Univariate Dose-Response Models in Case-Control Studies

For modelling dose-response relationships in case-control studies the multiplicative logistic regression model, assuming the relative risk to be an exponential function of the dose, is widely known. If the relative risk is assumed to be a linear function of the dose, several authors (see e.g. BERRY (1980)) have proposed an additive (linear) model. This model has a better fit with the data if such a linear relation holds. Confidence limits for the relative risk derived from the information matrix, however, appear to be rather inaccurate. Therefore, use of the ‘standard’ logistic model in two different ways was studied: extension with a quadratic term or a logarithmic transformation of the dose. By applying the methods both to an empirical data set and in a simulation experiment, it is shown that appropriate transformation (often logarithmic) of the dosage and then applying the ‘standard’ logistic model is an useful approach if a linear dose-response relationship holds.     

The Use of Epidemiology in Environmental Risk Assessment

Epidemiology provides estimates of the concentration–response relation for environmental and occupational toxicants in the species of interest, in or close to the dose range of interest. As such, when available, they provide the primary source for risk assessments. Further information can be acquired by using modern biostatistical techniques to assess the shape of the dose response relation, examine effect modification, and assure control for confounding. These approaches are particularly effective if they are done in the context of a meta-analysis or hierarchical model. This is illustrated with examples from the air pollution literature.     

Developing a risk indicator to comparatively assess environmental risks posed by microbial and conventional pest control agents

Selected biological control agents and conventional pesticides were used to critically review the applicability of a newly developed Risk Indicator (RI) system. Five basic components are proposed for the calculation of the overall environmental risk score: persistence of the active ingredient, dispersal potential, range of non-target organisms that are affected, and direct and indirect effects on the ecosystem. Several risk measurement systems were reviewed; risk categories in the proposed system were modified from a model developed for classical biocontrol agents. Additionally, one new category was included, to assess the risks to vertebrate non-target species. Besides a detailed discussion of the new RI model, the suitability of the model was demonstrated by calculating the risk scores for 17 selected products. It became obvious that the environmental risk score varied greatly within the assessed chemical products, and also within the group of biological products. The use pattern greatly influenced the estimated environmental risk posed by any given product. The overall environmental risk score varied between a very low risk score of 24 (Coniothyrium minitans, soil application) and a near maximum risk score of 4275 (high risk reference DDT, foliar spray). The proposed model can be used to communicate environmental risk and to design lower risk integrated pest management strategies. It is suggested that the proposed RI system may serve to define low risk and reduced risk pesticides. Yet, it remains debatable whether the RI will be useful in determining acceptability of data waivers for regulatory purposes.     

Risk perception of air pollution: An exploration of self-relevancy

Air pollution is a serious environmental issue that has been long recognized. Whereas government policies attempt to reduce air pollution by controlling various pollution sources, research on risk perception of air pollution tends to treat air pollution as a single unified risk and neglects the complication of the polluting sources. Previous research consistently demonstrates that lay people have an inaccurate understanding of air pollution and are unwilling to change their behaviors to decrease pollution. However, these findings can hardly be employed in policy-making because researchers and policy-makers treat air pollution differently. The aim of the present study is to obtain a nuanced understanding of people's risk perception of air pollution from different sources. Moreover, we propose a new risk dimension, Self-Relevancy, and try to understand people's reluctance to change their behaviors to reduce pollution. Three main findings emerged. First, we demonstrated that people perceived each air pollution source differently. Second, factor analysis of risk characteristics showed that ratings of Self-Relevancy questions mainly loaded on the same factor, which indicates the robustness of the Self-Relevancy risk dimension. Finally, we found that people's ratings of Self-Relevancy positively predicted their perceived risk for some of the pollution sources. These findings highlight the importance of investigating sub-categories of risks and exploring the risk dimension of Self-Relevancy in future risk perception research to provide detailed and informative data for policy-makers.     

