations or ignore spatial correlation, our method treats changepoints as a spatial process. This allows our model to respect spatial heterogeneity and exploit spatial correlations to improve estimation. Our method is derived from the ubiquitous cumulative sum (CUSUM) statistic that dominates changepoint detection in functional time series. However, instead of directly searching for the maximum of the CUSUM-based processes, we build spatially correlated two-piece linear models with appropriate variance structure to locate all changepoints at once. The proposed linear model approach increases the robustness of our method to variability in the CUSUM process, which, combined with our spatial correlation model, improves changepoint estimation near the edges. We demonstrate through extensive simulation studies that our method outperforms existing functional changepoint estimators in terms of both estimation accuracy and uncertainty quantification, under either weak or strong spatial correlation, and weak or strong change signals. Finally, we demonstrate our method using a temperature data set and a coronavirus disease 2019 (COVID-19) study. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Frequency analysis of tornadoes is very important for risk analysis and disaster control. In this paper, the annual frequency of tornadoes that occurred in the United States from 1967 to 2016 is analyzed. The simple analysis shows that frequencies of tornadoes of different sites are spatially correlated and over-dispersed. To explain the two characteristics of the data, the Bayesian hierarchical model is proposed. For comparison purposes, the Bayesian model with negative binomial distribution, Poisson distribution, Polya distribution, and first-order, non-negative, integer-valued autoregressive model with Bell innovations(BL-INAR(1)) are considered to fit the frequency of tornado. The distribution parameters of all sites are assumed to be spatially correlated, and the corresponding Bayesian hierarchical models were established. MCMC (Markov Chain Monte Carlo) method is applied to parameter estimations and relative statistical inference. By comparison of the analysis results, the negative binomial distribution is recommended to analyze the overdispersion and spatial correlation among the sites of the data. The comparison between the simulated frequencies based on the proposed model and the actual frequencies also verifies that the proposed method is a better model for the data. [ABSTRACT FROM AUTHOR]