A Revised Framework to Evaluate the Consistency Assumption Globally in a Network of Interventions

Background The unrelated mean effects (UME) model has been proposed for evaluating the consistency assumption globally in the network of interventions. However, the UME model does not accommodate multiarm trials properly and omits comparisons between nonbaseline interventions in the multiarm trials not investigated in 2-arm trials. Methods We proposed a refinement of the UME model that tackles the limitations mentioned above. We also accompanied the scatterplots on the posterior mean deviance contributions of the trial arms under the network meta-analysis (NMA) and UME models with Bland-Altman plots to detect outlying trials contributing to poor model fit. We applied the refined and original UME models to 2 networks with multiarm trials. Results The original UME model omitted more than 20% of the observed comparisons in both networks. The thorough inspection of the individual data points’ deviance contribution using complementary plots in conjunction with the measures of model fit and the estimated between-trial variance indicated that the refined and original UME models revealed possible inconsistency in both examples. Conclusions The refined UME model allows proper accommodation of the multiarm trials and visualization of all observed evidence in complex networks of interventions. Furthermore, considering several complementary plots to investigate deviance helps draw informed conclusions on the possibility of global inconsistency in the network. Highlights We have refined the unrelated mean effects (UME) model to incorporate multiarm trials properly and to estimate all observed comparisons in complex networks of interventions. Forest plots with posterior summaries of all observed comparisons under the network meta-analysis and refined UME model can uncover the consequences of potential inconsistency in the network. Using complementary plots to investigate the individual data points’ deviance contribution in conjunction with model fit measures and estimated heterogeneity aid in detecting possible inconsistency.

Coronavirus (COVID-19) Spike in Georgia: An Epidemiologic Study of Data, Modelling, and Policy Implications to Understand the Gender-and Race-Specific Variations

ObjectiveThe purpose of this study was to investigate the gender-and race-specific predictive variations in COVID-19 cases and deaths in Georgia, USA.MethodsThe data were extracted from the Georgia Department of Public Health (GDPH). Statistical methods, such as descriptive statistics, Artificial neural networks (ANN), and Bayesian approach, were utilized to analyze the data.ResultsMore Whites died from COVID-19 than African-Americans/Blacks in Cobb, Hall, Gwinnett, and non-Georgia residents; however, more Blacks died in Dekalb and Fulton counties. The highest posterior mean for female deaths was obtained in Gwinnett County (77.17; 95% CI, 74.23–80.07) and for male deaths in Fulton County (73.48; 95% CI, 72.18–74.49). For overall race/ethnicity, Whites had the highest posterior mean for deaths (183.18; 95% CI, 128.29–238.27) compared with Blacks (162.48; 95% CI, 127.15– 197.42). Assessing the classification of the chronic medical conditions using ANN, Cobb and Hall Counties showed the highest mean AUC-ROC of the models (78% and 79%, respectively).ConclusionsThe predictive models of COVID-19 transmission will help public health practitioners and researchers to better understand the course of the COVID-19 pandemic. The study findings are generalizable to populations with geographic and racial/ethnic similarities and may be used to determine gender/race-specific future virus models for effective interventions or policy modifications.Human SubjectsNo personal identifiable information was obtained.

Shared components of heritability across genetically correlated traits

Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability. We developed Pleiotropic Decomposition Regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components. We applied PDR to three clusters of 5-6 traits genetically correlated with coronary disease, asthma, and type II diabetes respectively, producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r2) between true and estimated effect sizes compared with the original summary statistics: by 94% and 70% for asthma and T2D out of sample, and by a predicted 300% for CAD.

Laplace-Beltrami Refined Shape Regression Applied to Neck Reconstruction for Craniosynostosis Patients

This contribution is part of a project concerning the creation of an artificial dataset comprising 3D head scans of craniosynostosis patients for a deep-learning-based classification. To conform to real data, both head and neck are required in the 3D scans. However, during patient recording, the neck is often covered by medical staff. Simply pasting an arbitrary neck leaves large gaps in the 3D mesh. We therefore use a publicly available statistical shape model (SSM) for neck reconstruction. However, most SSMs of the head are constructed using healthy subjects, so the full head reconstruction loses the craniosynostosis-specific head shape. We propose a method to recover the neck while keeping the pathological head shape intact. We propose a Laplace- Beltrami-based refinement step to deform the posterior mean shape of the full head model towards the pathological head. The artificial neck is created using the publicly available Liverpool-York-Model. We apply our method to construct artificial necks for head scans of 50 scaphocephaly patients. Our method reduces mean vertex correspondence error by approximately 1.3 mm compared to the ordinary posterior mean shape, preserves the pathological head shape, and creates a continuous transition between neck and head. The presented method showed good results for reconstructing a plausible neck to craniosynostosis patients. Easily generalized it might also be applicable to other pathological shapes.