Lattice Model for Influenza Spreading with Spontaneous Behavioral Changes

Individual behavioral response to the spreading of an epidemic plays a crucial role in the progression of the epidemic itself. The risk perception induces individuals to adopt a protective behavior, as for instance reducing their social contacts, adopting more restrictive hygienic measures or undergoing prophylaxis procedures. In this paper, starting with a previously developed lattice-gas SIR model, we construct a coupled behavior-disease model for influenza spreading with spontaneous behavioral changes. The focus is on self-initiated behavioral changes that alter the susceptibility to the disease, without altering the contact patterns among individuals. Three different mechanisms of awareness spreading are analyzed: the local spreading due to the presence in the neighborhood of infective individuals; the global spreading due to the news published by the mass media and to educational campaigns implemented at institutional level; the local spreading occurring through the “thought contagion” among aware and unaware individuals. The peculiarity of the present approach is that the awareness spreading model is calibrated on available data on awareness and concern of the population about the risk of contagion. In particular, the model is validated against the A(H1N1) epidemic outbreak in Italy during the season, by making use of the awareness data gathered by the behavioral risk factor surveillance system (PASSI). We find that, increasing the accordance between the simulated awareness spreading and the PASSI data on risk perception, the agreement between simulated and experimental epidemiological data improves as well. Furthermore, we show that, within our model, the primary mechanism to reproduce a realistic evolution of the awareness during an epidemic, is the one due to globally available information. This result highlights how crucial is the role of mass media and educational campaigns in influencing the epidemic spreading of infectious diseases.     

An X-ray spectrum estimation method from transmission measurement combined with scatter correction

PurposeConventional x-ray spectrum estimation methods from transmission measurement often lead to inaccurate results when extensive x-ray scatter is present in the measured projection. This study aims to apply the weighted L1-norm scatter correction algorithm in spectrum estimation for reducing residual differences between the estimated and true spectrum.MethodThe scatter correction algorithm is based on a simple radiographic scattering model where the intensity of scattered x-ray is directly estimated from a transmission measurement. Then, the scatter-corrected measurement is used for the spectrum estimation method that consists of deciding the weights of predefined spectra and representing the spectrum as a linear combination of the predefined spectra with the weights. The performances of the estimation method combined with scatter correction are evaluated on both simulated and experimental data.ResultsThe results show that the estimated spectra using the scatter-corrected projection nearly match the true spectra. The normalized-root-mean-square-error and the mean energy difference between the estimated spectra and corresponding true spectra are reduced from 5.8% and 1.33 keV without the scatter correction to 3.2% and 0.73 keV with the scatter correction for both simulation and experimental data, respectively.ConclusionsThe proposed method is more accurate for the acquisition of x-ray spectrum than the estimation method without scatter correction and the spectrum can be successfully estimated even the materials of the filters and their thicknesses are unknown. The proposed method has the potential to be used in several diagnostic x-ray imaging applications.     

Predation risk and the structure of freshwater zooplankton communities

Summary Many predators inflict substantial mortality on their prey. The prey respond to these selective pressures with changes in their spatial and temporal overlap with the predator (density risk responses), or with changes in their vulnerability to the predator (prey vulnerability responses). Here we develop a simple predation model that permits quantification of the basic response types of the prey in nature. We then test the hypothesis that prey response will be proportional to the intensity of the predation mortality relative to all other sources of mortality and decreased natality acting on the prey. A significant regression relationship is obtained for the prey vulnerability response but not for any of the density risk responses. The individual response values and regression statistics are used to interpret the relative importance of the different response types and to assess the predator's influence on prey community structure.Supported by Lehigh University Environmental Studies Center     

Quantitative Risk Assessment in Epidemiological Studies Investigating Threshold Effects

In this paper a method for quantitative risk assessment in epidemiological studies investigating threshold effects is proposed. The simple logistic regression model is used to describe the association between a binary response variable and a continuous risk factor. By defining acceptable levels for the absolute risk and the risk gradient the corresponding benchmark values of the risk factor can be calculated by means of nonlinear functions of the logistic regression coefficients. Standard errors and confidence intervals of the benchmark values are derived by means of the multivariate delta method. The proposed approach is compared with the threshold model of Ulm (1991) for assessing threshold values in epidemiological studies.     

