Benchmark dose profiles for bivariate exposures.

While benchmark dose (BMD) methodology is well-established for settings with a single exposure, these methods cannot easily handle multidimensional exposures with nonlinear effects. We propose a framework for BMD analysis to characterize the joint effect of a two-dimensional exposure on a continuous outcome using a generalized additive model while adjusting for potential confounders via propensity scores. This leads to a dose-response surface which can be summarized in two dimensions by a contour plot in which combinations of exposures leading to the same expected effect are identified. In our motivating study of prenatal alcohol exposure, cognitive deficits in children are found to be associated with both the frequency of drinking as well as the amount of alcohol consumed on each drinking day during pregnancy. The general methodological framework is useful for a broad range of settings, including combinations of environmental stressors, such as chemical mixtures, and in explorations of the impact of dose rate rather than simply cumulative exposure on adverse outcomes.

Effects of prenatal alcohol exposure on cognitive and behavioral development: Findings from a hierarchical meta-analysis of data from six prospective longitudinal U.S. cohorts.

BACKGROUND: Cognitive and behavioral sequelae of prenatal alcohol exposure (PAE) continue to be prevalent in the United States and worldwide. Because these sequelae are also common in other neurodevelopmental disorders, researchers have attempted to identify a distinct neurobehavioral profile to facilitate the differential diagnosis of fetal alcohol spectrum disorders (FASD). We used an innovative, individual participant meta-analytic technique to combine data from six large U.S. longitudinal cohorts to provide a more comprehensive and reliable characterization of the neurobehavioral deficits seen in FASD than can be obtained from smaller samples. METHODS: Meta-analyses were performed on data from 2236 participants to examine effects of PAE (measured as oz absolute alcohol/day (AA/day)) on IQ, four domains of cognition function (learning and memory, executive function, reading achievement, and math achievement), sustained attention, and behavior problems, after adjusting for potential confounders using propensity scores. RESULTS: The effect sizes for IQ and the four domains of cognitive function were strikingly similar to one another and did not differ at school age, adolescence, or young adulthood. Effect sizes were smaller in the more middle-class Seattle cohort and larger in the three cohorts that obtained more detailed and comprehensive assessments of AA/day. PAE effect sizes were somewhat weaker for parent- and teacher-reported behavior problems and not significant for sustained attention. In a meta-analysis of five aspects of executive function, the strongest effect was on set-shifting. CONCLUSIONS: The similarity in the effect sizes for the four domains of cognitive function suggests that PAE affects an underlying component or components of cognition involving learning and memory and executive function that are reflected in IQ and academic achievement scores. The weaker effects in the more middle-class cohort may reflect a more cognitively stimulating environment, a different maternal drinking pattern (lower alcohol dose/occasion), and/or better maternal prenatal nutrition. These findings identify two domains of cognition-learning/memory and set-shifting-that are particularly affected by PAE, and one, sustained attention, which is apparently spared.

Localisation and substrate specificities of transglycanases in charophyte algae relate to development and morphology.

Cell wall-modifying enzymes have been previously investigated in charophyte green algae (CGA) in cultures of uniform age, giving limited insight into their roles. Therefore, we investigated the in situ localisation and specificity of enzymes acting on hemicelluloses in CGA genera of different morphologies and developmental stages. In vivo transglycosylation between xyloglucan and an endogenous donor in filamentous Klebsormidium and Zygnema was observed in longitudinal cell walls of young (1 month) but not old cells (1 year), suggesting that it has a role in cell growth. By contrast, in parenchymatous Chara, transglycanase action occurred in all cell planes. In Klebsormidium and Zygnema, the location of enzyme action mainly occurred in regions where xyloglucans and mannans, and to a lesser extent mixed-linkage ß-glucan (MLG), were present, indicating predominantly xyloglucan:xyloglucan endotransglucosylase (XET) activity. Novel transglycosylation activities between xyloglucan and xylan, and xyloglucan and galactomannan were identified in vitro in both genera. Our results show that several cell wall-modifying enzymes are present in CGA, and that differences in morphology and cell age are related to enzyme localisation and specificity. This indicates an evolutionary significance of cell wall modifications, as similar changes are known in their immediate descendants, the land plants. This article has an associated First Person interview with the first author of the paper.

