Kyu Ha Lee

Conservation of sequence and expression of Xenopus and zebrafish dHAND during cardiac, branchial arch and lateral mesoderm development

dHAND and eHAND are related basic helix-loop-helix transcription factors that are expressed in the cardiac mesoderm and in numerous neural crest-derived cell types in chick and mouse. To better understand the evolutionary development of overlapping expression and function of the HAND genes during embryogenesis, we cloned the zebrafish and Xenopus orthologues. Comparison of dHAND sequences in zebrafish, Xenopus, chick, mouse and human demonstrated conservation throughout the protein. Expression of dHAND in zebrafish was seen in the earliest precursors of all lateral mesoderm at early gastrulation stages. At neurula and later stages, dHAND expression was observed in lateral precardiac mesoderm, branchial arch neural crest derivatives and posterior lateral mesoderm. At looping heart stages, cardiac dHAND expression remained generalized with no apparent regionalization. Interestingly, no eHAND orthologue was found in zebrafish. In Xenopus, dHAND and eHAND were co-expressed in the cardiac mesoderm without the segmental restriction seen in mice. Xenopus dHAND and eHAND were also expressed bilaterally in the lateral mesoderm without any left-right asymmetry. Within the branchial arches, XdHAND was expressed in a broader domain than XeHAND, similar to their mouse counterparts. Together, these data demonstrate conservation of HAND structure and expression across species.

Semi-Competing Risks Data Analysis Accounting for Death as a Competing Risk When the Outcome of Interest Is Nonterminal

Hospital readmission is a key marker of quality of health care. Notwithstanding its widespread use, however, it remains controversial in part because statistical methods used to analyze readmission, primarily logistic regression and related models, may not appropriately account for patients who die before experiencing a readmission event within the time frame of interest. Toward resolving this, we describe and illustrate the semi-competing risks framework, which refers to the general setting where scientific interest lies with some nonterminal event (eg, readmission), the occurrence of which is subject to a terminal event (eg, death). Although several statistical analysis methods have been proposed for semi-competing risks data, we describe in detail the use of illness–death models primarily because of their relation to well-known methods for survival analysis and the availability of software. We also describe and consider in detail several existing approaches that could, in principle, be used to analyze semi-competing risks data, including composite end point and competing risks analyses. Throughout we illustrate the ideas and methods using data on N=49 763 Medicare beneficiaries hospitalized between 2011 and 2013 with a principle discharge diagnosis of heart failure.

Hierarchical Models for Semicompeting Risks Data With Application to Quality of End-of-Life Care for Pancreatic Cancer

ABSTRACT Readmission following discharge from an initial hospitalization is a key marker of quality of healthcare in the United States. For the most part, readmission has been studied among patients with “acute” health conditions, such as pneumonia and heart failure, with analyses based on a logistic-Normal generalized linear mixed model. Naïve application of this model to the study of readmission among patients with “advanced” health conditions such as pancreatic cancer, however, is problematic because it ignores death as a competing risk. A more appropriate analysis is to imbed such a study within the semicompeting risks framework. To our knowledge, however, no comprehensive statistical methods have been developed for cluster-correlated semicompeting risks data. To resolve this gap in the literature we propose a novel hierarchical modeling framework for the analysis of cluster-correlated semicompeting risks data that permits parametric or nonparametric specifications for a range of components giving analysts substantial flexibility as they consider their own analyses. Estimation and inference is performed within the Bayesian paradigm since it facilitates the straightforward characterization of (posterior) uncertainty for all model parameters, including hospital-specific random effects. Model comparison and choice is performed via the deviance information criterion and the log-pseudo marginal likelihood statistic, both of which are based on a partially marginalized likelihood. An efficient computational scheme, based on the Metropolis-Hastings-Green algorithm, is developed and had been implemented in the R package SemiCompRisks. A comprehensive simulation study shows that the proposed framework performs very well in a range of data scenarios, and outperforms competitor analysis strategies. The proposed framework is motivated by and illustrated with an ongoing study of the risk of readmission among Medicare beneficiaries diagnosed with pancreatic cancer. Using data on n = 5298 patients at J=112 hospitals in the six New England states between 2000–2009, key scientific questions we consider include the role of patient-level risk factors on the risk of readmission and the extent of variation in risk across hospitals not explained by differences in patient case-mix. Supplementary materials for this article are available online.

