Multiple exposure distributed lag models with variable selection

Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado.

Improved inference for doubly robust estimators of heterogeneous treatment effects

We propose a doubly robust approach to characterizing treatment effect heterogeneity in observational studies. We develop a frequentist inferential procedure that utilizes posterior distributions for both the propensity score and outcome regression models to provide valid inference on the conditional average treatment effect even when high-dimensional or nonparametric models are used. We show that our approach leads to conservative inference in finite samples or under model misspecification and provides a consistent variance estimator when both models are correctly specified. In simulations, we illustrate the utility of these results in difficult settings such as high-dimensional covariate spaces or highly flexible models for the propensity score and outcome regression. Lastly, we analyze environmental exposure data from NHANES to identify how the effects of these exposures vary by subject-level characteristics.

The emergence of bifurcated structure in children's language

Human language is unique among animal communication systems, in part because of its dual patterning in which meaningless phonological units combine to form meaningful words (phonological structure) and words combine to form sentences (lexicosyntactic structure). Although dual patterning is well recognized, its emergence in language development has been scarcely investigated. Chief among questions still unanswered is the extent to which development of these separate structures is independent or interdependent, and what supports acquisition of each level of structure. We explored these questions by examining growth of lexicosyntactic and phonological structure in children with normal hearing (n = 49) and children with hearing loss who use cochlear implants (n = 56). Multiple measures of each kind of structure were collected at 2-year intervals (kindergarten through eighth grade), and used to construct latent scores for each type of structure. Growth curve analysis assessed (a) the relative independence of development for each level of structure; (b) interactions between these two levels of structure in real-time language processing; and (c) contributions to growth of each level of structure made by auditory input, socioeconomic status (as proxy for linguistic experience), and speech motor control. Findings suggested that phonological and lexicosyntactic structure develop largely independently. Auditory input, socioeconomic status, and speech motor control help shape these language structures, with the last two factors exerting stronger effects for children with cochlear implants. Only for children with cochlear implants were interdependencies in real-time processing observed, reflecting compensatory mechanisms likely present to help them handle the disproportionately large phonological deficit they exhibit. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties

We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. We show how posterior distributions of treatment and outcome models can be used together with doubly robust estimators. We propose an approach to uncertainty quantification for the doubly robust estimator, which utilizes posterior distributions of model parameters and (1) results in good frequentist properties in small samples, (2) is based on a single run of a Markov chain Monte Carlo (MCMC) algorithm, and (3) improves over frequentist measures of uncertainty which rely on asymptotic properties. We consider a flexible framework for modeling the treatment and outcome processes within the Bayesian paradigm that reduces model dependence, accommodates nonlinearity, and achieves dimension reduction of the covariate space. We illustrate the ability of the proposed approach to flexibly estimate causal effects in high dimensions and appropriately quantify uncertainty. We show that our proposed variance estimation strategy is consistent when both models are correctly specified, and we see empirically that it performs well in finite samples and under model misspecification. Finally, we estimate the effect of continuous environmental exposures on cholesterol and triglyceride levels.

Parental Language Input to Children With Hearing Loss: Does It Matter in the End?

Purpose Parental language input (PLI) has reliably been found to influence child language development for children at risk of language delay, but previous work has generally restricted observations to the preschool years. The current study examined whether PLI during the early years explains variability in the spoken language abilities of children with hearing loss at those young ages, as well as later in childhood. Participants One hundred children participated: 34 with normal hearing, 24 with moderate losses who used hearing aids (HAs), and 42 with severe-to-profound losses who used cochlear implants (CIs). Mean socioeconomic status was middle class for all groups. Children with CIs generally received them early. Method Samples of parent-child interactions were analyzed to characterize PLI during the preschool years. Child language abilities (CLAs) were assessed at 48 months and 10 years of age. Results No differences were observed across groups in how parents interacted with their children. Nonetheless, strong differences across groups were observed in the effects of PLI on CLAs at 48 months of age: Children with normal hearing were largely resilient to their parents' language styles. Children with HAs were most influenced by the amount of PLI. Children with CIs were most influenced by PLI that evoked child language and modeled more complex versions. When potential influences of preschool PLI on CLAs at 10 years of age were examined, those effects at preschool were replicated. When mediation analyses were performed, however, it was found that the influences of preschool PLI on CLAs at 10 years of age were partially mediated by CLAs at preschool. Conclusion PLI is critical to the long-term spoken language abilities of children with hearing loss, but the style of input that is most effective varies depending on the severity of risk for delay.

