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Preeclampsia is a pregnancy‐associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time‐to‐event setting of “semi‐competing risks” in which a non‐terminal event of interest is subject to the occurrence of a terminal event of interest. The semi‐competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non‐terminal event, which we call “clinically immediate” and “non‐immediate” terminal events. Though such phenomena arise in many clinical contexts, to‐date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty‐based illness‐death model with a binary submodel to distinguish risk of immediate terminal event following the non‐terminal event. The model admits direct dependence of the terminal event on the non‐terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions. [ABSTRACT FROM AUTHOR]

Background: The aim of this systematic review and meta-analysis was to compare the clinical efficacy of the early dental implant placement protocol with immediate and delayed dental implant placement protocols.Methods: An electronic and manual search of literature was made to identify clinical studies comparing early implant placement with immediate or delayed placement. Data from the included studies were pooled and quantitative analyses were performed for the implant outcomes reported as the number of failed implants (primary outcome variable) and for changes in peri-implant marginal bone level, peri-implant probing depth, and peri-implant soft tissue level (secondary outcome variables).Results: Twelve studies met the inclusion criteria. Significant difference in risk of implant failure was found neither between the early and immediate placement protocols (risk difference = -0.018; 95% confidence interval [CI] = -0.06, 0.025; P = 0.416) nor between early and delayed placement protocols (risk difference = -0.008; 95% CI = -0.044, 0.028; P = 0.670). Pooled data of changes in peri-implant marginal bone level demonstrated significantly less marginal bone loss for implants placed using the early placement protocol compared with those placed in fresh extraction sockets (P = 0.001; weighted mean difference = -0.14 mm; 95% CI = -0.22, -0.05). No significant differences were found between the protocols for the other variables.Conclusions: The available evidence supports the clinical efficacy of the early implant placement protocol. Present findings indicate that the early implant placement protocol results in implant outcomes similar to immediate and delayed placement protocols and a superior stability of peri-implant hard tissue compared with immediate implant placement. [ABSTRACT FROM AUTHOR]

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. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The hydrothermal synthesis behavior of ZnO nanowire (NW) on carbon nanotubes (CNTs) with respect to molar concentrations of zinc nitrate hexahydrate and hexamethylenetetramine and temperature was investigated. The ZnO NWs could be synthesized with a single-crystalline wurtzite structure and a diameter in the range of tens of nanometers. Their size does not strongly depend on the temperature, while it increases with increasing molar concentration. The NWs tend to have pyramidal shape at a growth front with increasing temperature and molar concentration probably due to enriched Zn2+ ion concentration. This demonstrates the morphological dependence of ZnO NWs on the relative rates between the decomposition of Zn precursor and subsequent oxidation of Zn to ZnO in hydrothermal synthesis. [ABSTRACT FROM AUTHOR]