An informative Bayesian structural equation model to assess source-specific health effects of air pollution

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.     

Human B cells on their route to latent infection--early but transient expression of lytic genes of Epstein-Barr virus

Epstein-Barr virus (EBV) is a human tumor virus and a paradigm of herpesviral latency. Mature naïve or memory B cells are EBV's preferred targets in vitro and in vivo. Upon infection of any B cell with EBV, the virus induces cellular proliferation to yield lymphoblastoid cell lines (LCLs) in vitro and establishes a latent infection in them. In these cells a ‘classical’ subset of latent viral genes is expressed that orchestrate and regulate cellular activation and proliferation, prevent apoptosis, and maintain viral latency. Surprisingly, little is known about the early events in primary human B cells infected with EBV. Recent analyses have revealed the initial but transient expression of additional viral genes that do not belong to the ‘classical’ latent subset. Some of these viral genes have been known to initiate the lytic, productive phase of EBV but virus synthesis does not take place early after infection. The early but transient expression of certain viral lytic genes is essential for or contributes to the initial survival and cell cycle entry of resting B cells to foster their proliferation and sustain a latent infection. This review summarizes the recent findings and discusses the presumed function(s) of viral genes expressed shortly but transiently after infection of B-lymphocytes with EBV.     

Bayesian covariance selection in generalized linear mixed models

The generalized linear mixed model (GLMM), which extends the generalized linear model (GLM) to incorporate random effects characterizing heterogeneity among subjects, is widely used in analyzing correlated and longitudinal data. Although there is often interest in identifying the subset of predictors that have random effects, random effects selection can be challenging, particularly when outcome distributions are nonnormal. This article proposes a fully Bayesian approach to the problem of simultaneous selection of fixed and random effects in GLMMs. Integrating out the random effects induces a covariance structure on the multivariate outcome data, and an important problem that we also consider is that of covariance selection. Our approach relies on variable selection-type mixture priors for the components in a special Cholesky decomposition of the random effects covariance. A stochastic search MCMC algorithm is developed, which relies on Gibbs sampling, with Taylor series expansions used to approximate intractable integrals. Simulated data examples are presented for different exponential family distributions, and the approach is applied to discrete survival data from a time-to-pregnancy study.     

On latent-variable model misspecification in structural measurement error models for binary response

Summary .  We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods.     

A possible role of vimentin on the cell surface for the activation of latent transforming growth factor-β

Latent TGF-β (LTGF-β) has to be converted to active TGF-β for its activities. Previously, we reported that certain fragments of latency associated peptide (LAP) augmented LTGF-β activation via increase in binding of LTGF-β to the endothelial cell (EC) surface followed by cell-associated proteolysis. By searching for EC membrane proteins crosslinked with the LAP fragment, we identified the molecule bound to LAP fragment as vimentin. Moreover, the LAP fragment-induced LTGF-β activation was attenuated by anti-vimentin antibody. These results indicate that binding of the LAP fragment to vimentin on the cell surface is indispensable for LTGF-β activation by the LAP fragment.

Structured summary

MINT-6806227:vimentin (uniprotkb:P48616) binds (MI:0407) to LAP (uniprotkb:P18341) by competition binding (MI:0405)MINT-6806183:LAP (uniprotkb:P18341) binds (MI:0407) to vimentin (uniprotkb:P48616) by cross-linking studies (MI:0030)     

Bayesian dynamic modeling of latent trait distributions

Studies of latent traits often collect data for multiple items measuring different aspects of the trait. For such data, it is common to consider models in which the different items are manifestations of a normal latent variable, which depends on covariates through a linear regression model. This article proposes a flexible Bayesian alternative in which the unknown latent variable density can change dynamically in location and shape across levels of a predictor. Scale mixtures of underlying normals are used in order to model flexibly the measurement errors and allow mixed categorical and continuous scales. A dynamic mixture of Dirichlet processes is used to characterize the latent response distributions. Posterior computation proceeds via a Markov chain Monte Carlo algorithm, with predictive densities used as a basis for inferences and evaluation of model fit. The methods are illustrated using data from a study of DNA damage in response to oxidative stress.     

Latent model for correlated binary data with diagnostic error

We propose a methodology for modeling correlated binary data measured with diagnostic error. A shared random effect is used to induce correlations in repeated true latent binary outcomes and in observed responses and to link the probability of a true positive outcome with the probability of having a diagnosis error. We evaluate the performance of our proposed approach through simulations and compare it with an ad hoc approach. The methodology is illustrated with data from a study that assessed the probability of corneal arcus in patients with familial hypercholesterolemia.     

Polyhazard models for lifetime data

We propose a polyhazard model to deal with lifetime data associated with latent competing risks. The causes of failure are assumed unobserved and affecting individuals independently. The general framework allows a broad class of hazard models that includes the most common hazard-based models. The model accommodates bathtub and multimodal hazards, keeping enough flexibility for common lifetime data that cannot be accommodated by usual hazard-based models. Maximum likelihood estimation is discussed, and parametric simulation is used for hypothesis testing.     

Estimating equations for a latent transition model with multiple discrete indicators

This paper proposes a two-part model for studying transitions between health states over time when multiple, discrete health indicators are available. The includes a measurement model positing underlying latent health states and a transition model between latent health states over time. Full maximum likelihood estimation procedures are computationally complex in this latent variable framework, making only a limited class of models feasible and estimation of standard errors problematic. For this reason, an estimating equations analogue of the pseudo-likelihood method for the parameters of interest, namely the transition model parameters, is considered. The finite sample properties of the proposed procedure are investigated through a simulation study and the importance of choosing strong indicators of the latent variable is demonstrated. The applicability of the methodology is illustrated with health survey data measuring disability in the elderly from the Longitudinal Study of Aging.     

Robustness of the latent variable model for correlated binary data

The marginal regression model offers a useful alternative to conditional approaches to analyzing binary data (Liang, Zeger, and Qaqish, 1992, Journal of the Royal Statistical Society, Series B 54, 3-40). Instead of modelling the binary data directly as do Liang and Zeger (1986, Biometrika 73, 13-22), the parametric marginal regression model developed by Qu et al. (1992, Biometrics 48, 1095-1102) assumes that there is an underlying multivariate normal vector that gives rise to the observed correlated binary outcomes. Although this parametric approach provides a flexible way to model different within-cluster correlation structures and does not restrict the parameter space, it is of interest to know how robust the parameter estimates are with respect to choices of the latent distribution. We first extend the latent modelling to include multivariate t-distributed latent vectors and assess the robustness in this class of distributions. Then we show through a simulation that the parameter estimates are robust with respect to the latent distribution even if latent distribution is skewed. In addtion to this empirical evidence for robustness, we show through the iterative algorithm that the robustness of the regression coefficents with respect to misspecifications of covariance structure in Liang and Zeger's model in fact indicates robustness with respect to underlying distributional assumptions of the latent vector in the latent variable model.