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures

The impact of neurotoxic chemical mixtures on childrens health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.

Incidence of AIDS-Defining Opportunistic Infections and Mortality during Antiretroviral Therapy in a Cohort of Adult HIV-Infected Individuals in Hanoi, 2007-2014

Background Although the prognosis for HIV-infected individuals has improved after antiretroviral therapy (ART) scale-up, limited data exist on the incidence of AIDS-defining opportunistic infections (ADIs) and mortality during ART in resource-limited settings. Methods HIV-infected adults in two large hospitals in urban Hanoi were enrolled to the prospective cohort, from October 2007 through December 2013. Those who started ART less than one year before enrollment were assigned to the survival analysis. Data on ART history and ADIs were collected retrospectively at enrollment and followed-up prospectively until April 2014. Results Of 2,070 cohort participants, 1,197 were eligible for analysis and provided 3,446 person-years (PYs) of being on ART. Overall, 161 ADIs episodes were noted at a median of 3.20 months after ART initiation (range 0.03–75.8) with an incidence 46.7/1,000 PYs (95% confidence interval [CI] 39.8–54.5). The most common ADI was tuberculosis with an incidence of 29.9/1,000 PYs. Mortality after ART initiation was 8.68/1,000 PYs and 45% (19/45) died of AIDS-related illnesses. Age over 50 years at ART initiation was significantly associated with shorter survival after controlling for baseline CD4 count, but neither having injection drug use (IDU) history nor previous ADIs were associated with poor survival. Semi-competing risks analysis in 951 patients without ADIs history prior to ART showed those who developed ADIs after starting ART were at higher risk of death in the first six months than after six months. Conclusion ADIs were not rare in spite of being on effective ART. Age over 50 years, but not IDU history, was associated with shorter survival in the cohort. This study provides in-depth data on the prognosis of patients on ART in Vietnam during the first decade of ART scale-up.

Temporal trend in the reported birth prevalence of cleft lip and/or cleft palate in Brazil, 2000 to 2013.

BACKGROUND The birth prevalence of cleft lip with or without cleft palate (CL/P) in Brazil increased between the years from 1975 to 1994 but has not been evaluated for temporal trend since then. METHODS We used data from the Brazilian National Health Information System for the years 2000 through 2013. We calculated the reported CL/P birth prevalence each year per 10,000 live births and estimated the average increase in reported prevalence per year (and 95% confidence interval [CI]) by fitting a negative binomial regression model. We also estimated the temporal trend in each of the five Brazilian regions for this time period. RESULTS The overall reported birth prevalence was 4.85 (95% CI, 4.78-4.91) per 10,000 live births. The reported birth prevalence of CL/P increased over this time period, from 3.94 (95% CI, 3.73-4.17) per 10,000 in 2000 to 5.46 (95% CI, 5.20-5.74) per 10,000 in 2013. The temporal trend differed for different Brazilian geographic regions, being confined primarily to the Northeast (4.7% per year; 95% CI, 4.0%-5.5%), North (3.3% per year; 95% CI, 1.8%-4.7%), and Central (2.9% per year; 95% CI, 0.9%-4.9%) regions. CONCLUSION In recent years, there appears to be an upward trend in the reported prevalence of CL/P in Brazil, confined to the less developed regions of the country. The increase likely reflects improved surveillance; whether it also reflects etiologic differences is unknown. Birth Defects Research (Part A) 106:789-792, 2016.

