Thomas Neyens

Toxicity profiles and solvent–toxicant interference in the planarian Schmidtea mediterranea after dimethylsulfoxide (DMSO) exposure

To investigate hydrophobic test compounds in toxicological studies, solvents like dimethylsulfoxide (DMSO) are inevitable. However, using these solvents, the interpretation of test compound‐induced responses can be biased. DMSO concentration guidelines are available, but are mostly based on acute exposures involving one specific toxicity endpoint. Hence, to avoid solvent–toxicant interference, we use multiple chronic test endpoints for additional interpretation of DMSO concentrations and propose a statistical model to assess possible synergistic, antagonistic or additive effects of test compounds and their solvents. In this study, the effects of both short‐ (1 day) and long‐term (2 weeks) exposures to low DMSO concentrations (up to 1000 µl l−1) were studied in the planarian Schmidtea mediterranea. We measured different biological levels in both fully developed and developing animals. In a long‐term exposure set‐up, a concentration of 500 µl l−1 DMSO interfered with processes on different biological levels, e.g. behaviour, stem cell proliferation and gene expression profiles. After short exposure times, 500 µl l−1 DMSO only affected motility, whereas the most significant changes on different parameters were observed at a concentration of 1000 µl l−1 DMSO. As small sensitivity differences exist between biological levels and developmental stages, we advise the use of this solvent in concentrations below 500 µl l−1 in this organism. In the second part of our study, we propose a statistical approach to account for solvent–toxicant interactions and discuss full‐scale solvent toxicity studies. In conclusion, we reassessed DMSO concentration limits for different experimental endpoints in the planarian S. mediterranea. Copyright

A zero-inflated overdispersed hierarchical Poisson model:

Count data are most commonly modeled using the Poisson model, or by one of its many extensions. Such extensions are needed for a variety of reasons: (1) a hierarchical structure in the data, e.g., due to clustering, the collection of repeated measurements of the outcome, etc.; (2) the occurrence of overdispersion (or underdispersion), meaning that the variability encountered in the data is not equal to the mean, as prescribed by the Poisson distribution; and (3) the occurrence of extra zeros beyond what a Poisson model allows. The first issue is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. Overdispersion is often dealt with through a model developed for this purpose, such as, for example, the negative-binomial model for count data. This can be conceived through a random Poisson parameter. Excess zeros are regularly accounted for using so-called zero-inflated models, which combine either a Poisson or negative-binomial model with an atom at zero. The novelty of this article is that it combines all these features. The work builds upon the modelling framework defined by Molenberghs et al. (2010) in which clustering and overdispersion are accommodated for through two separate sets of random effects in a generalized linear model.

A generalized Poisson-gamma model for spatially overdispersed data.

Modern disease mapping commonly uses hierarchical Bayesian methods to model overdispersion and spatial correlation. Classical random-effects based solutions include the Poisson-gamma model, which uses the conjugacy between the Poisson and gamma distributions, but which does not model spatial correlation, on the one hand, and the more advanced CAR model, which also introduces a spatial autocorrelation term but without a closed-form posterior distribution on the other. In this paper, a combined model is proposed: an alternative convolution model accounting for both overdispersion and spatial correlation in the data by combining the Poisson-gamma model with a spatially-structured normal CAR random effect. The Limburg Cancer Registry data on kidney and prostate cancer in Limburg were used to compare the conventional and new models. A simulation study confirmed results and interpretations coming from the real datasets. Relative risk maps showed that the combined model provides an intermediate between the non-patterned negative binomial and the sometimes oversmoothed CAR convolution model.

Modeling overdispersed longitudinal binary data using a combined beta and normal random-effects model

