Flexible modelling of neuron firing rates across different experimental conditions: an application to neural activity in the prefrontal cortex during a discrimination task

Summary.  In many electrophysiological experiments the main objectives include estimation of the firing rate of a single neuron, as well as a comparison of its temporal evolution across different experimental conditions. To accomplish these two goals, we propose a flexible approach based on the logistic generalized additive model including condition-by-time interactions. If an interaction of this type is detected in the model, we then establish that the use of the temporal odds ratio curves is very useful in discriminating between the conditions under which the firing probability is higher. Bootstrap techniques are used for testing for interactions and constructing pointwise confidence bands for the true odds ratio curves. Finally, we apply the new methodology to assessing relationships between neural response and decision-making in movement-selective neurons in the prefrontal cortex of behaving monkeys.     

Estimating herd-specific force of infection by using random-effects models for clustered binary data and monotone fractional polynomials

Summary.  In veterinary epidemiology, we are often confronted with hierarchical or clustered data. Typically animals are grouped within herds, and consequently we cannot ignore the possibility of animals within herds being more alike than between herds. Based on a serological survey of bovine herpes virus type 1 in cattle, we describe a method for the estimation of herd-specific rates at which susceptible animals acquire the infection at different ages. In contrast with the population-averaged force of infection, this method allows us to model the herd-specific force of infection, allowing investigation of the variability between herds. A random-effects approach is used to account for the correlation in the data, allowing us to study both population-averaged and herd-specific force of infection. In contrast, generalized estimating equations can be used when interest is only in the population-averaged force of infection. Further, a flexible predictor model is needed to describe the dependence of covariates appropriately. Fractional polynomials as proposed by Royston and Altman offer such flexibility. However, the flexibility of this model should be restricted, since only positive forces of infection have a meaningful interpretation.     

Immigrant Wage Disadvantage in Sweden and the United Kingdom: Wage Structure and Barriers to Opportunity1

This article examines immigrant/native-born wage inequalities among workers in two Western European countries: Sweden, social democratic and with comparatively low wage dispersion, and the United Kingdom, economically liberal and with comparatively high wage dispersion. The analysis includes immigrants from 26 countries of origin. Findings demonstrate that inequalities in terms of real wages are smaller in more egalitarian Sweden. However, in terms of relative positions within the labor market, inequalities are if anything smaller in the UK. These findings highlight the role of wage dispersion in magnifying immigrant disadvantage, but also the limits of wage compression for ameliorating barriers to immigrant opportunity.     

Model Averaging in Microbial Risk Assessment Using Fractional Polynomials

The alleviation of food-borne diseases caused by microbial pathogen remains a great concern in order to ensure the well-being of the general public. The relation between the ingested dose of organisms and the associated infection risk can be studied using dose-response models. Traditionally, a model selected according to a goodness-of-fit criterion has been used for making inferences. In this article, we propose a modified set of fractional polynomials as competitive dose-response models in risk assessment. The article not only shows instances where it is not obvious to single out one best model but also illustrates that model averaging can best circumvent this dilemma. The set of candidate models is chosen based on biological plausibility and rationale and the risk at a dose common to all these models estimated using the selected models and by averaging over all models using Akaike's weights. In addition to including parameter estimation inaccuracy, like in the case of a single selected model, model averaging accounts for the uncertainty arising from other competitive models. This leads to a better and more honest estimation of standard errors and construction of confidence intervals for risk estimates. The approach is illustrated for risk estimation at low dose levels based on Salmonella typhi and Campylobacter jejuni data sets in humans. Simulation studies indicate that model averaging has reduced bias, better precision, and also attains coverage probabilities that are closer to the 95% nominal level compared to best-fitting models according to Akaike information criterion.     

Joint modeling of hierarchically clustered and overdispersed non‐gaussian continuous outcomes for comet assay data

Multivariate longitudinal or clustered data are commonly encountered in clinical trials and toxicological studies. Typically, there is no single standard endpoint to assess the toxicity or efficacy of the compound of interest, but co‐primary endpoints are available to assess the toxic effects or the working of the compound. Modeling the responses jointly is thus appealing to draw overall inferences using all responses and to capture the association among the responses. Non‐Gaussian outcomes are often modeled univariately using exponential family models. To accommodate both the overdispersion and hierarchical structure in the data, Molenberghs et al. A family of generalized linear models for repeated measures with normal and conjugate random effects. Statistical Science 2010; 25:325–347 proposed using two separate sets of random effects. This papers considers a model for multivariate data with hierarchically clustered and overdispersed non‐Gaussian data. Gamma random effect for the over‐dispersion and normal random effects for the clustering in the data are being used. The two outcomes are jointly analyzed by assuming that the normal random effects for both endpoints are correlated. The association structure between the response is analytically derived. The fit of the joint model to data from a so‐called comet assay are compared with the univariate analysis of the two outcomes. Copyright © 2012 John Wiley & Sons, Ltd.