High-Dimensional Confounding Adjustment Using Continuous Spike and Slab Priors

In observational studies, estimation of a causal effect of a treatment on an outcome relies on proper adjustment for confounding. If the number of the potential confounders (p) is larger than the number of observations (n), then direct control for all potential confounders is infeasible. Existing approaches for dimension reduction and penalization are generally aimed at predicting the outcome, and are less suited for estimation of causal effects. Under standard penalization approaches (e.g. Lasso), if a variable Xj is strongly associated with the treatment T but weakly with the outcome Y, the coefficient βj will be shrunk towards zero thus leading to confounding bias. Under the assumption of a linear model for the outcome and sparsity, we propose continuous spike and slab priors on the regression coefficients βj corresponding to the potential confounders Xj . Specifically, we introduce a prior distribution that does not heavily shrink to zero the coefficients (βj s) of the Xj s that are strongly associated with T but weakly associated with Y. We compare our proposed approach to several state of the art methods proposed in the literature. Our proposed approach has the following features: 1) it reduces confounding bias in high dimensional settings; 2) it shrinks towards zero coefficients of instrumental variables; and 3) it achieves good coverages even in small sample sizes. We apply our approach to the National Health and Nutrition Examination Survey (NHANES) data to estimate the causal effects of persistent pesticide exposure on triglyceride levels.

Statistical Workflow for Feature Selection in Human Metabolomics Data

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel ‘rain plot’ approach to display the results of these analyses. The ‘rain plot’ combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically.

Doubly robust matching estimators for high dimensional confounding adjustment

Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. In this article, we propose matching on both the estimated propensity score and the estimated prognostic scores when the number of covariates is large relative to the number of observations. We derive asymptotic results for the matching estimator and show that it is doubly robust in the sense that only one of the two score models need be correct to obtain a consistent estimator. We show via simulation its effectiveness in controlling for confounding and highlight its potential to address nonlinear confounding. Finally, we apply the proposed procedure to analyze the effect of gender on prescription opioid use using insurance claims data.

Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research

In comparative effectiveness research, we are often interested in the estimation of an average causal effect from large observational data (the main study). Often this data does not measure all the necessary confounders. In many occasions, an extensive set of additional covariates is measured for a smaller and non-representative population (the validation study). In this setting, standard approaches for missing data imputation might not be adequate due to the large number of missing covariates in the main data relative to the smaller sample size of the validation data. We propose a Bayesian approach to estimate the average causal effect in the main study that borrows information from the validation study to improve confounding adjustment. Our approach combines ideas of Bayesian model averaging, confounder selection, and missing data imputation into a single framework. It allows for different treatment effects in the main study and in the validation study, and propagates the uncertainty due to the missing data imputation and confounder selection when estimating the average causal effect (ACE) in the main study. We compare our method to several existing approaches via simulation. We apply our method to a study examining the effect of surgical resection on survival among 10 396 Medicare beneficiaries with a brain tumor when additional covariate information is available on 2220 patients in SEER-Medicare. We find that the estimated ACE decreases by 30% when incorporating additional information from SEER-Medicare.

A synthetic estimator for the efficacy of clinical trials with all-or-nothing compliance

A critical issue in the analysis of clinical trials is patients' noncompliance to assigned treatments. In the context of a binary treatment with all or nothing compliance, the intent-to-treat analysis is a straightforward approach to estimating the effectiveness of the trial. In contrast, there exist 3 commonly used estimators with varying statistical properties for the efficacy of the trial, formally known as the complier-average causal effect. The instrumental variable estimator may be unbiased but can be extremely variable in many settings. The as treated and per protocol estimators are usually more efficient than the instrumental variable estimator, but they may suffer from selection bias. We propose a synthetic approach that incorporates all 3 estimators in a data-driven manner. The synthetic estimator is a linear convex combination of the instrumental variable, per protocol, and as treated estimators, resembling the popular model-averaging approach in the statistical literature. However, our synthetic approach is nonparametric; thus, it is applicable to a variety of outcome types without specific distributional assumptions. We also discuss the construction of the synthetic estimator using an analytic form derived from a simple normal mixture distribution. We apply the synthetic approach to a clinical trial for post-traumatic stress disorder.

