Bayesian Model Selection in terms of Kullback-Leibler discrepancy

In this article we investigate and develop the practical model assessment and selection methods for Bayesian models, when we anticipate that a promising approach should be objective enough to accept, easy enough to understand, general enough to apply, simple enough to compute and coherent enough to interpret. We mainly restrict attention to the Kullback-Leibler divergence, a widely applied model evaluation measurement to quantify the similarity between the proposed candidate model and the underlying true model, where the true model is only referred to a probability distribution as the best projection onto the statistical modeling space once we try to understand the real but unknown dynamics/mechanism of interest. In addition to review and discussion on the advantages and disadvantages of the historically and currently prevailing practical model selection methods in literature, a series of convenient and useful tools, each designed and applied for different purposes, are proposed to asymptotically unbiasedly assess how the candidate Bayesian models are favored in terms of predicting a future independent observation. What's more, we also explore the connection of the Kullback-Leibler based information criterion to the Bayes factors, another most popular Bayesian model comparison approaches, after seeing the motivation through the developments of the Bayes factor variants. In general, we expect to provide a useful guidance for researchers who are interested in conducting Bayesian data analysis

A Note on Bayesian Modeling Specification of Censored Data in JAGS

Just Another Gibbs Sampling (JAGS) is a convenient tool to draw posterior samples using Markov Chain Monte Carlo for Bayesian modeling. However, the built-in function dinterval() to model censored data misspecifies the computation of deviance function, which may limit its usage to perform likelihood based model comparison. To establish an automatic approach to specify the correct deviance function in JAGS, we propose a simple alternative modeling strategy to implement Bayesian model selection for analysis of censored outcomes. The proposed approach is applicable to a broad spectrum of data types, which include survival data and many other right-, left- and interval-censored Bayesian model structures

Correction: On Bayesian modeling of censored data in JAGS

Following the publication of the original article [1], the authors identified errors in the model specifications 1 and 2. The correct models are given below

Reap-2: An Interactive Quantitative Tool for Robust and Efficient Dose-Response Curve Estimation

REAP-2 is an interactive dose-response curve estimation tool for Robust and Efficient Assessment of drug Potency. It provides user-friendly dose-response curve estimation for in vitro studies and conducts statistical testing for model comparisons with a redesigned user interface. We also make a major update of the underlying estimation method with penalized beta regression, which demonstrates great reliability and accuracy in dose estimation and uncertainty quantification. In this note, we describe the method and implementation of REAP-2 with a highlight on potency estimation and drug comparison

Outcomes of critically ill children with pre‐existing mental health conditions

ABSTRACT Importance Critically ill children with pre‐existing mental health conditions may have an increased risk of poor health outcomes. Objective We aimed to evaluate if pre‐existing mental health conditions in critically ill pediatric patients would be associated with worse clinical outcomes, compared to children with no documented mental health conditions. Methods This retrospective observational cohort study utilized the TriNetX electronic health record database of critically ill subjects aged 12–18 years. Data were analyzed for demographics, pre‐existing conditions, diagnostic, medication, procedural codes, and mortality. Results From a dataset of 102 027 critically ill children, we analyzed 1999 subjects (284 [14.2%] with a pre‐existing mental health condition and 1715 [85.8%] with no pre‐existing mental health condition). Multivariable analysis demonstrated that death within one year was associated with the presence of pre‐existing mental health conditions (odds ratio 8.97 [3.48–23.15], P < 0.001), even after controlling for the presence of a complex chronic condition. Interpretation The present study demonstrates that the presence of pre‐existing mental health conditions was associated with higher odds of death within 1 year after receiving critical care. However, the confidence interval was wide and hence, the findings are inconclusive. Future studies with a larger sample size may be necessary to evaluate the true long‐term impact of children with pre‐existing mental health conditions who require critical care services

IL6-mediated inflammatory loop reprograms normal to epithelial-mesenchymal transition

Hepatocellular carcinoma (HCC) is the second-leading cause of cancer-related deaths world-wide with poor survival rate. As many as 40% of HCCs are clonal, with alteration of key tumor suppressor pathways in stem cells is the primary cause of HCC initiation. However, mechanisms that generate metastatic stem cells in pre-neoplastic liver tissue are not well understood. We hypothesized that chronic inflammation is a major driver of the transformation of genetically defective liver stem cells (LSCs) into highly metastatic liver cancer cells in premalignant liver tissue. We developed models of chronic inflammation in wild-type (WT) and β2-spectrin (β2SP)(+/-) (SPTBN1) mice. CD133(+) LSCs derived from pre-neoplastic livers of β2SP(+/-) mice treated with pIL6 ((IL6)β2SP(+/-) LSCs) were highly tumorigenic and metastatic, whereas those derived from WT mice treated with pIL6 ((IL6)WT LSCs) had significantly less proliferation and no tumorigenic properties. (IL6)β2SP(+/-) LSCs not only exhibited nuclear localization of Twist and Slug, markers of epithelial-mesenchymal transition, but also constitutive activation of NFκB (RELA). Knockdown of NFκB decreased the EMT phenotypes and metastatic capacity of these cells. NFκB in (IL6)β2SP(+/-) LSCs was activated by TGFβ-activated kinase 1 (TAK1) (MAP3K7), which is associated with poor survival in HCC and IL6 expression. The amount of constitutively activated NFκB increased dramatically from normal to cirrhotic to HCC tissues from human patients. In conclusion, our findings suggest that IL6-mediated inflammation programs constitutive activation of the TAK1-NFκB signaling cascade in CD133(+) LSCs and this program interacts with deficient TGFβ signaling, thereby accelerating the transformation of normal LSCs to metastatic cancer stem cells. Indeed, this study is the first to delineate the development of EMT-positive mCSCs in HCC-free liver tissue upon chronic inflammation