This pilot randomized controlled trial protocol aims to (1) assess the impact on the wellbeing of Syrian refugee young adults (18-24 years) of being a community mental health worker (CMHW) implementing WHO\'s evidence-based psychosocial intervention - Problem Management Plus (PM+) - with adults in their community, and (2) identify the mechanisms associated with the outcomes of enhanced wellbeing and coping, and reduced stress among these CMHWs. Over 108 million people have been forcibly displaced as of the end of 2022. Mental health consequences of these displacements are significant, yet human resources for health are not sufficient to meet the needs. A large proportion of refugee populations are youth and young adults (YA). Evidence indicates their engagement in supporting their communities leads to their own enhanced wellbeing and that of their community. This trial trains Syrian refugees to serve their communities as CMHW (n=19) or tutors (n=19) and compare wellbeing, stress and coping outcomes between these two groups and a control group (n = 40). We will also assess 7 mechanisms as potential pathways for the interventions to influence outcomes. Surveys will assess outcomes and mechanisms, hair samples will measure stress cortisol. The primary analysis will use a Bayesian Hierarchical Model approach to model the trajectories of the mechanisms and primary study endpoints over time for individuals in each of the arms. Our results will elucidate critical mechanisms in which engagement of young adults to support their community enhances their own wellbeing.
UNASSIGNED: National Institutes of Mental Health, NCT05265611, Registered prospectively in 2021.
UNASSIGNED: LBCTR2023015206, Registered in 2023.

In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the treatment effect in the overall population. A Bayesian hierarchical model (BHM) can be used to derive more precise, and less heterogenous estimates of subgroup treatment effects that are closer to the treatment effect in the overall population. BHM assumes exchangeability in treatment effect across subgroups after adjusting for effect modifiers and other relevant covariates. In this article, we will discuss the technical details for applying one-way and multi-way BHM using summary-level statistics, and patient-level data for subgroup analysis. Four case studies based on four new drug applications are used to illustrate the application of these models in subgroup analyses for continuous, dichotomous, time-to-event, and count endpoints.

Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modeling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. We encode such information as candidate auxiliary variables using a spike-and-slab submodel on the propensity of nodes to be hubs, which allows hypothesis-free selection and interpretation of a sparse subset of relevant variables. As efficient exploration of large posterior spaces is needed for real-world applications, we develop a variational expectation conditional maximization algorithm that scales inference to hundreds of samples, nodes and auxiliary variables. We illustrate and exploit the advantages of our approach in simulations and in a gene network study which identifies hub genes involved in biological pathways relevant to immune-mediated diseases.

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.

The substantial threat of concurrent air pollutants to public health is increasingly severe under climate change. To identify the common drivers and extent of spatiotemporal similarity of PM2.5 and ozone (O3), this paper proposed a log Gaussian-Gumbel Bayesian hierarchical model allowing for sharing a stochastic partial differential equation and autoregressive model of order one (SPDE-AR(1)) spatiotemporal interaction structure. The proposed model, implemented by the approach of integrated nested Laplace approximation (INLA), outperforms in terms of estimation accuracy and prediction capacity for its increased parsimony and reduced uncertainty, especially for the shared O3 sub-model. Besides the consistently significant influence of temperature (positive), extreme drought (positive), fire burnt area (positive), gross domestic product (GDP) per capita (positive), and wind speed (negative) on both PM2.5 and O3, surface pressure and precipitation demonstrate positive associations with PM2.5 and O3, respectively. While population density relates to neither. In addition, our results demonstrate similar spatiotemporal interactions between PM2.5 and O3, indicating that the spatial and temporal variations of these pollutants show relatively considerable consistency in California. Finally, with the aid of the excursion function, we see that the areas around the intersection of San Luis Obispo and Santa Barbara counties are likely to exceed the unhealthy O3 level for USG simultaneously with other areas throughout the year. Our findings provide new insights for regional and seasonal strategies in the co-control of PM2.5 and O3. Our methodology is expected to be utilized when interest lies in multiple interrelated processes in the fields of environment and epidemiology.

