Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements

© 2015 Elsevier Inc. Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary response. They provide a useful way to assess association between these two kinds of data, which in clinical studies are often collected jointly on a series of individuals and may help understanding, for instance, the mechanisms of recovery of a certain disease or the efficacy of a given therapy. When a nonlinear mixed-effects model is used to fit the longitudinal trajectories, the existing estimation strategies based on likelihood approximations have been shown to exhibit some computational efficiency problems (De la Cruz et al., 2011). In this article we consider a Bayesian estimation procedure for the joint model with a nonlinear mixed-effects model for the longitudinal data and a generalized linear model for the primary response. The proposed prior structure allows for the implementation of an MCMC sampler. Moreover, we consider that the errors in the longitudinal model may be correlated. We apply our method to the analysis of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. We also conduct a simulation study to assess the importance of modelling correlated errors and quantify the consequences of model misspecification

A new viscoelastic benchmark flow: Stationary bifurcation in a cross-slot

AbstractIn this work we propose the cross-slot geometry as a candidate for a numerical benchmark flow problem for viscoelastic fluids. Extensive data of quantified accuracy is provided, obtained via Richardson extrapolation to the limit of infinite refinement using results for three different mesh resolutions, for the upper-convected Maxwell, Oldroyd-B and the linear form of the simplified Phan-Thien–Tanner constitutive models. Furthermore, we consider two types of flow geometry having either sharp or rounded corners, the latter with a radius of curvature equal to 5% of the channel’s width. We show that for all models the inertialess steady symmetric flow may undergo a bifurcation to a steady asymmetric configuration, followed by a second transition to time-dependent flow, which is in qualitative agreement with previous experimental observations for low Reynolds number flows. The critical Deborah number for both transitions is quantified and a set of standard parameters is proposed for benchmarking purposes

Influence of channel aspect ratio on the onset of purely-elastic flow instabilities in three-dimensional planar cross-slots

In this work, we perform creeping-flow simulations of upper-convected Maxwell and simplified Phan-Thien-Tanner fluids to study the purely-elastic steady bifurcation and transition to time-dependent flow in three-dimensional planar cross-slots. By analysing the flow in geometries with aspect ratios ranging from the near Hele-Shaw flow like limit, up to the very deep, two-dimensional limit, we are able to characterize the mechanism of the cross-slot bifurcation with significant detail. We conclude that the bifurcation mechanism is similar to a buckling instability, by which fluid is redirected via paths of least resistance, resulting in the emergence of peripheral stagnation points, above and below the central stagnation point. The intake of matter at the centre via the inlet axis is thus reduced, being compensated by fluid flowing through low resistance corridors along the central vertical axis, above and below the central point. Furthermore, we propose and locally compute a modified Pakdel-McKinley criterion, thereby producing a scalar stability field and suggesting emergent peripheral stagnation points also indirectly contribute to the onset of time-dependent flow. (c) 2015 The Authors. Published by Elsevier B.V

Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances

[EN] Biomedical data may be composed of individuals generated from distinct, meaningful sources. Due to possible contextual biases in the processes that generate data, there may exist an undesirable and unexpected variability among the probability distribution functions (PDFs) of the source subsamples, which, when uncontrolled, may lead to inaccurate or unreproducible research results. Classical statistical methods may have difficulties to undercover such variabilities when dealing with multi-modal, multi-type, multi-variate data. This work proposes two metrics for the analysis of stability among multiple data sources, robust to the aforementioned conditions, and defined in the context of data quality assessment. Specifically, a global probabilistic deviation (GPD) and a source probabilistic outlyingness (SPO) metrics are proposed. The first provides a bounded degree of the global multi-source variability, designed as an estimator equivalent to the notion of normalized standard deviation of PDFs. The second provides a bounded degree of the dissimilarity of each source to a latent central distribution. The metrics are based on the projection of a simplex geometrical structure constructed from the Jensen-Shannon distances among the sources PDFs. The metrics have been evaluated and demonstrated their correct behaviour on a simulated benchmark and with real multi-source biomedical data using the UCI Heart Disease dataset. The biomedical data quality assessment based on the proposed stability metrics may improve the efficiency and effectiveness of biomedical data exploitation and research.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by own IBIME funds under the UPV project Servicio de evaluacion y rating de la calidad de repositorios de datos biomedicos [UPV-2014-872] and the EU FP7 Project Help4Mood - A Computational Distributed System to Support the Treatment of Patients with Major Depression [ICT-248765].Sáez Silvestre, C.; Robles Viejo, M.; García Gómez, JM. (2014). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research. 1-25. https://doi.org/10.1177/0962280214545122S12

The influence of semantic and phonological factors on syntactic decisions: An event-related brain potential study

During language production and comprehension, information about a word's syntactic properties is sometimes needed. While the decision about the grammatical gender of a word requires access to syntactic knowledge, it has also been hypothesized that semantic (i.e., biological gender) or phonological information (i.e., sound regularities) may influence this decision. Event-related potentials (ERPs) were measured while native speakers of German processed written words that were or were not semantically and/or phonologically marked for gender. Behavioral and ERP results showed that participants were faster in making a gender decision when words were semantically and/or phonologically gender marked than when this was not the case, although the phonological effects were less clear. In conclusion, our data provide evidence that even though participants performed a grammatical gender decision, this task can be influenced by semantic and phonological factors