Psychotropic drug classification based on sleep–wake behaviour of rats

Summary.  The aim of the paper is to present methodology for the classification of potential psychotropic drugs on the basis of their activity. We first sketch the background of this class of drugs and then zoom in on so-called pharmacoelectroencephalogram studies. These data pose some statistical challenges. For classification purposes, we propose a flexible hierarchical discriminant analysis tool, allowing us to take the specific nature of the drug class into account, as well as the features of the mixed models, in combination with fractional polynomials, fitted to the electroencephalogram data. The method is evaluated against the background of existing methods. The method's performance is studied by using a comprehensive analysis of a large electroencephalogram data set.     

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.     

A kap study on malaria in Zanzibar: implications for prevention and controlA study conducted for unicef Sub-Office Zanzibar

This study was a collaborative undertaking between UNICEF Sub-Office Zanzibar and Zanzibar Ministries of Health and Education between December 1996 and February 1997.The overall objective of the KAP study was to obtain and interpret detailed grassroots information about Zanzibaris understanding of the causation and transmission of malaria so as to select priority interventions to improve home-based management and prevention of childhood malaria. This information now forms the basis for designing various interventions targeting care-takers risk behaviors, household ability to take informed decisions, and both private and public sector provision of adequate advice and support to households.The assumption underlying the study was that communities will welcome change if they see the need for it, and they will develop the determination and capacity to bring about change to the extent that it seems worthwhile. This KAP profile, then, was to be a key component in a programme to induce beneficial change through public, especially school childrens, participation.The key findings of the study were: (a) although public health messages about malaria were technically correct, they did not address the common cultural myths and perceptions about causation and transmission of malaria; (b) there was a clear distinction made by respondents in defining malaria, i.e., fever, and malarias severe complications, convulsions and fits; (c) decisions concerning treatment were usually taken, with inputs from the family (especially grandparents), by the husbands (where they existed); and (d) treatment involved traditional medicine more frequently than the use of a modern health facility and services.     

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.