Multi-gene panel testing allows many cancer susceptibility genes to be tested quickly at a lower cost making such testing accessible to a broader population. Thus, more patients carrying pathogenic germline mutations in various cancer-susceptibility genes are being identified. This creates a great opportunity, as well as an urgent need, to counsel these patients about appropriate risk-reducing management strategies. Counseling hinges on accurate estimates of age-specific risks of developing various cancers associated with mutations in a specific gene, ie, penetrance estimation. We propose a meta-analysis approach based on a Bayesian hierarchical random-effects model to obtain penetrance estimates by integrating studies reporting different types of risk measures (eg, penetrance, relative risk, odds ratio) while accounting for the associated uncertainties. After estimating posterior distributions of the parameters via a Markov chain Monte Carlo algorithm, we estimate penetrance and credible intervals. We investigate the proposed method and compare with an existing approach via simulations based on studies reporting risks for two moderate-risk breast cancer susceptibility genes, ATM and PALB2. Our proposed method is far superior in terms of coverage probability of credible intervals and mean square error of estimates. Finally, we apply our method to estimate the penetrance of breast cancer among carriers of pathogenic mutations in the ATM gene.

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.

Indoor radon is an important risk factor for lung cancer, as 3-14% of lung cancer cases can be attributed to radon. The aim of our study was to estimate the impact of indoor radon exposure on lung cancer incidence over the last 40 years in Slovenia. We analyzed the distribution of lung cancer incidence across 212 municipalities and 6032 settlements in Slovenia. The standardized incidence ratios were smoothed with the Besag-York-Mollie model and fitted with the integrated nested Laplace approximation. A categorical explanatory variable, the risk of indoor radon exposure with low, moderate and high risk values, was added to the models. We also calculated the population attributable fraction. Between 2.8% and 6.5% of the lung cancer cases in Slovenia were attributable to indoor radon exposure, with values varying by time period. The relative risk of developing lung cancer was significantly higher among the residents of areas with a moderate and high risk of radon exposure. Indoor radon exposure is an important risk factor for lung cancer in Slovenia in areas with high natural radon radiation (especially in the southern and south-eastern parts of the country).

Single-visit surveys of plots are often used for estimating the abundance of species of conservation concern. Less-than-perfect availability and detection of individuals can bias estimates if not properly accounted for. We developed field methods and a Bayesian model that accounts for availability and detection bias during single-visit visual plot surveys. We used simulated data to test the accuracy of the method under a realistic range of generating parameters and applied the method to Florida\'s east coast diamondback terrapin in the Indian River Lagoon system, where they were formerly common but have declined in recent decades. Simulations demonstrated that the method produces unbiased abundance estimates under a wide range of conditions that can be expected to occur in such surveys. Using terrapins as an example we show how to include covariates and random effects to improve estimates and learn about species-habitat relationships. Our method requires only counting individuals during short replicate surveys rather than keeping track of individual identity and is simple to implement in a variety of point count settings when individuals may be temporarily unavailable for observation. We provide examples in R and JAGS for implementing the model and to simulate and evaluate data to validate the application of the method under other study conditions.

Measuring soccer shooting skill is a challenging analytics problem due to the scarcity and highly contextual nature of scoring events. The introduction of more advanced data surrounding soccer shots has given rise to model-based metrics which better cope with these challenges. Specifically, metrics such as expected goals added, goals above expectation, and post-shot expected goals all use advanced data to offer an improvement over the classical conversion rate. However, all metrics developed to date assign a value of zero to off-target shots, which account for almost two-thirds of all shots, since these shots have no probability of scoring. We posit that there is non-negligible shooting skill signal contained in the trajectories of off-target shots and propose two shooting skill metrics that incorporate the signal contained in off-target shots. Specifically, we develop a player-specific generative model for shot trajectories based on a mixture of truncated bivariate Gaussian distributions. We use this generative model to compute metrics that allow us to attach non-zero value to off-target shots. We demonstrate that our proposed metrics are more stable than current state-of-the-art metrics and have increased predictive power.