An uncertainty-based model of the effects of fixation on choice

When people view a consumable item for a longer amount of time, they choose it more frequently; this also seems to be the direction of causality. The leading model of this effect is a drift-diffusion model with a fixation-based attentional bias. Here, we propose an explicitly Bayesian account for the same data. This account is based on the notion that the brain builds a posterior belief over the value of an item in the same way it would over a sensory variable. As the agent gathers evidence about the item from sensory observations and from retrieved memories, the posterior distribution narrows. We further postulate that the utility of an item is a weighted sum of the posterior mean and the negative posterior standard deviation, with the latter accounting for risk aversion. Fixating for longer can increase or decrease the posterior mean, but will inevitably lower the posterior standard deviation. This model fits the data better than the original attentional drift-diffusion model but worse than a variant with a collapsing bound. We discuss the often overlooked technical challenges in fitting models simultaneously to choice and response time data in the absence of an analytical expression. Our results hopefully contribute to emerging accounts of valuation as an inference process.

Information Validates the Prior: A Theorem on Bayesian Updating and Applications

We develop a result on expected posteriors for Bayesians with heterogenous priors, dubbed information validates the prior (IVP). Under familiar ordering requirements, Anne expects a (Blackwell) more informative experiment to bring Bob’s posterior mean closer to Anne’s prior mean. We apply the result in two contexts of games of asymmetric information: voluntary testing or certification, and costly signaling or falsification. IVP can be used to determine how an agent’s behavior responds to additional exogenous or endogenous information. We discuss economic implications. (JEL C11, D82, D84)

Individual heterogeneity in fitness in a long-lived herbivore

Heterogeneity in the intrinsic quality and nutritional condition of individuals affects reproductive success and consequently fitness. Understanding differences in energy allocation towards survival and reproduction within and among years might help explain variability in individual fitness. Black brant (Branta bernicla nigricans) are long-lived, migratory, specialist herbivores. Long migratory pathways and short summer breeding seasons constrain the time and energy available for reproduction, thus magnifying life-history trade-offs. These constraints, combined with long lifespans and trade-offs between current and future reproductive value, provide a model system to examine the role of individual heterogeneity in driving life-history strategies and individual heterogeneity in fitness. We used hierarchical Bayesian models to examine reproductive trade-offs, modeling the relationships between within-year measures of reproductive energy allocation and among-year demographic rates of individual females breeding on the Yukon-Kuskokwim Delta, Alaska using capture-recapture and reproductive data from 1988 to 2014. We provide evidence for relationships between breeding probability and clutch size (posterior mean of β = 0.45, 95% CRI = 0.33 – 0.57, SD = 0.06), breeding probability and nest initiation date (posterior mean of β = -0.12, 95% CRI = -0.2 ¬– -0.04, SD = 0.04), and an interaction between clutch size and initiation date (posterior mean of β = -0.12, 95% CRI = -0.2 – -0.04, SD = 0.04). Average lifetime clutch size also had a weak positive relationship with survival probability (posterior mean of β = 0.03, 95% CRI = -0.01 – 0.7, SD = 0.02). Our results support the use of demographic buffering strategies for black brant; reductions in reproductive energy allocation preserve high adult survival rates during years with poor environmental conditions, maximizing future reproductive value. We also indirectly show links among environmental conditions during growth, fitness, and energy allocation, highlighting the effects of early growth conditions on individual heterogeneity, and subsequently, reproductive investment.

On the existence of posterior mean for Bayesian logistic regression

Existence conditions for posterior mean of Bayesian logistic regression depend on both chosen prior distributions and a likelihood function. In logistic regression, different patterns of data points can lead to finite maximum likelihood estimates (MLE) or infinite MLE of the regression coefficients. Albert and Anderson [On the existence of maximum likelihood estimates in logistic regression models, Biometrika 71 1984, 1, 1–10] gave definitions of different types of data points, which are complete separation, quasicomplete separation and overlap. Conditions for the existence of the MLE for logistic regression models were proposed under different types of data points. Based on these conditions, we propose the necessary and sufficient conditions for the existence of posterior mean under different choices of prior distributions. In this paper, a general wide class of priors, which are informative priors and non-informative priors having proper distributions and improper distributions, are considered for the existence of posterior mean. In addition, necessary and sufficient conditions for the existence of posterior mean for an individual coefficient is also proposed.