Semi-parametric benchmark dose analysis with monotone additive models.

Benchmark dose analysis aims to estimate the level of exposure to a toxin associated with a clinically significant adverse outcome and quantifies uncertainty using the lower limit of a confidence interval for this level. We develop a novel framework for benchmark dose analysis based on monotone additive dose-response models. We first introduce a flexible approach for fitting monotone additive models via penalized B-splines and Laplace-approximate marginal likelihood. A reflective Newton method is then developed that employs de Boor's algorithm for computing splines and their derivatives for efficient estimation of the benchmark dose. Finally, we develop a novel approach for calculating benchmark dose lower limits based on an approximate pivot for the nonlinear equation solved by the estimated benchmark dose. The favorable properties of this approach compared to the Delta method and a parameteric bootstrap are discussed. We apply the new methods to make inferences about the level of prenatal alcohol exposure associated with clinically significant cognitive defects in children using data from six NIH-funded longitudinal cohort studies. Software to reproduce the results in this paper is available online and makes use of the novel semibmd  R package, which implements the methods in this paper.

Approximate Bayesian inference for case-crossover models.

A case-crossover analysis is used as a simple but powerful tool for estimating the effect of short-term environmental factors such as extreme temperatures or poor air quality on mortality. The environment on the day of each death is compared to the one or more "control days" in previous weeks, and higher levels of exposure on death days than control days provide evidence of an effect. Current state-of-the-art methodology and software (integrated nested Laplace approximation [INLA]) cannot be used to fit the most flexible case-crossover models to large datasets, because the likelihood for case-crossover models cannot be expressed in a manner compatible with this methodology. In this paper, we develop a flexible and scalable modeling framework for case-crossover models with linear and semiparametric effects which retains the flexibility and computational advantages of INLA. We apply our method to quantify nonlinear associations between mortality and extreme temperatures in India. An R package implementing our methods is publicly available.

Bayesian inference for Cox proportional hazard models with partial likelihoods, nonlinear covariate effects and correlated observations.

We propose a flexible and scalable approximate Bayesian inference methodology for the Cox Proportional Hazards model with partial likelihood. The model we consider includes nonlinear covariate effects and correlated survival times. The proposed method is based on nested approximations and adaptive quadrature, and the computational burden of working with the log-partial likelihood is mitigated through automatic differentiation and Laplace approximation. We provide two simulation studies to show the accuracy of the proposed approach, compared with the existing methods. We demonstrate the practical utility of our method and its computational advantages over Markov Chain Monte Carlo methods through the analysis of Kidney infection times, which are paired, and the analysis of Leukemia survival times with a semi-parametric covariate effect and spatial variation.

Virulence genes and previously unexplored gene clusters in four commensal Neisseria spp. isolated from the human throat expand the neisserial gene repertoire.

Commensal non-pathogenic Neisseria spp. live within the human host alongside the pathogenic Neisseria meningitidis and Neisseria gonorrhoeae and due to natural competence, horizontal gene transfer within the genus is possible and has been observed. Four distinct Neisseria spp. isolates taken from the throats of two human volunteers have been assessed here using a combination of microbiological and bioinformatics techniques. Three of the isolates have been identified as Neisseria subflava biovar perflava and one as Neisseria cinerea. Specific gene clusters have been identified within these commensal isolate genome sequences that are believed to encode a Type VI Secretion System, a newly identified CRISPR system, a Type IV Secretion System unlike that in other Neisseria spp., a hemin transporter, and a haem acquisition and utilization system. This investigation is the first to investigate these systems in either the non-pathogenic or pathogenic Neisseria spp. In addition, the N. subflava biovar perflava possess previously unreported capsule loci and sequences have been identified in all four isolates that are similar to genes seen within the pathogens that are associated with virulence. These data from the four commensal isolates provide further evidence for a Neisseria spp. gene pool and highlight the presence of systems within the commensals with functions still to be explored.