A Longitudinal Analysis of the Executive Functions in High-Level Soccer Players.

INTRODUCTION: Assessments of executive functions (EFs) with varying levels of perceptual information or action fidelity are common talent-diagnostic tools in soccer, yet their validity still has to be established. Therefore, a longitudinal development of EFs in high-level players to understand their relationship with increased exposure to training is required. METHODS: A total of 304 high-performing male youth soccer players (10-21 years old) in Germany were assessed across three seasons on various sport-specific and non-sport-specific cognitive functioning assessments. RESULTS: The posterior means (90% highest posterior density) of random slopes indicated that both abilities predominantly developed between 10 and 15 years of age. A plateau was apparent for domain-specific abilities during adolescence, whereas domain-generic abilities improved into young adulthood. CONCLUSION: The developmental trajectories of soccer players' EFs follow the general populations' despite long-term exposure to soccer-specific training and game play. This brings into question the relationship between high-level experience and EFs and renders including EFs in talent identification questionable.

A novel case-control subsampling approach for rapid model exploration of large clustered binary data.

In many settings, an analysis goal is the identification of a factor, or set of factors associated with an event or outcome. Often, these associations are then used for inference and prediction. Unfortunately, in the big data era, the model building and exploration phases of analysis can be time-consuming, especially if constrained by computing power (ie, a typical corporate workstation). To speed up this model development, we propose a novel subsampling scheme to enable rapid model exploration of clustered binary data using flexible yet complex model set-ups (GLMMs with additive smoothing splines). By reframing the binary response prospective cohort study into a case-control-type design, and using our knowledge of sampling fractions, we show one can approximate the model estimates as would be calculated from a full cohort analysis. This idea is extended to derive cluster-specific sampling fractions and thereby incorporate cluster variation into an analysis. Importantly, we demonstrate that previously computationally prohibitive analyses can be conducted in a timely manner on a typical workstation. The approach is applied to analysing risk factors associated with adverse reactions relating to blood donation.

Exposure Enriched Case-Control (EECC) Design for the Assessment of Gene-Environment Interaction.

Genetic susceptibility and environmental exposure both play an important role in the aetiology of many diseases. Case-control studies are often the first choice to explore the joint influence of genetic and environmental factors on the risk of developing a rare disease. In practice, however, such studies may have limited power, especially when susceptibility genes are rare and exposure distributions are highly skewed. We propose a variant of the classical case-control study, the exposure enriched case-control (EECC) design, where not only cases, but also high (or low) exposed individuals are oversampled, depending on the skewness of the exposure distribution. Of course, a traditional logistic regression model is no longer valid and results in biased parameter estimation. We show that addition of a simple covariate to the regression model removes this bias and yields reliable estimates of main and interaction effects of interest. We also discuss optimal design, showing that judicious oversampling of high/low exposed individuals can boost study power considerably. We illustrate our results using data from a study involving arsenic exposure and detoxification genes in Bangladesh.

Spatial regression with covariate measurement error: A semiparametric approach.

Spatial data have become increasingly common in epidemiology and public health research thanks to advances in GIS (Geographic Information Systems) technology. In health research, for example, it is common for epidemiologists to incorporate geographically indexed data into their studies. In practice, however, the spatially defined covariates are often measured with error. Naive estimators of regression coefficients are attenuated if measurement error is ignored. Moreover, the classical measurement error theory is inapplicable in the context of spatial modeling because of the presence of spatial correlation among the observations. We propose a semiparametric regression approach to obtain bias-corrected estimates of regression parameters and derive their large sample properties. We evaluate the performance of the proposed method through simulation studies and illustrate using data on Ischemic Heart Disease (IHD). Both simulation and practical application demonstrate that the proposed method can be effective in practice.

A flexible parametric approach to examining spatial variation in relative survival.