Improving Power for Testing Genetic Association in Case–Control Studies by Reducing the Alternative Space

Summary .  To detect association between a genetic marker and a disease in case–control studies, the Cochran–Armitage trend test is typically used. The trend test is locally optimal when the genetic model is correctly specified. However, in practice, the underlying genetic model, and hence the optimal trend test, are usually unknown. In this case, Pearson's chi-squared test, the maximum of three trend test statistics (optimal for the recessive, additive, and dominant models), and the test based on genetic model selection (GMS) are useful. In this article, we first modify the existing GMS method so that it can be used when the risk allele is unknown. Then we propose a new approach by excluding a genetic model that is not supported by the data. Using either the model selection or exclusion, the alternative space is reduced conditional on the observed data, and hence the power to detect a true association can be increased. Simulation results are reported and the proposed methods are applied to the genetic markers identified from the genome-wide association studies conducted by the Wellcome Trust Case–Control Consortium. The results demonstrate that the genetic model exclusion approach usually performs better than existing methods under its worst situation across scientifically plausible genetic models we considered.     

最佳管理措施评估方法研究进展

针对流域非点源污染的关键源区,进行最佳管理措施(BMPs)的配置,是非点源污染控制的有效途径.污染削减效率的准确识别对于BMPs在目标流域内的有效实施具有非常重要的意义.通过综合对比和分析实地监测、养分平衡、风险评估以及模型模拟等四类最佳管理措施评估方法的有效性、特点、适用条件及其局限性,得出以下结论:养分平衡法较为简便且易于使用,相较于其他方法,所需时间短且又可以消除评估效果的滞后效应,但对污染物削减的时间效应和传输过程影响考虑较少.风险评估和模型模拟方法可以更好地应对不同时空尺度下削减措施效率的评估,但需要大量实测数据的支持,同时模型模拟中普遍存在的时空不确定性影响很难消除.由于各种评估方法都有一定的适用条件,单一方法难以有效地完成评估目标,需要综合应用各类方法,才能最大程度地发挥这些方法的潜在功能和有效性,进而实现BMPs措施使用的成本-效益目标.     

Freedom of Choice About Incidental Findings Can Frustrate Participants' True Preferences

Ethicists, regulators and researchers have struggled with the question of whether incidental findings in genomics studies should be disclosed to participants. In the ethical debate, a general consensus is that disclosed information should benefit participants. However, there is no agreement that genetic information will benefit participants, rather it may cause problems such as anxiety. One could get past this disagreement about disclosure of incidental findings by letting participants express their preferences in the consent form. We argue that this freedom of choice is problematic. In transferring the decision to participants, it is assumed that participants will understand what they decide about and that they will express what they truly want. However, psychological findings about people's reaction to probabilities and risk have been shown to involve both cognitive and emotional challenges. People change their attitude to risk depending on what is at stake. Their mood affects judgments and choices, and they over‐ and underestimate probabilities depending on whether they are low or high. Moreover, different framing of the options can steer people to a specific choice. Although it seems attractive to let participants express their preferences to incidental findings in the consent form, it is uncertain if this choice enables people to express what they truly prefer. In order to better understand the participants' preferences, we argue that future empirical work needs to confront the participant with the complexity of the uncertainty and the trade‐offs that are connected with the uncertain predictive value of genetic risk information.     

