Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution

Generalized linear mixed models are popular for regressing a discrete response when there is clustering, e.g. in longitudinal studies or in hierarchical data structures. It is standard to assume that the random effects have a normal distribution. Recently, it has been examined whether wrongly assuming a normal distribution for the random effects is important for the estimation of the fixed effects parameters. While it has been shown that misspecifying the distribution of the random effects has a minor effect in the context of linear mixed models, the conclusion for generalized mixed models is less clear. Some studies report a minor impact, while others report that the assumption of normality really matters especially when the variance of the random effect is relatively high. Since it is unclear whether the normality assumption is truly satisfied in practice, it is important that generalized mixed models are available which relax the normality assumption. A replacement of the normal distribution with a mixture of Gaussian distributions specified on a grid whereby only the weights of the mixture components are estimated using a penalized approach ensuring a smooth distribution for the random effects is proposed. The parameters of the model are estimated in a Bayesian context using MCMC techniques. The usefulness of the approach is illustrated on two longitudinal studies using R-functions.     

An overview of group sequential methods in longitudinal clinical trials

During the last decade, several papers have been published on group sequential methods in general and on sequential longitudinal clinical trials in particular. This paper gives an overview of the proposed methods, emphasizing longitudinal clinical trials. Furthermore, it tries to answer some practical questions that may arise during the conduct of interim analyses in longitudinal trials. Simulations have been carried out to obtain insight in these practical considerations.     

The detection of residual serial correlation in linear mixed models

Diggle (1988) described how the empirical semi-variogram of ordinary least squares residuals can be used to suggest an appropriate serial correlation structure in stationary linear mixed models. In this paper, this approach is extended to non-stationary models which include random effects other than intercepts, and will be applied to prostate cancer data, taken from the Baltimore Longitudinal Study of Aging. A simulation study demonstrates the effectiveness of this extended variogram for improving the covariance structure of the linear mixed model used to describe the prostate data.     

An application of maximum likelihood and generalized estimating equations to the analysis of ordinal data from a longitudinal study with cases missing at random

Data are analysed from a longitudinal psychiatric study in which there are no dropouts that do not occur completely at random. A marginal proportional odds model is fitted that relates the response (severity of side effects) to various covariates. Two methods of estimation are used: generalized estimating equations (GEE) and maximum likelihood (ML). Both the complete set of data and the data from only those subjects completing the study are analysed. For the completers-only data, the GEE and ML analyses produce very similar results. These results differ considerably from those obtained from the analyses of the full data set. There are also marked differences between the results obtained from the GEE and ML analysis of the full data set. The occurrence of such differences is consistent with the presence of a non-completely-random dropout process and it can be concluded in this example that both the analyses of the completers only and the GEE analysis of the full data set produce misleading conclusions about the relationships between the response and covariates.     

Short-term reproducibility of pressure pain thresholds in masseter and temporalis muscles of symptom-free subjects

Seventy-eight Brazilian beekeepers who had been stung on average six times per month were studied. Sixty-eight beekeepers (87.1%) showed restricted local clinical reactions; nine individuals (11.5%) had extensive local reactions, and only one (1.2%) suffered anaphylactic shock. The humoral immunologic pattern of these individuals were studied by using immunoenzymatic methods to determine the serum titles of specific IgE and specific IgG4. Three groups of beekeepers presenting different humoral immunologic patterns were identified, in which the predominant pattern was the absence of specific IgE and high levels of specific IgG4 (38.4%). There was a positive correlation between the high levels of specific IgG4 and the number of bee stings. This correlation was not found in either specific or total IgE. The results of the present study suggest: i) the immunologic response to the number of exposures to Africanized honey-bee venom is not related to the number of exposures; and, ii) other nonhumoral and/or nonimmunologic factors may be involved in the reaction to the insect sting, which are responsible for both the clinical symptoms and protection.     

The “Conducting Master”: An Interactive,Real-Time Gesture Monitoring System Based on Spatiotemporal Motion Templates

Research in the field of embodied music cognition has shown the importance of coupled processes of body activity (action) and multimodal representations of these actions (perception) in how music is processed. Technologies in the field of human–computer interaction (HCI) provide excellent means to intervene into, and extend, these coupled action-perception processes. In this article this model is applied to a concrete HCI application, called the “Conducting Master.” The application facilitates multiple users to interact in real time with the system in order to explore and learn how musical meter can be articulated into body movements (i.e., meter-mimicking gestures). Techniques are provided to model and automatically recognize these gestures in order to provide multimodal feedback streams back to the users. These techniques are based on template-based methods that allow approaching meter-mimicking gestures explicitly from a spatiotemporal account. To conclude, some concrete setups are presented in which the functionality of the Conducting Master was evaluated.     

Tree-based versus distance-based key recognition in musical audio

A tree-based method for the recognition of the tonal center or key in a musical audio signal is presented. Time-varying key feature vectors of 264 synthesized sounds are extracted from an auditory-based pitch model and converted into character strings using PCA-analysis and classification trees. The results are compared with distance-based methods. The characteristics of the new tonality analysis tool are illustrated on various examples. The potential of this method as a building stone in a music retrieval system is discussed.     

Biomass pyrolysis experiments in an analytical entrained flow reactor between 1073 K and 1273 K

Experiments are performed in an entrained flow reactor to better understand the kinetic processes involved in biomass pyrolysis under high temperatures (1073-1273 K) and fast heating condition (>500 K s⁻¹). The influence of the particle size (0.4 and 1.1 mm), of the temperature (1073-1273 K), of the presence of steam in the gas atmosphere (0 and 20 vol%) and of the residence time (between 0.7 and 3.5 s for gas) on conversion and selectivity is studied. Under these conditions, the particle size is the most crucial parameter that influences decomposition. For 1.1 mm particles, pyrolysis requires more than 0.5 s and heat transfer processes are limiting. For 0.4 mm particles, pyrolysis seems to be finished before 0.5 s. More than 70 wt% of gas is produced. Forty percent of the initial carbon is found in CO; less than 5% is found in CO₂. The hydrogen content is almost equally distributed among H₂, H₂O and light hydrocarbons (CH₄, C₂H₂, C₂H₄). Under these conditions, the evolution of the produced gas mixture is not very significant during the first few seconds, even if there seems to be some reactions between H₂, the C₂ and tars.     

Drainage of Sloping Lands with Variable Recharge: Analytical Formulas and Model Development

Semianalytical transient equations for shallow subsurface transverse drainage systems installed in sloping lands are developed. They provide a general relationship between drain flow rates, water table elevations, and recharge rates. This relationship demonstrates that, depending on the recharge intensity, several drain flow rates can be observed at a given water table elevation. The recharge contribution is shown to depend on a water table shape factor and to decrease when the water table is low or the slope is steep. For very steep slopes, the recharge intensity no longer influences the drain flow rate. These equations can be used to confirm previous results obtained in steady-state conditions and to determine precisely under which conditions slope needs to be considered in drainage design. They have been incorporated into the field drainage model SIDRA, which simulates hourly values of water table elevations and drain flow rates. The model predictions are compared with the predictions of a steady-state equation and a numerical model, which solves the Boussinesq equation (SLOP model).