Most of the few published models used to obtain small-area estimates of relative survival are based on a generalized linear model with piecewise constant hazards under a Bayesian formulation. Limitations of these models include the need to artificially split the time scale, restricted ability to include continuous covariates, and limited predictive capacity. Here, an alternative Bayesian approach is proposed: a spatial flexible parametric relative survival model. This overcomes previous limitations by combining the benefits of flexible parametric models: the smooth, well-fitting baseline hazard functions and predictive ability, with the Bayesian benefits of robust and reliable small-area estimates. Both spatially structured and unstructured frailty components are included. Spatial smoothing is conducted using the intrinsic conditional autoregressive prior. The model was applied to breast, colorectal, and lung cancer data from the Queensland Cancer Registry across 478 geographical areas. Advantages of this approach include the ease of including more realistic complexity, the feasibility of using individual-level input data, and the capacity to conduct overall, cause-specific, and relative survival analysis within the same framework. Spatial flexible parametric survival models have great potential for exploring small-area survival inequalities, and we hope to stimulate further use of these models within wider contexts. Copyright © 2016 John Wiley & Sons, Ltd.

Meta-analysis on studies with heterogeneous and partially observed covariates.

Individual participant data meta-analysis is a commonly used alternative to the traditional aggregate data meta-analysis. It is popular because it avoids relying on published results and enables direct adjustment for relevant covariates. However, a practical challenge is that the studies being combined often vary in terms of the potential confounders that were measured. Furthermore, it will inevitably be the case that some individuals have missing values for some of those covariates. In this paper, we demonstrate how these challenges can be resolved using a propensity score approach, combined with multiple imputation, as a strategy to adjust for covariates in the context of individual participant data meta-analysis. To illustrate, we analyze data from the Bill and Melinda Gates Foundation-funded Healthy Birth, Growth, and Development Knowledge Integration project to investigate the relationship between physical growth rate in the first year of life and cognition measured later during childhood. We found that the overall effect of average growth velocity on cognitive outcome is slightly, but significantly, positive with an estimated effect size of 0.36 (95% CI 0.18, 0.55).

A dose-response analysis of the effects of prenatal alcohol exposure on cognitive development.

BACKGROUND: Most studies of the effects of prenatal alcohol exposure (PAE) on cognitive function have assumed that the dose-response curve is linear. However, data from a few animal and human studies suggest that there may be an inflection point in the dose-response curve above which PAE effects are markedly stronger and that there may be differences associated with pattern of exposure, assessed in terms of alcohol dose per drinking occasion and drinking frequency. METHODS: We performed second-order confirmatory factor analysis on data obtained at school age, adolescence, and early adulthood from 2227 participants in six US longitudinal cohorts to derive a composite measure of cognitive function. Regression models were constructed to examine effects of PAE on cognitive function, adjusted for propensity scores. Analyses based on a single predictor (absolute alcohol (AA)/day) were compared with analyses based on two predictors (dose/occasion and drinking frequency), using (1) linear models and (2) nonparametric general additive models (GAM) that allow for both linear and nonlinear effects. RESULTS: The single-predictor GAM model showed virtually no nonlinearity in the effect of AA/day on cognitive function. However, the two-predictor GAM model revealed differential effects of maternal drinking pattern. Among offspring of infrequent drinkers, PAE effects on cognitive function were markedly stronger in those whose mothers drank more than ~3 drinks/occasion, and the effect of dose/occasion was strongest among the very frequent drinkers. Frequency of drinking did not appear to alter the PAE effect on cognitive function among participants born to mothers who limited their drinking to ~1 drink/occasion or less. CONCLUSIONS: These findings suggest that linear models based on total AA/day are appropriate for assessing whether PAE affects a given cognitive outcome. However, examination of alcohol dose/occasion and drinking frequency is needed to fully characterize the impact of different levels of alcohol intake on cognitive impairment.

Bayesian outcome selection modeling.