Spatial Multiresolution Analysis of the Effect of PM2.5 on Birth Weights

Fine particulate matter (PM_2.5) measured at a given location is a mix of pollution generated locally and pollution traveling long distances in the atmosphere. Therefore, the identification of spatial scales associated with health effects can inform on pollution sources responsible for these effects, resulting in more targeted regulatory policy. Recently, prediction methods that yield high-resolution spatial estimates of PM_2.5 exposures allow one to evaluate such scale-specific associations. We propose a two-dimensional wavelet decomposition that alleviates restrictive assumptions required for standard wavelet decompositions. Using this method we decompose daily surfaces of PM_2.5 to identify which scales of pollution are most associated with adverse health outcomes. A key feature of the approach is that it can remove the purely temporal component of variability in PM_2.5 levels and calculate effect estimates derived solely from spatial contrasts. This eliminates the potential for unmeasured confounding of the exposure - outcome associations by temporal factors, such as season. We apply our method to a study of birth weights in Massachusetts, U.S.A from 2003-2008 and find that both local and urban sources of pollution are strongly negatively associated with birth weight. Results also suggest that failure to eliminate temporal confounding in previous analyses attenuated the overall effect estimate towards zero, with the effect estimate growing in magnitude once this source of variability is removed.

The positive effects of population-based preferential sampling in environmental epidemiology

In environmental epidemiology, exposures are not always available at subject locations and must be predicted using monitoring data. The monitor locations are often outside the control of researchers, and previous studies have shown that "preferential sampling" of monitoring locations can adversely affect exposure prediction and subsequent health effect estimation. We adopt a slightly different definition of preferential sampling than is typically seen in the literature, which we call population-based preferential sampling. Population-based preferential sampling occurs when the location of the monitors is dependent on the subject locations. We show the impact that population-based preferential sampling has on exposure prediction and health effect estimation using analytic results and a simulation study. A simple, one-parameter model is proposed to measure the degree to which monitors are preferentially sampled with respect to population density. We then discuss these concepts in the context of PM2.5 and the EPA Air Quality System monitoring sites, which are generally placed in areas of higher population density to capture the population's exposure.

Mitigating Bias in Generalized Linear Mixed Models: The Case for Bayesian Nonparametrics

Generalized linear mixed models are a common statistical tool for the analysis of clustered or longitudinal data where correlation is accounted for through cluster-specific random effects. In practice, the distribution of the random effects is typically taken to be a Normal distribution, although if this does not hold then the model is misspecified and standard estimation/inference may be invalid. An alternative is to perform a so-called nonparametric Bayesian analyses in which one assigns a Dirichlet process (DP) prior to the unknown distribution of the random effects. In this paper we examine operating characteristics for estimation of fixed effects and random effects based on such an analysis under a range of "true" random effects distributions. As part of this we investigate various approaches for selection of the precision parameter of the DP prior. In addition, we illustrate the use of the methods with an analysis of post-operative complications among n = 18, 643 female Medicare beneficiaries who underwent a hysterectomy procedure at N = 503 hospitals in the US. Overall, we conclude that using the DP priori n modeling the random effect distribution results in large reductions of bias with little loss of efficiency. While no single choice for the precision parameter will be optimal in all settings, certain strategies such as importance sampling or empirical Bayes can be used to obtain reasonable results in a broad range of data scenarios.

Perioperative myocardial injury in patients receiving cardiac output-guided haemodynamic therapy: a substudy of the OPTIMISE Trial

BACKGROUND

Evidence suggests that cardiac output-guided haemodynamic therapy algorithms improve outcomes after high-risk surgery, but there is some concern that this could promote acute myocardial injury. We evaluated the incidence of myocardial injury in a perioperative goal-directed therapy trial.

METHODS

Patients undergoing major gastrointestinal surgery (n=723) were randomly assigned to cardiac output-guided haemodynamic therapy (intervention group) or usual care as part of the OPTIMISE trial. At four participating sites, 288 patients were enrolled in a biomarker substudy. Serum high-sensitivity cardiac troponin I (TnI) concentration and N-terminal pro-brain natriuretic peptide (NT-proBNP) concentration were measured before and at 24 and 72 h after surgery.