Modelling effects of diquat under realistic exposure patterns in genetically differentiated populations of the gastropod Lymnaea stagnalis

Pesticide use leads to complex exposure and response patterns in non-target aquatic species, so that the analysis of data from standard toxicity tests may result in unrealistic risk forecasts. Developing models that are able to capture such complexity from toxicity test data is thus a crucial issue for pesticide risk assessment. In this study, freshwater snails from two genetically differentiated populations of Lymnaea stagnalis were exposed to repeated acute applications of environmentally realistic concentrations of the herbicide diquat, from the embryo to the adult stage. Hatching rate, embryonic development duration, juvenile mortality, feeding rate and age at first spawning were investigated during both exposure and recovery periods. Effects of diquat on mortality were analysed using a threshold hazard model accounting for time-varying herbicide concentrations. All endpoints were significantly impaired at diquat environmental concentrations in both populations. Snail evolutionary history had no significant impact on their sensitivity and responsiveness to diquat, whereas food acted as a modulating factor of toxicant-induced mortality. The time course of effects was adequately described by the model, which thus appears suitable to analyse long-term effects of complex exposure patterns based upon full life cycle experiment data. Obtained model outputs (e.g. no-effect concentrations) could be directly used for chemical risk assessment.     

Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers

In clinical research and practice, landmark models are commonly used to predict the risk of an adverse future event, using patients' longitudinal biomarker data as predictors. However, these data are often observable only at intermittent visits, making their measurement times irregularly spaced and unsynchronized across different subjects. This poses challenges to conducting dynamic prediction at any post-baseline time. A simple solution is the last-value-carry-forward method, but this may result in bias for the risk model estimation and prediction. Another option is to jointly model the longitudinal and survival processes with a shared random effects model. However, when dealing with multiple biomarkers, this approach often results in high-dimensional integrals without a closed-form solution, and thus the computational burden limits its software development and practical use. In this article, we propose to process the longitudinal data by functional principal component analysis techniques, and then use the processed information as predictors in a class of flexible linear transformation models to predict the distribution of residual time-to-event occurrence. The measurement schemes for multiple biomarkers are allowed to be different within subject and across subjects. Dynamic prediction can be performed in a real-time fashion. The advantages of our proposed method are demonstrated by simulation studies. We apply our approach to the African American Study of Kidney Disease and Hypertension, predicting patients' risk of kidney failure or death by using four important longitudinal biomarkers for renal functions.     

Likelihood, parsimony, and heterogeneous evolution

Evolutionary rates vary among sites and across the phylogenetic tree (heterotachy). A recent analysis suggested that parsimony can be better than standard likelihood at recovering the true tree given heterotachy. The authors recommended that results from parsimony, which they consider to be nonparametric, be reported alongside likelihood results. They also proposed a mixture model, which was inconsistent but better than either parsimony or standard likelihood under heterotachy. We show that their main conclusion is limited to a special case for the type of model they study. Their mixture model was inconsistent because it was incorrectly implemented. A useful nonparametric model should perform well over a wide range of possible evolutionary models, but parsimony does not have this property. Likelihood-based methods are therefore the best way to deal with heterotachy.     

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate‐mediated range shifts

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad‐scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment‐only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment‐only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions.     

Hierarchical Modeling for Estimating Relative Risks of Rare Genetic Variants: Properties of the Pseudo‐Likelihood Method

Summary Many major genes have been identified that strongly influence the risk of cancer. However, there are typically many different mutations that can occur in the gene, each of which may or may not confer increased risk. It is critical to identify which specific mutations are harmful, and which ones are harmless, so that individuals who learn from genetic testing that they have a mutation can be appropriately counseled. This is a challenging task, since new mutations are continually being identified, and there is typically relatively little evidence available about each individual mutation. In an earlier article, we employed hierarchical modeling ( Capanu et al., 2008 , Statistics in Medicine 27 , 1973–1992) using the pseudo‐likelihood and Gibbs sampling methods to estimate the relative risks of individual rare variants using data from a case–control study and showed that one can draw strength from the aggregating power of hierarchical models to distinguish the variants that contribute to cancer risk. However, further research is needed to validate the application of asymptotic methods to such sparse data. In this article, we use simulations to study in detail the properties of the pseudo‐likelihood method for this purpose. We also explore two alternative approaches: pseudo‐likelihood with correction for the variance component estimate as proposed by Lin and Breslow (1996, Journal of the American Statistical Association 91 , 1007–1016) and a hybrid pseudo‐likelihood approach with Bayesian estimation of the variance component. We investigate the validity of these hierarchical modeling techniques by looking at the bias and coverage properties of the estimators as well as at the efficiency of the hierarchical modeling estimates relative to that of the maximum likelihood estimates. The results indicate that the estimates of the relative risks of very sparse variants have small bias, and that the estimated 95% confidence intervals are typically anti‐conservative, though the actual coverage rates are generally above 90%. The widths of the confidence intervals narrow as the residual variance in the second‐stage model is reduced. The results also show that the hierarchical modeling estimates have shorter confidence intervals relative to estimates obtained from conventional logistic regression, and that these relative improvements increase as the variants become more rare.     