In psychiatric and social epidemiology studies, it is common to measure multiple different outcomes using a comprehensive battery of tests thought to be related to an underlying construct of interest. In the research that motivates our work, researchers wanted to assess the impact of in utero alcohol exposure on child cognition and neuropsychological development, which are evaluated using a range of different psychometric tests. Statistical analysis of the resulting multiple outcomes data can be challenging, because the outcomes measured on the same individual are not independent. Moreover, it is unclear, a priori, which outcomes are impacted by the exposure under study. While researchers will typically have some hypotheses about which outcomes are important, a framework is needed to help identify outcomes that are sensitive to the exposure and to quantify the associated treatment or exposure effects of interest. We propose such a framework using a modification of stochastic search variable selection, a popular Bayesian variable selection model and use it to quantify an overall effect of the exposure on the affected outcomes. The performance of the method is investigated empirically and an illustration is given through application using data from our motivating study.

A hierarchical meta-analysis for settings involving multiple outcomes across multiple cohorts.

Evidence from animal models and epidemiological studies has linked prenatal alcohol exposure (PAE) to a broad range of long-term cognitive and behavioural deficits. However, there is a paucity of evidence regarding the nature and levels of PAE associated with increased risk of clinically significant cognitive deficits. To derive robust and efficient estimates of the effects of PAE on cognitive function, we have developed a hierarchical meta-analysis approach to synthesize information regarding the effects of PAE on cognition, integrating data on multiple outcomes from six U.S. Iongitudinal cohort studies. A key assumption of standard methods of meta-analysis, effect sizes are independent, is violated when multiple intercorrelated outcomes are synthesized across studies. Our approach involves estimating the dose-response coefficients for each outcome and then pooling these correlated dose-response coefficients to obtain an estimated "global" effect of exposure on cognition. In the first stage, we use individual participant data to derive estimates of the effects of PAE by fitting regression models that adjust for potential confounding variables using propensity scores. The correlation matrix characterizing the dependence between the outcome-specific dose-response coefficients estimated within each cohort is then run, while accommodating incomplete information on some outcome. We also compare inferences based on the proposed approach to inferences based on a full multivariate analysis.

Analysis of multiple-cycle data from couples undergoing in vitro fertilization: methodologic issues and statistical approaches.

The number of in vitro fertilization (IVF) cycles in the United States increased from fewer than 46,000 in 1995 to more than 120,000 in 2005. IVF and other assisted reproductive technology (ART) data are routinely collected and used to identify outcome predictors. However, researchers do not always make full use of the data due to their complexity. Design approaches have included restriction to first-cycle attempts only, which reduces power and identifies effects only of those factors associated with initial success. Many statistical techniques have been used or proposed for analysis of IVF data, ranging from simple t tests to sophisticated models designed specifically for IVF. We applied several of these methods to data from a prospective cohort of 2687 couples undergoing ART from 1994 through 2003. Results across methods are compared and the appropriateness of the various methods is discussed with the intent to illustrate methodologic validity. We observed a remarkable similarity of coefficient estimates across models. However, each method for dealing with multiple cycle data relies on assumptions that may or may not be expected to hold in a given IVF study. The robustness and reported magnitude of effect for individual predictors of IVF success may be inflated or attenuated due to violation of statistical assumptions, and should always be critically interpreted. Given that risk factors associated with IVF success may also advance our understanding of the physiologic processes underlying conception, implantation, and gestation, the application of valid methods to these complex data is critical.

Prediction and surveillance of influenza epidemics.