BackgroundIn medical and biomedical areas, binary and binomial outcomes are very common. Such data are often collected longitudinally from a given subject repeatedly overtime, which result in clustering of the observations within subjects, leading to correlation, on the one hand. The repeated binary outcomes from a given subject, on the other hand, constitute a binomial outcome, where the prescribed mean-variance relationship is often violated, leading to the so-called overdispersion.MethodsTwo longitudinal binary data sets, collected in south western Ethiopia: the Jimma infant growth study, where the child’s early growth is studied, and the Jimma longitudinal family survey of youth where the adolescent’s school attendance is studied over time, are considered. A new model which combines both overdispersion, and correlation simultaneously, also known as the combined model is applied. In addition, the commonly used methods for binary and binomial data, such as the simple logistic, which accounts neither for the overdispersion nor the correlation, the beta-binomial model, and the logistic-normal model, which accommodate only for the overdispersion, and correlation, respectively, are also considered for comparison purpose. As an alternative estimation technique, a Bayesian implementation of the combined model is also presented.ResultsThe combined model results in model improvement in fit, and hence the preferred one, based on likelihood comparison, and DIC criterion. Further, the two estimation approaches result in fairly similar parameter estimates and inferences in both of our case studies. Early initiation of breastfeeding has a protective effect against the risk of overweight in late infancy (p = 0.001), while proportion of overweight seems to be invariant among males and females overtime (p = 0.66). Gender is significantly associated with school attendance, where girls have a lower rate of attendance (p = 0.001) as compared to boys.ConclusionWe applied a flexible modeling framework to analyze binary and binomial longitudinal data. Instead of accounting for overdispersion, and correlation separately, both can be accommodated simultaneously, by allowing two separate sets of the beta, and the normal random effects at once.

A joint model for hierarchical continuous and zero-inflated overdispersed count data

Many applications in public health, medical and biomedical or other studies demand modelling of two or more longitudinal outcomes jointly to get better insight into their joint evolution. In this regard, a joint model for a longitudinal continuous and a count sequence, the latter possibly overdispersed and zero-inflated (ZI), will be specified that assembles aspects coming from each one of them into one single model. Further, a subject-specific random effect is included to account for the correlation in the continuous outcome. For the count outcome, clustering and overdispersion are accommodated through two distinct sets of random effects in a generalized linear model as proposed by Molenberghs et al. [A family of generalized linear models for repeated measures with normal and conjugate random effects. Stat Sci. 2010;25:325–347]; one is normally distributed, the other conjugate to the outcome distribution. The association among the two sequences is captured by correlating the normal random effects describing the continuous and count outcome sequences, respectively. An excessive number of zero counts is often accounted for by using a so-called ZI or hurdle model. ZI models combine either a Poisson or negative-binomial model with an atom at zero as a mixture, while the hurdle model separately handles the zero observations and the positive counts. This paper proposes a general joint modelling framework in which all these features can appear together. We illustrate the proposed method with a case study and examine it further with simulations.

Disease mapping of zero-excessive mesothelioma data in Flanders

PURPOSE To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. METHODS The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. RESULTS The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. CONCLUSIONS Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this.

Proximity of breeding and foraging areas affects foraging effort of a crepuscular, insectivorous bird

When complementary resources are required for an optimal life cycle, most animals need to move between different habitats. However, the level of connectivity between resources can vary and, hence, influence individuals’ behaviour. We show that landscape composition and configuration affect the connectivity between breeding (heathlands) and foraging habitats (extensively-grazed grasslands) of the European Nightjar (Caprimulgus europaeus), a crepuscular insectivorous bird. On a daily basis, nightjars connect breeding and foraging sites by rapidly crossing unsuitable habitats in order to exploit a higher prey biomass in foraging sites. However, low availability of foraging habitat near breeding sites and clustered landscapes greatly increase foraging distance. Birds occupying these sub-optimal breeding areas compensate for longer travels by increasing foraging duration, and their physiology shows increased stress levels. All findings suggest that landscape heterogeneity can affect population dynamics of nightjars. Therefore, we recommend an integrated management approach for this EU-protected bird species.

Author Correction: Proximity of breeding and foraging areas affects foraging effort of a crepuscular, insectivorous bird

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Integrated nested Laplace approximation for the analysis of count data via the combined model: A simulation study

ABSTRACT The combined model accounts for different forms of extra-variability and has traditionally been applied in the likelihood framework, or in the Bayesian setting via Markov chain Monte Carlo. In this article, integrated nested Laplace approximation is investigated as an alternative estimation method for the combined model for count data, and compared with the former estimation techniques. Longitudinal, spatial, and multi-hierarchical data scenarios are investigated in three case studies as well as a simulation study. As a conclusion, integrated nested Laplace approximation provides fast and precise estimation, while avoiding convergence problems often seen when using Markov chain Monte Carlo.