Mass regulation in response to predation risk can indicate population declines

In theory, survival rates and consequent population status might be predictable from instantaneous behavioural measures of how animals prioritize foraging vs. avoiding predation. We show, for the 30 most common small bird species ringed in the UK, that one quarter respond to higher predation risk as if it is mass-dependent and lose mass. Half respond to predation risk as if it only interrupts their foraging and gain mass thus avoiding consequent increased starvation risk from reduced foraging time. These mass responses to higher predation risk are correlated with population and conservation status both within and between species (and independently of foraging habitat, foraging guild, sociality index and size) over the last 30 years in Britain, with mass loss being associated with declining populations and mass gain with increasing populations. If individuals show an interrupted foraging response to higher predation risk, they are likely to be experiencing a high quality foraging environment that should lead to higher survival. Whereas individuals that show a mass-dependent foraging response are likely to be in lower quality foraging environments, leading to relatively lower survival.     

The Concept of Data Utility in Health Risk Assessment: A Multi-Disciplinary Perspective

Human and ecological health risk assessments and the decisions that stem from them require the acquisition and analysis of data. In agencies that are responsible for health risk decision-making, data (and/or opinions/judgments) are obtained from sources such as scientific literature, analytical and process measurements, expert elicitation, inspection findings, and public and private research institutions. Although the particulars of conducting health risk assessments of given disciplines may be dramatically different, a common concern is the subjective nature of judging data utility. Often risk assessors are limited to available data that may not be completely appropriate to address the question being asked. Data utility refers to the ability of available data to support a risk-based decision for a particular risk assessment. This article familiarizes the audience with the concept of data utility and is intended to raise the awareness of data collectors (e.g., researchers), risk assessors, and risk managers to data utility issues in health risk assessments so data collection and use will be improved. In order to emphasize the cross-cutting nature of data utility, the discussion has not been organized into a classical partitioning of risk assessment concerns as being either human health- or ecological health-oriented, as per the U.S. Environmental Protection Agency's Superfund Program.     

Semiparametric estimation for joint modeling of colorectal cancer risk and functional biomarkers measured with errors

This research is motivated by a pilot colorectal adenoma study, where the outcome of interest is the presence of colorectal adenoma representing risk for colorectal cancer, and the predictors of interest are protein biomarkers that are repeatedly measured with errors along the length of a microscopic structure in the human colon, the colon crypt. Biomarkers of this type are referred to as functional biomarkers. The investigators are interested in identifying features of functional biomarkers that are associated with risk for colorectal cancer. In this paper, we investigate a joint modeling approach, where the binary clinical outcome is modeled using a logistic regression model with the unobserved true functional biomarkers as the predictors. Most existing methods are developed either for linear models or for functional biomarkers measured without errors and cannot be directly applied to our data. The applicable methods include a two-step method and a maximum likelihood method, which have some limitations. We propose a robust semiparametric method to overcome the limitations of the existing methods. We study the properties of the proposed method, and show in simulations that it compares favorably with other methods and also offers significant savings in CPU time. We analyze the pilot colorectal adenoma data and show that expression levels of AFC, a tumor suppressor gene, in the transitional area from the proliferation zone to the differentiation zone of colon crypts are likely to be associated with risk for colorectal cancer. Given the relatively small sample size in the pilot study, our results need to be validated in the future full-scale studies.