This study investigated the relationship between hospital exposures, intestinal microbiota, and subsequent risk of Clostridium difficile-associated disease (CDAD), with use of a nested case-control design. The study included 599 patients, hospitalized from September 2006 through May 2007 in Montreal, Quebec, from whom fecal samples were obtained within 72 h after admission; 25 developed CDAD, and 50 matched controls were selected for analysis. Nonsteroidal anti-inflammatory drugs and antibiotic use were associated with CDAD. Fecal specimens were evaluated by 16S ribosomal RNA microarray to characterize bacteria in the intestinal microbiota during the at-risk period. Probe intensities were higher for Firmicutes, Proteobacteria, and Actinobacteria in the patients with CDAD, compared with controls, whereas probe intensities for Bacteroidetes were lower. After epidemiologic factors were controlled for, only Bacteroidetes and Firmicutes remained significantly and independently associated with development of CDAD. Hospital exposures were associated with changes in the intestinal microbiota and risk of CDAD, and these changes were not driven exclusively by antimicrobial use.

Objective Subarachnoid hemorrhage (SAH) is associated with inflammation which may mediate poor outcome in SAH. We hypothesize that elevated serum tumor necrosis factor-alpha (TNFα) and interleukin-6 (IL-6) are associated with vasospasm and poor outcome in SAH. Methods In 52 consecutive SAH subjects, we compared TNFα and IL-6 levels on post-SAH days 0–1, 2–3, 4–5, 6–8, and 10–14 with respect to vasospasm and to poor outcome at 3- and 6-months. Vasospasm was defined as >50% reduction in vessel caliber on angiography. Poor outcome was defined as modified Rankin score >2. Results Elevated TNFα on post-SAH days 2–3 was associated with poor 3-month outcome (p=0.0004). Global elevation of TNFα over time (post-SAH days 0–14) was independently associated with poor 3-month outcome after adjusting for Hunt-and-Hess grade and age (p=0.02). Neither cross-sectional nor IL-6 levels over time were associated with outcome. Neither TNFα nor IL-6 levels were associated with vasospasm. Conclusions Elevation in serum TNFα on post-SAH days 2–3 and global elevation of TNFα over time are associated with poor outcome but not with angiographic vasospasm in this small cohort. Future studies are needed to define the role of TNFα in SAH-related brain injury and its potential as a SAH outcome biomarker.

Attention deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder characterized by inappropriate levels of attention, hyperactivity, and impulsivity. Although a strong genetic component to the disorder has been established, the molecular genetic underpinnings of this disorder remain elusive. Recently, several studies have reported an association between polymorphisms within the latrophilin 3 gene (LPHN3) and ADHD. Interestingly, the same single-nucleotide polymorphism conferring susceptibility to ADHD has also been found to predict efficacy of stimulant medication in children. The main objectives of the current article are: (i) To tackle the phenotype heterogeneity issue in ADHD by defining an objective and quantitative measure of response to treatment in a sample of ADHD children based on a hand held automatic device (Actiwatch) and (ii) to use this measure to reproduce for the first time the association between LPHN3 variants and response to methylphenidate (MPH) using a double-blind, placebo-controlled crossover experimental design. The results of our study confirm the hypothesis that LPHN3 is associated with response to MPH in ADHD children. Although this will require further validation, our work suggests that the use of an objective measure of response to treatment, such as the change in the child's motor activity measured by Actiwatch, has the potential to uncover genetic association signals that in some conditions might not be obtained using more subjective measures, such as the clinical consensus rating, for example.

Estimating causal exposure effects in observational studies ideally requires the analyst to have a vast knowledge of the domain of application. Investigators often bypass difficulties related to the identification and selection of confounders through the use of fully adjusted outcome regression models. However, since such models likely contain more covariates than required, the variance of the regression coefficient for exposure may be unnecessarily large. Instead of using a fully adjusted model, model selection can be attempted. Most classical statistical model selection approaches, such as Bayesian model averaging, do not readily address causal effect estimation. We present a new model averaged approach to causal inference, Bayesian causal effect estimation (BCEE), which is motivated by the graphical framework for causal inference. BCEE aims to unbiasedly estimate the causal effect of a continuous exposure on a continuous outcome while being more efficient than a fully adjusted approach.

No abstract

Marginal structural models are commonly used to estimate the causal effect of a time-varying treatment in presence of time-dependent confounding. When fitting an MSM to data, the analyst must specify both the structural model for the outcome and the treatment models for the inverse-probability-of-treatment weights. The use of stabilized weights is recommended because they are generally less variable than the standard weights. In this paper, we are concerned with the use of the common stabilized weights when the structural model is specified to only consider partial treatment history, such as the current or most recent treatments. We present various examples of settings where these stabilized weights yield biased inferences while the standard weights do not. These issues are first investigated on the basis of simulated data and subsequently exemplified using data from the Honolulu Heart Program. Unlike common stabilized weights, we find that basic stabilized weights offer some protection against bias in structural models designed to estimate current or most recent treatment effects.

BackgroundOver time, adaptive Gaussian Hermite quadrature (QUAD) has become the preferred method for estimating generalized linear mixed models with binary outcomes. However, penalized quasi-likelihood (PQL) is still used frequently. In this work, we systematically evaluated whether matching results from PQL and QUAD indicate less bias in estimated regression coefficients and variance parameters via simulation.MethodsWe performed a simulation study in which we varied the size of the data set, probability of the outcome, variance of the random effect, number of clusters and number of subjects per cluster, etc. We estimated bias in the regression coefficients, odds ratios and variance parameters as estimated via PQL and QUAD. We ascertained if similarity of estimated regression coefficients, odds ratios and variance parameters predicted less bias.ResultsOverall, we found that the absolute percent bias of the odds ratio estimated via PQL or QUAD increased as the PQL- and QUAD-estimated odds ratios became more discrepant, though results varied markedly depending on the characteristics of the datasetConclusionsGiven how markedly results varied depending on data set characteristics, specifying a rule above which indicated biased results proved impossible.This work suggests that comparing results from generalized linear mixed models estimated via PQL and QUAD is a worthwhile exercise for regression coefficients and variance components obtained via QUAD, in situations where PQL is known to give reasonable results.

A Systematic Evolution of Ligands by EXponential enrichment (SE-LEX) experiment begins in round one with a random pool of oligonucleotides in equilibrium solution with a target. Over a few rounds, oligonucleotides having a high affinity for the target are selected. Data from a high throughput SELEX experiment consists of lists of thousands of oligonucleotides sampled after each round. Thus far, SELEX experiments have been very good at suggesting the highest affinity oligonucleotide, but modeling lower affinity recognition site variants has been difficult. Furthermore, an alignment step has always been used prior to analyzing SELEX data.We present a novel model, based on a biochemical parametrization of SE-LEX, which allows us to use data from all rounds to estimate the affinities of the oligonucleotides. Most notably, our model also aligns the oligonucleotides. We use our model to analyze a SELEX experiment containing double stranded DNA oligonucleotides and the transcription factor Bicoid as the target. Our SELEX model outperformed other published methods for predicting putative binding sites for Bicoid as indicated by the results of an in-vivo ChIP-chip experiment.