Marginal correlation from logit- and probit-data-normal models for hierarchical binary data

© 2014 Taylor & Francis Group, LLC. In hierarchical data settings, be it of a longitudinal, spatial, multi-level, clustered, or otherwise repeated nature, often the association between repeated measurements attracts at least part of the scientific interest. Quantifying the association frequently takes the form of a correlation function, including but not limited to intraclass correlation. Vangeneugden et al. (2010) derived approximate correlation functions for longitudinal sequences of general data type, Gaussian and non-Gaussian, based on generalized linear mixed-effects models. Here, we consider the extended model family proposed by Molenberghs et al. (2010). This family flexibly accommodates data hierarchies, intra-sequence correlation, and overdispersion. The family allows for closed-form means, variance functions, and correlation function, for a variety of outcome types and link functions. Unfortunately, for binary data with logit link, closed forms cannot be obtained. This is in contrast with the probit link, for which such closed forms can be derived. It is therefore that we concentrate on the probit case. It is of interest, not only in its own right, but also as an instrument to approximate the logit case, thanks to the well-known probit-logit conversion. Next to the general situation, some important special cases such as exchangeable clustered outcomes receive attention because they produce insightful expressions. The closed-form expressions are contrasted with the generic approximate expressions of Vangeneugden et al. (2010) and with approximations derived for the so-called logistic-beta-normal combined model. A simulation study explores performance of the method proposed. Data from a schizophrenia trial are analyzed and correlation functions derived.peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=lsta20status: publishe

A family of generalized linear models for repeated measures with normal and conjugate random effects

Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Two of the main reasons for extending this family are (1) the occurrence of overdispersion, meaning that the variability in the data is not adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of hierarchical structure in the data, stemming from clustering in the data which, in turn, may result from repeatedly measuring the outcome, for various members of the same family, etc. The first issue is dealt with through a variety of overdispersion models, such as, for example, the beta-binomial model for grouped binary data and the negative-binomial model for counts. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these phenomena may occur simultaneously, models combining them are uncommon. This paper proposes a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. We place particular emphasis on so-called conjugate random effects at the level of the mean for the first aspect and normal random effects embedded within the linear predictor for the second aspect, even though our family is more general. The binary, count and time-to-event cases are given particular emphasis. Apart from model formulation, we present an overview of estimation methods, and then settle for maximum likelihood estimation with analytic-numerical integration. Implications for the derivation of marginal correlations functions are discussed. The methodology is applied to data from a study in epileptic seizures, a clinical trial in toenail infection named onychomycosis and survival data in children with asthma. © Institute of Mathematical Statistics, 2010.Published in at http://dx.doi.org/10.1214/10-STS328 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)status: publishe

Quantifying intraclass correlations for nonnegative traits

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A Weibull-count approach for handling under- and overdispersed longitudinal/clustered data structures

© 2018 SAGE Publications. A Weibull-model-based approach is examined to handle under- and overdispersed discrete data in a hierarchical framework. This methodology was first introduced by Nakagawa and Osaki (1975, IEEE Transactions on Reliability, 24, 300–301), and later examined for under- and overdispersion by Klakattawi et al. (2018, Entropy, 20, 142) in the univariate case. Extensions to hierarchical approaches with under- and overdispersion were left unnoted, even though they can be obtained in a simple manner. This is of particular interest when analysing clustered/longitudinal data structures, where the underlying correlation structure is often more complex compared to cross-sectional studies. In this article, a random-effects extension of the Weibull-count model is proposed and applied to two motivating case studies, originating from the clinical and sociological research fields. A goodness-of-fit evaluation of the model is provided through a comparison of some well-known count models, that is, the negative binomial, Conway–Maxwell–Poisson and double Poisson models. Empirical results show that the proposed extension flexibly fits the data, more specifically, for heavy-tailed, zero-inflated, overdispersed and correlated count data. Discrete left-skewed time-to-event data structures are also flexibly modelled using the approach, with the ability to derive direct interpretations on the median scale, provided the complementary log–log link is used. Finally, a large simulated set of data is created to examine other characteristics such as computational ease and orthogonality properties of the model, with the conclusion that the approach behaves best for highly overdispersed cases.status: publishe

Intraguild predation influences oviposition behavior of blow flies (Diptera: Calliphoridae)

The objective of the present study was to determine whether blow flies (Diptera: Calliphoridae) are able to identify larvae of an intraguild predator species in the substrate and avoid laying eggs there. Blow flies oviposited in traps with different treatments: substrate only and substrate with larvae of Chrysomya albiceps (Wiedemann, 1819), Chrysomya megacephala (Fabricius, 1794), or Chrysomya putoria (Wiedemann, 1830). Ch. megacephala, Ch. putoria, and Lucilia eximia (Wiedemann, 1819) avoided laying eggs in the trap containing Ch. albiceps larvae. Cochliomyia macellaria (Fabricius, 1775) did not oviposit differently in each substrate but had overall low abundance. The prevalence of species on corpses may be influenced by the ability of the species to detect the presence of other species, mainly predators. In this sense, intraguild predation may result in misinterpretations of a crime scene and should be considered when assessing the minimum postmortem interval

La organización escolar en el sistema educativo actual

Resumen basado en el de la publicaciónSe presenta la evolución de la organización del sistema educativo español en las últimas décadas. Con la nueva normativa educativa se introducen importantes novedades en la organización, funcionamiento y gobierno de los centros educativos, profundizando en los conceptos de participación de la comunidad educativa y de autonomía pedagógica, organizativa y de gestión. Se intenta clarificar cual es el tratamiento que la legislación educativa da a los diferentes aspectos de la organización escolar, realizando una comparación con la normativa anterior y, en algunos casos, un análisis crítico. Se dedica un capítulo al proceso de escolarización en Andalucía.AndalucíaBiblioteca de Educación del Ministerio de Educación, Cultura y Deporte; Calle San Agustín, 5 - 3 planta; 28014 Madrid; Tel. +34917748000; [email protected]