OBJECTIVE: To describe the use of surveillance and forecasting models to predict and track epidemics (and, potentially, pandemics) of influenza. METHODS: We collected 5 years of historical data (2005-2009) on emergency department presentations and hospital admissions for influenza-like illnesses (International Classification of Diseases [ICD-10-AM] coding) from the Emergency Department Information System (EDIS) database of 27 Queensland public hospitals. The historical data were used to generate prediction and surveillance models, which were assessed across the 2009 southern hemisphere influenza season (June-September) for their potential usefulness in informing response policy. Three models are described: (i) surveillance monitoring of influenza presentations using adaptive cumulative sum (CUSUM) plan analysis to signal unusual activity; (ii) generating forecasts of expected numbers of presentations for influenza, based on historical data; and (iii) using Google search data as outbreak notification among a population. RESULTS: All hospitals, apart from one, had more than the expected number of presentations for influenza starting in late 2008 and continuing into 2009. (i) The CUSUM plan signalled an unusual outbreak in December 2008, which continued in early 2009 before the winter influenza season commenced. (ii) Predictions based on historical data alone underestimated the actual influenza presentations, with 2009 differing significantly from previous years, but represent a baseline for normal ED influenza presentations. (iii) The correlation coefficients between internet search data for Queensland and statewide ED influenza presentations indicated an increase in correlation since 2006 when weekly influenza search data became available. CONCLUSION: This analysis highlights the value of health departments performing surveillance monitoring to forewarn of disease outbreaks. The best system among the three assessed was a combination of routine forecasting methods coupled with an adaptive CUSUM method.

Propensity score analysis for a semi-continuous exposure variable: a study of gestational alcohol exposure and childhood cognition.

Propensity score methodology has become increasingly popular in recent years as a tool for estimating causal effects in observational studies. Much of the related research has been directed at settings with binary or discrete exposure variables with more recent work involving continuous exposure variables. In environmental epidemiology, a substantial proportion of individuals is often completely unexposed while others may experience heavy exposure leading to an exposure distribution with a point mass at zero and a heavy right tail. We suggest a new approach to handle this type of exposure data by constructing a propensity score based on a two-part model and show how this model can be used to more reliably adjust for covariates of a semi-continuous exposure variable. We also consider the case when a misspecified propensity score is used in a regression adjustment and derive an explicit form of the bias. We show that the potential bias gets smaller as the estimated propensity score gets closer to the true expectation of the exposure variable given a set of observed covariates. While this result pertains to a more general setting, we use it to evaluate the potential bias in settings in which the true exposure has a semi-continuous structure. We also evaluate and compare the performance of our proposed method through simulation studies relative to a simpler linear regression-based propensity score for a continuous exposure variable as well as through direct covariate adjustment. Overall, we find that using a propensity score constructed via a two-part model significantly improves the regression estimate when the exposure variable is semi-continuous in nature. Specifically when the proportion of non-exposed subjects is high and the effects of covariates on exposure and outcome are strong, the proposed two-part propensity score method outperforms the more standard competing methods. We illustrate our method using data from the Detroit Longitudinal Cohort Study in which the exposure variable reflects gestational alcohol exposure featuring zero values and a long tail.

Early-life or lifetime sun exposure, sun reaction, and the risk of squamous cell carcinoma in an Asian population.

BACKGROUND: It has been widely accepted that sun exposure is a risk factor of squamous cell carcinoma (SCC) among fair-skinned populations. However, sun exposure and sun reaction have not been explored in Asians and no gender-specific data were available. METHOD: In a case-control study, 176 incident skin cancer cases were recruited from National Cheng-Kung University Medical Center from 1996 to 1999. Controls included 216 age-, gender-, and residency-matched subjects from the southwestern Taiwan. A questionnaire was administered to collect information on life style and other risk factors. Logistic regression analysis was performed to evaluate the association between sun exposure or sun reaction and the risk of SCC by gender. RESULTS: Early-age (age 15 to 24) and lifetime sun exposure were significantly associated with increased risk of SCC in a dose-response pattern [odds ratio (OR) = 1.49-3.08, trend p = 0.009 and 0.0007, respectively]. After stratified by gender, the third tertile of early-age sun exposure was significantly associated with the SCC risk among men (OR = 3.08). The second and third tertiles of lifetime sun exposure was significantly associated with SCC risk among women (OR = 3.78 and 4.53, respectively). Skin reaction after 2-h sun exposure during childhood and adolescence was not significantly associated with the risk of SCC. CONCLUSIONS: Lifetime sun exposure was more related to SCC risk in women, while early-age sun exposure was more relevant to men's SCC risk. This may be attributable to different lifestyle between men and women.