Geometric constraints during epithelial jamming

As an injury heals, an embryo develops or a carcinoma spreads, epithelial cells systematically change their shape. In each of these processes cell shape is studied extensively whereas variability of shape from cell to cell is regarded most often as biological noise. But where do cell shape and its variability come from? Here we report that cell shape and shape variability are mutually constrained through a relationship that is purely geometrical. That relationship is shown to govern processes as diverse as maturation of the pseudostratified bronchial epithelial layer cultured from non-asthmatic or asthmatic donors, and formation of the ventral furrow in the Drosophila embryo. Across these and other epithelial systems, shape variability collapses to a family of distributions that is common to all. That distribution, in turn, is accounted for by a mechanistic theory of cell–cell interaction, showing that cell shape becomes progressively less elongated and less variable as the layer becomes progressively more jammed. These findings suggest a connection between jamming and geometry that spans living organisms and inert jammed systems, and thus transcends system details. Although molecular events are needed for any complete theory of cell shape and cell packing, observations point to the hypothesis that jamming behaviour at larger scales of organization sets overriding geometric constraints.Epithelial cells are shown to scale via a shape distribution that is common to a number of different systems, suggesting that cell shape and shape variability are constrained through a relationship that is purely geometrical.

Survival prediction and variable selection with simultaneous shrinkage and grouping priors

The presented work is motivated by the need of reliably estimating and predicting the survival rates for individuals diagnosed with cancer, when gene expression profiles are available for identifying molecular risks factors for cancer. The regression analysis of such data is challenged by three characteristics of data: i time-to-event outcome, ii high-dimensional covariate space, and iii a group structure of genes. One strategy to simultaneously deal with all three of the aforementioned challenges is to build a penalized regression model using special penalty functions such as elastic net, fused lasso, and group lasso. To our knowledge, existing methods are sparse or non-existent, however, when a Bayesian estimation/inference is the goal for the penalized regression models. In this article, we propose a Bayesian semi-parametric framework for the regression analysis of gene expression data with survival outcomes. Our proposed Bayesian methods permit researchers to take advantage of numerous benefits including the ability to incorporate substantive prior information, the straightforward and automated quantification of uncertainty in prediction, and the prescriptive nature of computation. The performance of our proposed models for variable selection and prediction is thoroughly investigated through simulation studies, where we consider four scenarios based on different underlying group structures of covariates and covariate effects. The results generally show the satisfactory variable selection capability and predictability of our methods. Finally, we apply our proposed framework to three different gene expression data sets. We developed an efficient Markov chain Monte Carlo algorithm for the implementation of our proposed framework and provided an easy-to-use R package.

Author Correction: Geometric constraints during epithelial jamming

In the first correction to this Article, the authors added James P. Butler and Jeffrey J. Fredburg as equally contributing authors. However, this was in error; the statement should have remained indicating that Lior Atia, Dapeng Bi and Yasha Sharma contributed equally. This has now been corrected.

Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation

The analysis of multiple outcomes is becoming increasingly common in modern biomedical studies. It is well-known that joint statistical models for multiple outcomes are more flexible and more powerful than fitting a separate model for each outcome; they yield more powerful tests of exposure or treatment effects by taking into account the dependence among outcomes and pooling evidence across outcomes. It is, however, unlikely that all outcomes are related to the same subset of covariates. Therefore, there is interest in identifying exposures or treatments associated with particular outcomes, which we term outcome-specific variable selection. In this work, we propose a variable selection approach for multivariate normal responses that incorporates not only information on the mean model, but also information on the variance-covariance structure of the outcomes. The approach effectively leverages evidence from all correlated outcomes to estimate the effect of a particular covariate on a given outcome. To implement this strategy, we develop a Bayesian method that builds a multivariate prior for the variable selection indicators based on the variance-covariance of the outcomes. We show via simulation that the proposed variable selection strategy can boost power to detect subtle effects without increasing the probability of false discoveries. We apply the approach to the Normative Aging Study (NAS) epigenetic data and identify a subset of five genes in the asthma pathway for which gene-specific DNA methylations are associated with exposures to either black carbon, a marker of traffic pollution, or sulfate, a marker of particles generated by power plants.