RESULTS

Median preoperative TnI and NT-ProBNP concentrations were 4.3 ng litre(-1) and 144 pg ml(-1), respectively. After surgery, 67 (46%) patients in the intervention group and 68 (48%) patients receiving usual care had TnI concentrations above the 99th centile upper reference limit (P=0.82). Peak serum TnI concentration was similar in the intervention and usual care groups (median [interquartile range]: 10.0 [5.3-21.5] vs 7.8 [5.0-21.8] ng litre(-1); P=0.85), and no differences were observed in serum TnI concentrations over 72 h (repeated-measures anova, P=0.51). Likewise, there were no differences in peak NT-proBNP concentration between intervention and usual care groups (645 [362-1169] vs 659 [381-1028] pg ml(-1); P=0.86) or in serial NT-proBNP concentrations over 72 h (P=0.20).

CONCLUSIONS

Myocardial injury is common among patients undergoing major gastrointestinal surgery. In this study, the frequency was not affected by cardiac output-guided fluid and low-dose inotropic therapy.

Combined dysfunctions of immune cells predict nosocomial infection in critically ill patients

BACKGROUND

Nosocomial infection occurs commonly in intensive care units (ICUs). Although critical illness is associated with immune activation, the prevalence of nosocomial infections suggests concomitant immune suppression. This study examined the temporal occurrence of immune dysfunction across three immune cell types, and their relationship with the development of nosocomial infection.

METHODS

A prospective observational cohort study was undertaken in a teaching hospital general ICU. Critically ill patients were recruited and underwent serial examination of immune status, namely percentage regulatory T-cells (Tregs), monocyte deactivation (by expression) and neutrophil dysfunction (by CD88 expression). The occurrence of nosocomial infection was determined using pre-defined, objective criteria.

RESULTS

Ninety-six patients were recruited, of whom 95 had data available for analysis. Relative to healthy controls, percentage Tregs were elevated 6-10 days after admission, while monocyte HLA-DR and neutrophil CD88 showed broader depression across time points measured. Thirty-three patients (35%) developed nosocomial infection, and patients developing nosocomial infection showed significantly greater immune dysfunction by the measures used. Tregs and neutrophil dysfunction remained significantly predictive of infection in a Cox hazards model correcting for time effects and clinical confounders {hazard ratio (HR) 2.4 [95% confidence interval (CI) 1.1-5.4] and 6.9 (95% CI 1.6-30), respectively, P=0.001}. Cumulative immune dysfunction resulted in a progressive risk of infection, rising from no cases in patients with no dysfunction to 75% of patients with dysfunction of all three cell types (P=0.0004).

CONCLUSIONS

Dysfunctions of T-cells, monocytes, and neutrophils predict acquisition of nosocomial infection, and combine additively to stratify risk of nosocomial infection in the critically ill.

Exercise therapy across the prostate cancer continuum

Exercise has been increasingly investigated as an adjunct therapy for cancer patients. The purpose of this paper is to comprehensively review the literature regarding exercise as a therapeutic adjunct for prostate cancer (PC). Several studies in patients with PC have shown quality of life improvements associated with exercise. Although no study has established the effect of exercise as a monotherapy for PC, the molecular mechanisms responsible for the potential association between exercise and PC are being elucidated. Given the low-risk, high-reward nature of these studies, further investigations are needed to better define the function of exercise along the PC continuum.

Quick identification of febrile neonates with low risk for serious bacterial infection: an observational study

OBJECTIVE

To examine the possible usefulness of simple and quick criteria for identifying febrile neonates with low risk for serious bacterial infection (SBI).

DESIGN

All febrile neonates who were admitted between August 1998 and August 2003 to the Pediatric Emergency Department, HaEmek Medical Center, Afula, Israel, and to the Poriya Hospital, Tiberias, Israel, were included in the study. The recommended evaluation of each neonate included details of medical history and a complete physical examination, including blood culture, erythrocyte sedimentation rate (ESR), white cell count (WBC), and analysis and culture of urine and cerebrospinal fluid. Other tests were carried out as necessary. Patients who met all the following criteria were considered to have low risk for SBI: (1) unremarkable medical history; (2) good appearance; (3) no focal physical signs of infection; (4) ESR <30 mm at the end of the first hour; (5) WBC 5000-15 000/mm(3); (6) a normal urine analysis by the dipstick method.