Combining individual- and group-level exposure information: child carbon monoxide in the Guatemala woodstove randomized control trial.

BACKGROUND: Epidemiology frequently relies on surrogates of long-term exposures, often either individual-level short-term measurements or group-level based on long-term characteristics of subjects and their environment. Whereas individual-level measures are often imprecise due to within-subject variability, group-level measures tend to be inaccurate due to residual between-subject variability within groups. Rather than choose between these error-prone estimates, we borrow strength from each by use of mixed-model prediction and we compare the predictive validity. METHODS: We compared alternative measures of long-term exposure to carbon monoxide (CO) among children in the RESPIRE woodstove randomized control trial during years 2003 and 2004. The main study included 1932 repeated 48-hour-average personal CO measures among 509 children from 0-18 months of age. We used a validation study with additional CO measures among a random subsample of 70 of the children to compare the predictive validity of individual-level estimates (based on observed short-term exposures), group-level estimates (based on stove type and other residential characteristics), and mixed-model predictions that combine these 2 sources of information. RESULTS: The estimated error variance for mixed-model prediction was 63% lower than the individual-level measure based on the exposure data and 58% lower than the corresponding group-level measure. CONCLUSIONS: When both individual- and group-level estimates are available but imperfect, mixed-model prediction may provide substantially better measures of long-term exposure, potentially increasing the sensitivity of epidemiologic studies to underlying causal relations.

Effects of retinoic acid on Dominant hemimelia expression in mice.

BACKGROUND: Dominant hemimelia (Dh) is an autosomal dominant mutation that arose spontaneously in mice. Dh animals are asplenic and they exhibit asymmetric hindlimb defects in association with reduced numbers of lumbar vertebrae. These defects suggest that Dh acts early in embryonic development to affect patterning of the anterior-posterior (A-P) and left-right axes. This study was undertaken to determine whether retinoic acid (RA), which is involved in A-P patterning and coordination of bilaterally synchronized somitogenesis, affects phenotypic expression of the Dh gene. METHODS: Thirty-four pregnant females were given, by oral intubation, a single dose of 50 or 75 mg all-trans RA per kilogram body weight at GD 9, 10, or 11. The pregnant females were then euthanized at GD 18 and fetuses removed by cesarean section. A total of 326 fetuses were identified by phenotype and linked DNA and their skeletons were analyzed. RESULTS: There was a differential effect of RA on the axial skeleton and hindlimb of Dh/+ mice as compared to their wild-type littermates. Dose- and stage-specific effects on sternebrae and vertebrae were observed. CONCLUSIONS: The effects of RA dosing on numbers of sternebrae and vertebrae suggest that Dh embryos have a primary defect in retinoid-mediated A-P patterning. Dosing with RA may produce the observed effects on phenotypic expression of Dh/+ by indirectly or directly modifying an already existing altered Hox expression pattern. As the relationship between axial patterning and the asymmetric limb is unknown, Dh is an important model for studying this relationship.

Maternal depressive symptoms, parenting self-efficacy, and child growth.

OBJECTIVES: We assessed whether maternal depressive symptoms and parenting self-efficacy were associated with child growth delay. METHODS: We collected data from a random sample of 595 low-income mothers and their children aged 6 to 24 months in Teresina, Piauí, Brazil, including information on sociodemographic characteristics, mothers' depressive symptoms and parenting self-efficacy, and children's anthropometric characteristics. We used adjusted logistic regression models in our analyses. RESULTS: Depressive symptoms among mothers were associated with 1.8 times higher odds (95% confidence interval [CI] = 1.1, 2.9) of short stature among children. Parenting self-efficacy was not associated with short stature, nor did it mediate or modify the relationship between depressive symptoms and short stature. Maternal depressive symptoms and self-efficacy were not related to child underweight. CONCLUSIONS: Our results showed that among low-income Brazilian families maternal depressive symptoms, but not self-efficacy, were associated with short stature in children aged 6 to 24 months after adjustment for known predictors of growth.