RESULTS

Complete data were available for 386 neonates. SBI was documented in 108 (28%) neonates, of whom 14% had a urinary tract infection, 9.3% had acute otitis media, 2.3% had pneumonia, 1.3% had cellulitis, 0.5% had bacterial meningitis and 0.5% had bacterial gastroenteritis. The overall incidence of SBI was 1 in 166 (0.6%) neonates who fulfilled the criteria compared with 107 in 220 (48.6%) in the neonates who did not fulfil the criteria (p<0.001). The negative predictive value for SBI of the combination of the low-risk criteria was 99.4% (95% confidence interval 99.35% to 99.45%).

CONCLUSIONS

Fulfillment of the criteria for low risk might be a reliable and useful tool for excluding SBI in febrile neonates.

Solvents and color discrimination ability. Nonreplication of previous findings

Previous research has shown exposure-related increases in the prevalence of acquired color vision deficits among printers. We administered the Lanthony D-15 desaturated test of color vision to 82 print shop workers. Two tests of cognitive function, Trails A and B and the Symbol Digit Modalities Test, were also administered. Personal air sampling indicated that current exposure to organic solvents was highest among printers and lowest among bindery workers. In contrast to previous studies, the age-adjusted quantitative Lanthony D-15 desaturated test error scores did not differ significantly between exposure groups, and the proportion of subjects with > or = 1 error was greater in the lower-exposure, rather than higher-exposure, groups (P = .03). Of note, the proportion of subjects with > or = 2 errors did not differ significantly between groups (P = .24). Cognitive tests showed no significant association with exposure. These results are discussed in the context of methodological issues related to lighting sources, reliability of test results, and establishment of criteria for identifying deficits.

Further delineation of the acrocallosal syndrome

The acrocallosal syndrome (ACS) was recognized by Schinzel in 1979 as a specific entity, characterized by the association of craniofacial anomalies, total or partial agenesis of corpus callosum, polysyndactyly and mental retardation. The inheritance is autosomal recessive, based on instances of recurrence in siblings and cousins and parental consanguinity. A large inbred kindred with recurrent ACS is presented. This family further strengthens the hypothesis of autosomal recessive inheritance for this syndrome. The array of clinical manifestations in this sibship and those previously reported exemplify the phenomenon of inter- and intrafamilial variability that must be considered when defining ACS. Based on a review of published reports and the present family, essential, additional and occasional findings are distinguished. Attention is drawn to geographical clustering of the families.

Antimicrobial properties of dental dentin-enamel adhesives

This study evaluated the antimicrobial activity of Universal Bond II adhesive and two experimental versions of Universal Bond II adhesives against the bacteria, Streptococcus mutans. The zones of bacterial inhibition produced by three samples of the adhesive (identified as Batches 1, 2, and 3) were measured and compared. Batch 1 contained 0.7% glutaraldehyde, Batch 2 contained 1.0% glutaraldehyde, and Batch 3 contained 0.45% glutaraldehyde (current Universal Bond II adhesive). The bacteria was swabbed over the surface of agar plates in two directions. The plates were divided into the following two groups: Group I-Batches 1, 2, 3 were placed into wells for 1 minute before being cured. Group II-Batches 1, 2, 3 were placed into wells for 20 seconds before being cured. Zones of microbial inhibition were measured in millimeters at the end of 24, 48, and 72 hours. All batches of the adhesive produced zones of inhibition against S. mutans. All batches of the adhesive maintained zones of inhibition throughout the 72 hours of the study.

Noonan's syndrome associated with hypoplastic left heart

A case of a week old female baby, admitted because of apathy, hypothermia, dyspnea, jaundice and cyanosis is described. She had the characteristic phenotype of Turner's syndrome with normal karyotype. Signs of severe heart failure were present. Therapy with diuretics, digoxin, dopamine and mechanical ventilation were unsuccessful, and the patient died several hours after her admission. The anatomopathological examination revealed the presence of hypoplastic left heart syndrome with mitral atresia and aortic atresia, atrial septal defect, double outlet right ventricle, and a patent ductus arteriosus.

Cardiovascular malformations in lecanosomatopagus conjoined twins: a cardiologic curiosity

Female conjoined twins were delivered after 42 weeks' gestation, but they died within a few minutes of birth. They were dicephalus, dibrachius and dipus conjoined twins with two separate spines and fusion of the trunk and the pelvis. The pericardial sac was common, and the heart was a single structure. The atrial complex was a common chamber with an attempt at division into two parts by a circular ridge of tissue; the ventricular complex was formed by three chambers which were all communicating between each other in the superior margin of their muscular interventricular septum.